Tracing Data (Thesis)

1/6 2016

TRACING DATA EXPLORING VALUE THROUGH AN ORGANIZATIONAL CONTEXT

Mathias Madsen mmad@itu.dk Digital Innovation and Management

Mathias Madsen IT University of Copenhagen 01/06 2016 Supervisors: Marisa Cohn and Ingmar Lippert

ABSTRACT

An increased focus among businesses in gathering and processing data, have the potential to transform the organizational practices of whole industries. Individuals and organizations are increasingly relying on algorithmic predictions in decision making practices, which raises concerns of the processes that leads to the development of these algorithms and how knowledge is produced in organizations. This thesis explores the emergence of a tool for identifying profitable customers (leads), and how actors from different worlds conceptualized and represented customer data in order to generate value. With a general interest in the organizational processes of data valuation, this paper is developed through the lenses of Grounded Theory and ethnography. Drawing on a qualitative research design, this paper provides a chronological account of events that related to how data management practices were framed and how translative effect aided in the decision making processes. An analysis of the use of framing and translations to ascribe and influence value is appointed. Finally, this paper proposes two emerging hypotheses. First, it suggests that the procedural force of framings can help determine the audience influence on the structure and the level of capability by the enactor of the framing. Second, it introduces a framework for project management that addresses conceptual involvement in the development of a shared project goal. This paper offers a view into the social sciences of marketing by exploring valuation from an organizational perspective. This is a reflection of current practices from one organizational context, but it proposes frameworks and considerations that might be applicable to other fields.

Table of ConTenTs 1. INTRODUCTION

7

2. ENTER ACADEMIA

8

2.1. PROBLEMATIZATION

8

2.2. RESEARCH QUESTIONS

9

3. RESEARCH FOUNDATION

11

3.1. A SITUATED STUDY

11

3.2. VOCABULARY

12

4. RESEARCH DESIGN

13

4.1. PRELIMINARY CONSIDERATIONS

13

4.2. METHODOLOGY

13

4.3. METHODS

16

4.4. ANALYTICAL PHASES

20

5. ANALYSIS I: FIELD STUDY

22

5.1. IDENTIFYING THEORETICAL CONCERNS

22

5.2. ENTER THE FIELD

23

5.3. A PRELIMINARY ANALYSIS

24

5.4. DEFINING REQUIREMENTS

31

5.5. LEAD SCORING

35

5.6. DATA MANAGEMENT

39

5.7. ACCOUNTING FOR THE CHOSEN SOLUTION

42

5.8. SUB CONCLUSION

46

6. ANALYSIS II: EXPLORING FINDINGS 6.1. DATA VALUATION

50 50

6.2. FRAMINGS

52

6.3. TRANSLATIONS

56

6.4. VALUATION AS DETERMINED BY FRAMINGS AND TRANSLATIONS

60

6.5. SUB CONCLUSION

62

7. DISCUSSION

66

7.1. CONTRIBUTION

66

7.2. EMPIRICAL EVIDENCE

67

8. CONCLUSIONS

70

9. REFLECTIONS

71

10. LITERATURE

73

APPENDICES

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APPENDIX A - PROJECT DESCRIPTION FOR PARTICIPANTS

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APPENDIX B - RESEARCH LOG

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APPENDIX C - 2016-02-12 LEAD GENERATION

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APPENDIX D - ARCHITECTURE DRAWING BY THE WEB BUREAU

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APPENDIX E - DATA ENTITIES IDENTIFIED AT THE WORKSHOP

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APPENDIX F - ADDITIONAL CONCEPTUALIZATIONS

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APPENDIX G - PRESENTATION SLIDES

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APPENDIX H - 2016-02-18 LEAD SCORING

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APPENDIX I - 2016-02-25 DATA MANAGEMENT

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APPENDIX J - 2016-03-09 DATA STORING

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APPENDIX K - ARCHITECTURE DRAWING BY THE DATA BUREAU

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APPENDIX L - 2016-03-16 PRESENTATION REVIEW

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APPENDIX M - 2016-03-18 PROPOSAL

129

1. INTRODUCTION

1. INTRODUCTION This is a story about value; how one company applied contemporary insights about customers to shape and define new business opportunities. This is an exploration of how value is generated from these insights and used to estimate the value of new potential customers (leads, i.e. Havaldar, 2014) and how the latter are scored according to their perceived transactional value. Traces of how actors conceptualize and represent data are followed through this study to understand how relevance and value are accounted for. As organizational procedures become visible, it is explored how data valuation and usage are shaped by particular framings and how translative effects are used to account for decision making processes.

This is a small sample of events that occur within one organization, but it reflects recent tendencies of how businesses’ practices are increasingly shaped by concurrent customer data. While businesses previously learned most about their customers through marketing research techniques such as questionnaires and focus groups (Kierlanczyk, 2016; Vasquez, 2011), businesses are deploying a focused approach in extracting customer intentions from digital traces of customer interactions (Kierlanczyk, 2016). Customers have been reduced to data entities that allow businesses to analyze and represent customer behavior through new lenses. As customer patterns are emerging, companies are able to develop new business strategies. This is a promise of a discourse: how new ways of gathering and processing data have the potential to transform the organizational practices of whole industries. Companies are increasingly basing their organizational practices on insights generated through these methods, which introduces concerns about the processes and framings that have influenced how customer data have been valued and transformed into new knowledge. This is not a story about Big Data, but rather a dissection of the processes that exist when organizations seek to extract knowledge from past insights.

Data Analysts and business leaders have embraced digital methods for harnessing these opportunities, but scholars have yet to cover how these practices are affected by the processes and conceptualizations of the actors involved. How are these processes framed? Which factors are used to estimate the value of data? How are insights generated on these accounts perceived as valuable? And how do actors in general engage with data management practices? These questions are part of the overarching research question of this thesis - how are actors conceptualizing and representing data?

The story of value begins with a conceptualization of the matters I seek to study and an introduction to the questions that has guided my research. I follow this with a brief insight to my academic and personal background to allow the reader to understand my analytic

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2. ENTER ACADEMIA lenses. As a foundation for the empirical evidence gathered, I describe my methodological approach and elaborate on the processes used to develop my claims. Following this, I tell the empirical tale of value from a chronological perspective, while I address how the events studied relates to framings and translations. I relate these analytical moments to other scholars in order to develop claims about how value is influenced by framings and translations. As I conclude this analytic endeavor, I discuss how my story can contribute to the academic field and how future research could extend on the concerns I raise.

2. ENTER ACADEMIA This section introduces the reader to the formal setting of the study and describes the challenges that are investigated and questioned through this paper. I conclude this chapter by defining the research questions that has driven my curiosity through this dissertation.

2.1. PROBLEMATIZATION For the last two decades infrastructures of information technologies for managing customer relations, supply chains and financial assets have been increasingly embedded into business practices (Aral, Brynjolfsson, & Wu, 2006). This has allowed companies to collect large amounts of data on business performance, partners and customers. In recent years, managers have had an increased focus on data-driven decisions, which have encouraged them to explore their capabilities through data analyses (McElheran & Brynjolfsson, 2016). As businesses seek to embrace the possibilities that these analyses can provide, I seek to explore the consequences of how actors are conceptualizing and shaping data management practices.

For five months I investigated and observed one account of how the complex matters of data valuation and management were approached and how actors interacted with customer data through different phases of conceptualization, selection, valuation and representation. This was possible through my employment at a Consultancy Bureau (CB), who were assisting a Global Business (GB) in developing a website and facilitating a discussion about customer data usage in the organization. By tracing how actors were framing and conceptualizing customer data management at meetings related to a Lead Generation project, I have been able to gather empirical evidence of some of the effects taking place when data is managed and valued in an organizational context. While I seek to limit my analytical focus to the social sciences of data management practices, I use the

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2. ENTER ACADEMIA empirical evidence to develop theories within this field.

2.2. RESEARCH QUESTIONS This paper explores the processes of how actors from different conceptual worlds organize, sort and deconstruct data, how data are valuated and how these insights are translated by actors and represented towards decision makers within the organization. In particular, I seek to investigate the following: •

How are organizational members conceptualizing and representing data?

•

How and why are data entities selected, valued and applied meaning and how does this relate to the decision making processes?

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RESEARCH FOUNDATION & DESIGN THE BASIS OF THE RESEARCH STUDY IS DESCRIBED WITH REFERENCES TO THE METHODOLOGICAL CONSIDERATIONS AND THE METHODS USED TO ACQUIRE AND INSCRIBE THE EMPIRICAL EVIDENCE.

3. RESEARCH FOUNDATION

3. RESEARCH FOUNDATION This chapter gives the reader an overview of the terms relevant within the field and introduces a brief insight to the theoretical and professional background of me as the researcher.

3.1. A SITUATED STUDY Since no study can be impartial and unbiased, I find it relevant to outline some of the main events and scholars that have shaped how I understand, represent and interact with empirical evidence.

I have a personal interest in how technologies are transforming knowledge creation and shaping the world we live in. This has expanded my horizon on the technical aspects of how data analysts work and which tools and methods they use in their analytical practices. As I explore data management practices in GB, I bring these insights into my analysis which both assist my understanding and frames how I apply meaning to particular events.

My employment as a web developer at CB allowed me to enter the organizational practices of GB. Insights represented by the Strategic Coordinator from CB initiated my original interest in how data was valued and utilized, so it is important to address that the empirical evidence gathered in this study can be shaped by how I was able to access the organizational practices through observations, presentations and interactions. At meetings with members from GB, I was presented as a researcher from the IT University of Copenhagen, but occasionally I believe that some actors rather considered me a representative from CB. This is important, since these actors might have been shaped by my presence. I provide a reflective account of this notion in 7.2. Empirical Evidence, page 67.

Through courses on marketing and communication, I have gained a solid foundation for understanding business management practices and terminologies. While this has allowed me to translate actor behavior and interactions into organizational practices, I acknowledge that my perception and reflections are shaped by these predetermined conceptualizations. On a theoretical level, I am influenced by particular framings and conceptualizations by scholars within Science and Technology Studies (STS). In particular, I might relate my practices to Donna Haraway on Situated Knowledges; Susan Star and Geoffrey Bowker on classifications; and Bruno Latour, John Law and Michel Callon in terms of Actor Network Theory (ANT) and digital methods. Although I seek to define my own conceptualizations throughout the analytical phases in this paper, I recognize

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3. RESEARCH FOUNDATION how these scholars might have influenced my initial understanding of these concepts.

Furthermore, as Bourdieu argues, sociologists should strive to deconstruct the objects studied, including its relation to its own and other fields as well as the researcher’s own place in the study (Bourdieu & Wacquant, 1992, pp. 254-256), in order to ensure that the work produced is not rendered a reproduction within the theoretical domain. My empirical evidence is thus described from my own conceptualization (with the above personal, professional and theoretical perspectives in mind), while I extend my analysis with arguments by other scholars.

3.2. VOCABULARY In the exploration of data management processes, I use certain terms and definitions that will require the reader to have a basic understanding of how these principles are embedded into organizational practices. My conceptualization of these terms are defined here.

3.2.1. DATA Data is a broad term, often defined by the actors who utilize it in their practices. For this paper, I will mainly define data from the perspective of actors from CB and GB. Data in this context are described as customer knowledge, such as contact information, business affiliations and the customer’s interactions with the company (e.g. web behavior and newsletter recipients). Hence, the term ‘data’ is used in plural tense in this paper, while ‘data entities’ relates to specific units within the data. Initially, the actors refer to data from GB’s current database of customers, but when actors discuss data in relation to the Lead Generation project, many sources of current and future customer data are regarded as well. While this usage of the term is generally accepted by all the actors involved, a diverse conceptualization of the term will in some cases become apparent.

3.2.2. CUSTOMERS In this thesis, customers are defined and referred to as either suspects, prospects, leads or opportunities (‘Lead generation Workshop,’ 2015, slide 37). These are marketing terms that allow companies to classify customers in terms of their potential value. Suspects are anyone who might have a basic interest in the company, but who may not necessarily meet qualification criteria formulated by Marketing and Sales. Prospects have indicated their interest in the products or knowledge provided by the company and leads are prospects that fits qualified criteria provided by Sales and Marketing (e.g. related to geography and company size). When leads with the right authority and budgets has shown a genuine interest in the company’s products, they are defined as opportunities. In this paper, leads are represented by CB and GB as as data entities in the customer database with contact details as the main reference point for Sales and Marketing.

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4. RESEARCH DESIGN

4. RESEARCH DESIGN In this paper, I seek to uncover some of the processes that relates to data management practices. I will refrain from having a predetermined theoretical focus as I embed an inductive approach to my research in my exploration of one account of data valuation, management and usage. In this chapter I introduce preliminary considerations and describe elements from ethnography and Grounded Theory that I use as a framework to describe and unfold the organizational processes that relate to my research questions. Following this, I review the methods used for gathering and analyzing data; and concludingly, describe the analytical phases of this study.

4.1. PRELIMINARY CONSIDERATIONS Through the initial phases of this research study, I had several ethical concerns of how I approached, interacted with and represented the actors involved. I wanted participants to understand the general reasons for my study, while their interactions should not be shaped by my presence. Recognizing that these requirements would be difficult to achieve, I gave a brief introduction to the context and purpose of the study, both verbally and within a written project description (see Appendix A). With no predefined theoretical focus, I was able to introduce the actors to a general description of the project goals, which I believe refrained them from changing their organizational practices significantly. Since internal budgets and practices were shared through these meetings, I found it essential to give all actors and involved parties anonymity, although this was never requested from the actors involved. In all revised notes and documents, I have therefore ensured that names were changed for companies and actors.

4.2. METHODOLOGY In order to explore the social interactions related to data management practices, I embed theoretical concepts and ideas from ethnographic studies and Grounded Theory. Since the multiplicity of these theoretical domains vary, I use this section to account for my notion and usage of the main principles and perspectives I am influenced by in this research study.

4.2.1. ETHNOGRAPHY Ethnography is often regarded as the preferred method to study social settings (Knoblauch, 2005; Miller, 2014). Even though this method has been practiced for more than a hundred years, the fundamental principles are still relevant today. Ethnography as a methodology focuses on gathering knowledge through observation, which can be gathered using both quantitative and qualitative methods. However, as identified by Giampietro Gobo, the role of the protagonist is what distinguishes ethnography significantly from other

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4. RESEARCH DESIGN methodologies (2008, p. 5). Two research strategies are proposed in ethnographic studies: participant observation and non-participant observation, which refer to the level of interaction the researcher has to the subject observed. Interaction is avoided with the latter, while participant observation encourages the researcher to establish relations and interactions with the social actors in their natural environment. By acknowledging the gap between attitudes and behaviors, Randall, Harper and Rouncefield elucidate the importance of ethnographers to understand how social realities are arrived at, rather than describing what they are like (2007, p. 194). Ethnography offers detailed insights to the organizational structure by allowing the researcher to observe people in their natural settings. As Randall, Harper and Rouncefield observe, it might even be possible for the researcher to obtain a bigger view of the organization than the one available to other organizational members (2007, p. 175). While the position as a researcher gives a broad overview of the organization, it is essential to recognize how the perspective gained can never be completely neutral or objective, rather it gives a fragmented and contemporary view of the actors and processes covered. As Emerson (1995) states: “[t]he ethnographer cannot take in everything; rather, he will, in conjunction with those in the setting, develop certain perspectives by engaging in some activities and relationships rather than others�

(Emerson, 1995, p. 2). While there is much empirical evidence to be collected, it is relevant to recognize that for the study itself to become meaningful, the ethnographer has to choose a specific focus (Randall, Harper, & Rouncefield 2007, p. 183). This study will thus use a qualitative research study to investigate social processes in the Lead Generation project.

My position as an employee of CB allowed me to be accepted as a member of the organizational domain. This has given me a key understanding of the organizational structure of the involved companies and brought me unique insights to the meetings where problems were discussed and decisions formed. Through participant observation, I was able to enter the social setting of GB and establish an adequate relationship with many of the actors involved with the Lead Generation project. Although I kept interactions to a minimum, I acknowledge how this could shape my perspective and representation of the organization. With field notes and documents from meetings as my main source of knowledge, I rely on the principles from Grounded Theory to explore the empirical evidence further.

4.2.2. GROUNDED THEORY Introduced by Glaser and Strauss in 1967 as a corresponding method to current practices, Grounded Theory is a methodology that seeks to construct theory through the analysis of data, rather than through logical deduction from prior knowledge and studies (1994, p. 5).

Analysts should embrace emerging categories from the data as patterns and interrelations

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4. RESEARCH DESIGN are formed (Glaser & Strauss, 1994, p. 40). As elucidated by Strauss and Corbin, theory is evolving throughout research through the continuous interplay between analysis and data collection; an approach referred to as the constant comparative method (1994, p.

273). The constant comparative method proposes four stages for developing a Grounded Theory: first, the analyst should generate categories to compare incidents; second, categories should be integrated with joint characteristics; third, the analyst should seek to delimit categories and theories; and through these stages, the analyst should finally be able to generalize incidents and develop either discussional or propositional theories (1994, pp. 105-113). Glaser suggests that analysts should use theoretical sampling for collecting, coding and analyzing data, which is best done by using initial findings as a foundation for collecting new data (1994, pp. 105-113). Theories can be developed using the coding schemes of open coding, axial coding or selective coding. With open coding, the categories are expected to emerge from the study; axial coding allows the analyst to make new connections between categories; and selective coding requires the analyst to represent emerging patterns by selecting a core variable that categories should be guided and grouped by (Emerson, 1995, p. 23). While this process of coding seeks to emphasize certain aspects of the events, it is important to recognize how some characteristics are concealed from these codes. Emerson describes this uncertainty of coding as a matter of creatively “linking up specific events and observations to more general analytic categories and issues”, rather than simply ‘discovering’ what is in the data (1995, p. 25). Strauss & Corbin (1994) describes how this methodology aids the creative generation of theory (p.

103) and closes the gap between theory and empirical research (p. 275), while it is argued that generating theory and doing social research are two parts of the same process (p.

103).

As data are collected through ethnographic methods, I rely on Grounded Theory to structure the analytical process as a basis for theorizing findings from the study. My main empirical evidence, field notes, were categorized in several stages as I applied the constant comparative method. Using open coding, I have been able to categorize the empirical evidence and break the data into conceptual components. Axial coding was afterwards applied to identify relations and patterns. These categorizations of my field notes allowed me to reshape my analytical focus and relate each topic to a larger and more inclusive concept. I expand on this in 5.1. Identifying Theoretical Concerns, page 22. Through the constant comparative method, I developed propositional theories by comparing different sections from field notes in order to find related cases that either confirmed, denied or added new properties to my categorizations. To allow myself to review and challenge this thinking, I wrote main theoretical considerations and events in a research log (see Appendix B). In 4.2. Methodology, page 13, I describe in depth how

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4. RESEARCH DESIGN these stages were conducted, as I relate to specific methods and tools for my analysis.

4.3. METHODS Based on the methodological considerations of ethnography and Grounded Theory highlighted in the previous section, this section describes how the empirical evidence was gathered, analyzed and represented.

4.3.1. EMPIRICAL EVIDENCE This research design proposes a qualitative strategy for gathering empirical evidence.

Although Grounded Theory does not state either qualitative or quantitative research to provide a better foundation for theorizing, Glaser & Strauss emphasize sociological theories are often found using qualitative methods (1994, pp. 17-18). My focus on how actors selected and valued data encouraged me to pursue a qualitative method for gathered the empirical evidence of the thesis. Through this method, I am able to go in depth with the social settings within the studied field. With a focus on social interactions and the processes of data management and contextualization, I wanted the empirical evidence to be as naturally occurring as possible. Having that in mind, I believe the principles of participant observation served best for this research design.

Actors related to the Lead Generation project constitute my main empirical evidence.

While these are mainly actors affiliated with GB and CB, the diversity of meeting contexts introduced additional actors to the study. A total of 15 actors were present at the meetings, and to illustrate the presence of these actors in relation to their affiliation, I present Figure 1. As with the rest of this paper, the names have been altered to guarantee anonymity of the subjects encountered during the field research. While this table should help the reader to get an impression of the variation of actor representation in the meetings, I highlight a few noteworthy aspects. Hillary was the Lead Generation project owner, which explains why she was present at all meetings. As the project owner, it was her responsibility that the project goals were reached and that all decisions were reached through efficient processes. Ann and Bill from CB assisted her with procedural and technical decisions during the meetings. Although actors from the Web Bureau (WB) and the Data Bureau (DB) were each only present at one of the meetings I observed, they were indirectly present at meetings where their architectural proposals were presented.

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4. RESEARCH DESIGN

Figure 1: Actor overview of meeting presence. The colors represent actor affiliation.

In addition to studying actors in their natural settings, I was given access to PowerPoint presentations from both CB and WB, which provided me with deeper insights to the visual frameworks that guided the meetings. Apart from these bodies of empirical materials, I have had several meetings with the Strategic Coordinator (Bill) since December 2015 and have been able to consult with him regarding details from the study throughout the writing of my analysis. A full account of the revised and anonymized field notes are available in Research Log, page 78. Emerson provides a notion on how field notes provide the “ethnographer’s, not the members’, accounts of the latter’s experiences, meanings, and concerns” (1995, p. 6). As I seek to reflect upon social interactions based on my own field notes, this notion serves as a helpful reminder for not making unfounded assumptions about actor intentions.

4.3.2. INSCRIBING EVENTS Venturini highlights one of the important reasons for representing the process of inscribing events: “[t]o trace a phenomenon means converting it in a piece of writing” (2012, p. 8). This highlights the concern shared by many social scientists the methods for inscribing and formalizing events. Inspired by the principles of Grounded Theory and ethnography, I have inscribed events in manners that I found to serve my procedural exploration best. As Emerson argues, there is no ‘correct’ way to describe observations.

Since descriptions involve issues of perception and interpretation, “different descriptions

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4. RESEARCH DESIGN of ‘the same’ situations and events are possible” (1995, p. 2). To allow myself to reflect upon this knowledge creation process, I used a Research Log to document all stages of translations that my empirical evidence went through (see Research Log, page 78).

This documentation process is highly encouraged by Emerson as such notes allow the researcher to preserve distinct experiences “close to the moment of occurrence and, hence, for deepening reflection upon and understanding of those experiences” (Emerson, 1995, p. 7).

To give an account of the process that formed the development of theories, I will describe each of the procedural events that influenced the representation of the empirical evidence in this paper. I initially captured main statements and conversational topics of each meeting through tablet notes and drawings. These notes were written in the spoken language, Danish, and apart from describing the main conversational topics, the notes included both renditions of actors’ physical position and my personal thoughts on the interactions and processes taking place. Within 24 hours after each meeting, the notes were translated into English and transformed into a more thorough description of the events (between 6 and 29 pages), which would include new considerations that I, at this point, had found valuable (e.g. attitudes and other descriptive comments). This inscription process in between meetings allowed me as a researcher to reflect upon the contextual and sociological events in depth, which enabled me to observe the next meetings with new lenses. In spite of the general practices in Grounded Theory, two meetings were recorded, which allowed me to review my own noting procedures and identify misinterpretations.

After the fourth meeting had taken place, the field notes were first of all coded in terms of context, while a later focus regarded actor interactions and attitudes. These coding schemes provided me with an overview of what was being discussed and how actors interacted, which I utilized to reflect upon the reasoning behind these social interactions.

Recognizing that these notes would be used extensively for my own comprehension of the study, all notes were at this point anonymized by altering names of the actor and parties involved. An interesting side-effect of this is how it has allowed me to conceptually divide my knowledge about actors from CB between what was learned during the study and what knowledge I had obtained during my employment at CB. As I reviewed the notes to begin theorizing, axial coding was applied to identify relations. At this point, I began iteratively to integrate, refine and write my theoretical considerations.

4.3.3. EMPIRICAL METHODS For analytical purposes, my field notes has been collected in MAXQDA, a Computer Assisted Qualitative Data Analytic tool. This tool provided me with a toolbox for coding, analyzing and visualizing the field notes. I personally applied the segment codes, and

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4. RESEARCH DESIGN the visualizations I use are based on statistical observations. Although MAXQDA itself provides some automated analytic tools and visualizations, I refrain from using these partly black boxed methods. Thus, MAXQDA is mainly used for structural purposes. For further analysis, I have used spreadsheets for generating charts, as these have aided my understanding significantly. While these methods have provided me with overviews of complex observations, I have used these representations as initiators of asking new questions, which I investigate further by zooming into the coded field notes.

4.3.4. REPRESENTATION OF EMPIRICAL EVIDENCE In order for me to gain an overview of the meeting contexts and attitudinal changes, I developed several visualizations that allowed me to clarify and conceptualize the context and interactions of the study. Using these visualizations, I was able to identify new questions and concerns that I could investigate by zooming in on specific topics, meetings or paragraphs from the empirical evidence. The most informative visualizations are present in this paper, while I include a few additional figures in Appendix L. Through my thesis, I distinguish between figures I have developed as figures and externally accessed visualizations as artefacts.

One particular representation used in this paper, is a Document Portrait (see figure 2 and 3). This figure is developed using MAXQDA and it provides a sequence of codes for a given field note. Color attributes of the codes are displayed in a matrix with 1,200 circles and the size of the segments are used to weight the codes. The representation outlines the field note from the top left circle towards the last circle on the right on the last line. All 1,200 circles are divided up according to the share of the coded segments. (MAXQDA, 2014). The Document Portrait is useful for displaying the structure of a field note in terms of the occurrences of particular codes. It use the Document Portrait in this paper to illustrate the conversational structure of particular meetings.

4.3.5. CLAIMS ABOUT THE EMPIRICAL EVIDENCE The claims this paper develops are based on empirical evidence from one domain only.

Hence, the representativeness of this study could and should be questioned. In my attempt to generalize and theorize findings, I do not disregard this challenge. Rather, I seek to honor it in 7.1. Contribution, page 66, where I relate my claims to corresponding literature. With this in mind, I find it important to recognize the concern of Emerson: “if substance (‘data’, ‘findings’, ‘facts’) are products of the methods used, substance cannot be considered independently of method; what the ethnographer finds out is inherently connected with how she finds it out” (1995, p. 6). It would not be possible to replicate my study, but the disclosure of the empirical methods in this section should allow the reader to understand the methodological work that has been conducted in the development of

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4. RESEARCH DESIGN theories.

4.4. ANALYTICAL PHASES Through my extensive use of a Grounded Theoretical approach, I find it helpful to divide the analysis into two phases. For the first phase, I will refrain from referencing other theoretical concepts, as I believe this would be distracting for the claims I seek to make.

In this phase I describe the study with a focal point on aspects that related to how data are conceptualized, represented, selected and applied value. In the second phase of my analysis, I elaborate further on some of the concepts that have been extracted from the study. Here I embrace relevant literature from the field of Science and Technology Studies to account for the generativeness of my claims.

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4. RESEARCH DESIGN

ANALYSIS I: FIELD STUDY

THIS CHAPTER INTRODUCES THE EMPIRICAL EVIDENCE OF THE FIELD STUDY. EACH SECTION IS BRIEFLY DISCUSSED FROM AN ANALYTICAL PERSPECTIVE.

5. ANALYSIS I: FIELD STUDY

5. ANALYSIS I: FIELD STUDY The story of value begins as an analytic endeavor. To unravel this study, I use a chronological approach to describe and represent the empirical evidence of my claims. This approach is only helpful, if the reader is given an account of the theoretical concerns that have guided my thinking through this research. Thus, I initiate this chapter by describing the two theoretical concerns identified. Following this, I describe where the Lead Generation project is situated and subsequently I guide the reader through my observations in a chronological order. Through this description I will embrace and deepen the understanding of events that relates to how data was conceptualized, represented, selected and valuated by various actors. While this should help you as a reader to understand how claims are derived from the study itself, I conclude the section by assembling all insights with the research questions in mind. In 6. Analysis II: Exploring Findings, page 50 I use insights from this section to construct arguments about how the concepts identified can provide a foundation for developing theories.

5.1. IDENTIFYING THEORETICAL CONCERNS The theory building in this thesis has been an iterative process, where I continuously compared my codes to visualizations, field notes and recordings from the meetings. The coding scheme that I used to organize my field notes, provided me with different methods for exploring the empirical evidence; both visually and structurally. My first coding scheme allowed me to identify that the main conversational topic at the meetings concerned the Project plan / scope (66 segments) and practices related to data management and architectural designs (62 segments). While I knew that Data management would be prominently represented on this list of codes, the former coding engaged my curiosity. As I explored the 66 segments within this category, I discovered that most of the segments related to either existing project plans or management practices. A pattern within this category emerged. This pattern reflected how meetings were distinctively driven by procedural practices and structures; or particular framings. Further analysis of the field notes allowed me to reflect upon how data conceptualizations and valuations were influenced by specific framings. In the following sections of this chapter, I explore different examples of these framings, but first I would like to expand on another notion I explored.

While my first coding scheme mainly covered the context of the conversations, I started to question the purpose of actors’ interactions throughout meetings. This encouraged me to develop a new coding scheme of five categories: Asking questions, Idea generation, Knowledge sharing, Opinions and views, and Decision making. Like with the first coding scheme, these categories are derived from my own conceptualization of the events recognized in the field notes. The identification of these categories initiated a new set of

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5. ANALYSIS I: FIELD STUDY questions of why, when and how specific interactional patterns emerged. In my exploration of these patterns, I discovered each of the categories to be motivated by actors’ intentions of translating their comprehension towards others. This notion will become more explicit as I dive into specific examples from the empirical evidence that accounts for the use of translations.

As I explore the Lead Generation project throughout the rest of this chapter, it is revealed how Framings and Translations are embodied into the practices of the members of CB and GB. Thus, each section will be concluded by how the meetings are shaped by particular framings and how translations are enhancing the understanding between actors.

5.2. ENTER THE FIELD Since this study focuses on the social sciences of organizational interactions with data valuation, I will avoid describing the studied organization in detail. However, I find it relevant to note that the organization studied is a medium sized enterprise (based on standards from the European Union, see Eurostat, 2015) with affiliations in more than 20 countries. GB are operating within the Business-to-business market – all customers are therefore from the corporate market. Although all decisions regarding the Lead Generation project are made by actors from the head office in Denmark, the results are going to be embedded into practices within the whole organization. Before we dive into the insights generated from my observations of the Lead Generation project, I find it useful to account for events prior to my involvement, as these shape how the Lead Generation project was initiated. These events are described based on renditions and PowerPoint presentations (‘Lead generation Workshop,’ 2015; ‘Workshop Reporting,’ 2015) given by the Strategic Coordinator from CB (Bill).

In 2014, CB was contacted regarding the development of a new global website for GB. Throughout 2015 CB was in dialogue with the marketing department of GB regarding the concept and design of a new website. As the focus for the new website was to improve communication with the customers, this project fostered several questions about the customer data GB currently stored: what data was stored about their customers, how did GB’s target groups relate to actual customers, and how did the company use this data in their interactions (i.e. through communication and sales) with customers? While CB expressed that this data could be used for many beneficial business propositions, it was revealed that the data was mainly used as a reference tool for the Sales department.

Consequently, a new Lead Generation project was initiated by the Marketing Manager Hillary and CB in August 2015. During the same quarter of 2015, managers from the Sales and IT departments began formulating a Business Intelligence project with the focus of improving customer insights for the Sales department.

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5. ANALYSIS I: FIELD STUDY In order to understand the processes of the Lead Generation project, the empirical evidence is now divided into five sections, which I will now give a short introduction to: 1. A Preliminary Analysis: A workshop initiated a discussion about how the organization could create value from their current customer database of customer interactions and buying behavior. CB analyzed their customer database and identified five new behavior-based target groups. While this mainly provided GB with a concurrent view of their customers, the insights revealed the possibilities of such analytic procedures. Thus, it was decided to expand the Lead Generation project to allow Marketing and Sales a more timely and elaborated overview of leads; a scoring of leads.

2. Defining Requirements: To initiate the discussion of how a tool for lead generation should be developed, actors with different fields of knowledge met to discuss the possibilities and challenges this project could be influenced by. Insights and framings from the preliminary analysis were presented and an architectural sketch of the data management and scoring procedures was developed.

3. Lead Scoring: Actors from Marketing, Web and Customer Relations met to discuss which data entities collected from the website that should be regarded valuable, and hence become part of the customer scoring variables.

4. Data Management: The requirements of the lead scoring engine was discussed and two companies introduced what technical aspects that would constitute their data management architecture.

5. Accounting for the chosen solution: An agreement was reached between decision makers from IT, Marketing and Sales on the project goals and data management architecture. While the project needed additional funding to continue, the Vice President of GB was given a presentation of the project outcome and new premises that accounted for the required funding.

Each of these sections are described below, through defining moment that relates to my analytical focus of framings and translations.

5.3. A PRELIMINARY ANALYSIS The main objective of the Lead Generation project was initially to establish how value could derive from current customer data in order to help improving their current interactions with customers. Having in mind that such project needed to be driven by decision makers from the organization itself, CB used preliminary insights from the customer database to facilitate a workshop with managers from GB. Although, I was not present at the workshop myself, Bill gave me a brief rendition of the context and outcome of the meeting.

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5. ANALYSIS I: FIELD STUDY According to Bill, the purpose of the workshop was two-fold: CB wanted decision makers to take ownership of the project outcome and they wanted GB decision makers to identify what customer insights they found relevant. To facilitate this discussion, CB asked the participants to write down customer data entities on sticky notes and describe the relevance of them (see Artefact 1). Data entities that were agreed by participants to be relevant were afterwards categorized by three distinctions: Core data, Interactional data and Trigger data. These distinctions were introduced by CB: Trigger data, as data entities that are bound by behaviors and personalities, e.g. website visits and e-shop activities; Interactional data, as data that concerns customer interactions with GB, e.g. phone calls, and event participation; and Core data, as basic customer knowledge and company affiliation, e.g. name and email. The final lists of identified data entities were afterwards polished by CB (see Appendix E) and integrated with how data was afterwards defined among decision makers in the organization.

Artefact 1: Brainstorming session from the Lead Generation Workshop on 05.10 2015.

5.3.1. DEFINING LEAD GENERATION The workshop from September 2015 did in many ways operate as a baseline for defining how CB should understand the organization and which lenses they should use for observing their customers. To establish a foundation for understanding how lead generation – identifying consumer interest – should be utilized in GB, a discussion was facilitated by CB. Although I was not present at the workshop, I use six presentation

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5. ANALYSIS I: FIELD STUDY slides (see Presentation slides, page 90) and insights provided by Bill to account for how the lead generation process was collaboratively defined: 1) A slide entitled ‘Objectives’ initiated the discussion. While only listing six stages of actions that the Lead Generation project required, this slide allowed CB to situate the process of defining a lead in relation to the other main goals. Hence, the relevance of this stage was clarified for the members of GB. 2) Within the field of Marketing and Sales, the complexity of understanding consumer behavior and the need for prioritizing selling activities has resulted in a variety of simplistic models. This is evident in GB as well, where they currently handle leads through a lead funnel; a set of procedures that a lead will go through in order to become a customer. To facilitate a discussion about the lead generation process, CB used the next slide to recognize GB’s current approach to leads (see Artefact 2). This lead funnel was based on a similar model by Miller Heiman (Whittaker 2015) and described in five main stages: Universe, Above, In, Best Few and Order. These rather inexplicit marketing terms are basically defined as: Universe, all potential customers; Above, qualified customers; In, verified customer potential; Best Few, clearly defined customers; and Order, the customer orders something. It resembles a funnel because of the amount of potential customers from the Universe is larger than the actual customers seen in Order. In this version of the model, GB had outlined how each of these stages related to their sales process within the organization. While this model represented how GB currently considered leads, CB found it relevant to use this model as a starting point, when defining leads in collaboration with the participants.

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5. ANALYSIS I: FIELD STUDY

Artefact 2: GB Sales Process & Funnel Steps (2015-10-05 Lead generation Workshop, slide 36).

3) Recognizing how a lead discussion could quickly escalate into other matters of concern, the following slide served as a reminder of what leads generally are defined as: “Leads are those prospects that fit the qualifying criteria. Qualifying criteria should be predefined by marketing and sales� (see Presentation slides, page 90); in short, leads are thus defined as potential customers who are qualified as relevant by Marketing and Sales. To emphasize how leads related to the other customer distinctions, the same slide included notions of how suspects, prospects and opportunities were defined.

4) The next slide served as a point of reference by introducing a simplistic sales funnel. This model was based on the same principles as the lead funnel that was currently embedded in the organization with only three of the aforementioned stages present: Above the funnel, In the funnel and Best few. This figure allowed actors from both CB and GB to represent their views of how a lead funnel could best be utilized in the organization.

5) When the next slide was shown (Artefact 3), all the basic elements of the lead funnel had been discussed among the actors and a somewhat aligned understanding of the lead process had been determined. With this model, a new layer was added to the top of the classic sales funnel; the Marketing Qualified Lead, customers that

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5. ANALYSIS I: FIELD STUDY are converted into leads through marketing determined qualifications (Walters, 2015). This model elucidates the influence of marketing in qualifying leads, rather than mainly relying on the Sales department to interact with prospects. With this model, CB introduce a new approach to managing leads.

6) On the final slide regarding the lead funnel, CB asked participants to reflect upon which data generated from Marketing could aid Sales in doing their job.

As an outcome from this discussion, CB had allowed GB to see the relevance of generating Marketing Qualified Leads.

The rest of the workshop was constructed similarly with particular framings and definitions provided by CB, concluded with specific questions or discussion topics for the participants.

Artefact 3: Lead Funnel with Marketing Qualified Leads added (2015-10-05 Lead Generation Workshop, slide 39).

5.3.2. ANALYZING CUSTOMER DATA Prior to the workshop in September, CB was given access to more than 200,000 customers from the company database. Carl, A freelance Data Analyst was contacted

by CB to help them with the task of analyzing these datasets in order to find meaningful insights about the customers. Some of the most relevant findings about engagement levels were presented in the end of the workshop. By scoring different engagement interactions from the available customer data, Carl was able to identify five significant engagement levels (clusters) from the customer database. Focusing on the three biggest markets (United States of America, Great Britain and Germany), CB presented how these five engagement levels were distributed among the CB customer base in terms of share

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5. ANALYSIS I: FIELD STUDY of companies and share of turnover. Artefact 4 provides a representation of how Cluster 1 performed in terms of key parameters such as consumables, newsletter open rates and e-shop purchases. While the graph provides a comparable insight to the complete customer database, CB had extracted insights about the quality and value of the data, e.g. how many of the customers from this segment that were defined as key contacts (decision makers); how the segment was represented within the primary market of GB; and the average turnover of the customer within the last 12 months. Since these clusters themselves are not directly relevant to this paper, I will refrain from elaborating further on how they were constructed. According to Bill, this was the first time this many different sources of customer data from GB had been linked, and by identifying profitable segments from their current customer database, the management were able see the relevance of this new approach to lead generation. This visual representation of their customer database engaged the participants to address compliance issues with their current data management practices and to explore what other data entities that would be useful for their organization to gather.

Artefact 4: A representation of how customers in Cluster 1 performed (2015-10-05 Lead generation Workshop, slide 63).

When the decision makers were later presented with a conclusive brief that incorporated both analysis and decisions from the workshop, it was decided to extend the Lead Generation project. The insights generated from the preliminary analysis of their customer database provided GB with a useful notion of how they could improve sales processes

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5. ANALYSIS I: FIELD STUDY and customer interactions by dividing users into data-verified segments.

5.3.3. A PRELIMINARY ANALYSIS; IN DISCUSSION Although the descriptions above rely on presentations and insights from Bill, I believe they give the reader an informative introduction to some of the preliminary decisions and frameworks that have been embodied in later practices of the Lead Generation project.

Several notions of framings and translations appeared through this rendition, which requires further discussion.

The distinction of data as either Core data, Interactional data or Trigger data served as a guide for decision makers to reflect upon the different kinds of customer data available.

While this was perceived as a helpful ordering by the actors from GB, I find it crucial to reflect upon how this distinction required the actors to embed a particular framing into their practices. Through this framing, data was distinguished based on a certain set of criteria that required actors to define their conceptualization of the entity. Data entities that did not immediately embody these criteria would require actors to translate their perception of the entity and adjust the definition to fit within one of the defined categories. Having this in mind, I believe actors might have found it difficult to decide on the classification of Web behavior as either Interactional data or Trigger data. I would argue that Web behavior can both translate to customer interactions and behavioral patterns.

When entities are categorized, certain aspects from the subject is emphasized, while other characteristics are concealed. This is a relevant notion since this ordering is utilized in later practices and conceptualizations in the organization. Since the data entities are defined and sorted based on perceptions from the present actors, who is to say that this definition is maintained in later practices? While I recognize the value of using a framing for identifying and organizing data entities, I find it relevant to question how this influences the outcome of the discussion. Would the selection process, for instance, have turned out differently if there were no predetermined distinction to confine the entities by? Would costumer data later be weighted differently? While it would be difficult to answer these questions directly, I present a similar process in 5.5. Lead Scoring, page 35, where no distinction between data entities existed.

When the lead generation process was discussed, CB provided a framing for managing the translational complexities they expected to encounter. While CB wanted to present a new way of recognizing and handling leads, they initiated this discussion using GB’s current framing of a sales funnel. This allowed the actors to reflect upon current practices and how these related to lead processes in the organization. When a similar model was presented afterwards, the actors from GB were able to reason with the arguments proposed by CB and integrate these conceptualizations into their own framing. I believe

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5. ANALYSIS I: FIELD STUDY CB used this approach deliberatively in order to help the decision makers in translating their current practices towards a new approach.

By analyzing GB’s current customer database, CB illustrated how lead generation could be utilized differently by integrating customer data from various sources. Using an algorithm to identify profitable segments from their current customer database, actors were shown how customer knowledge could be translated into profitable insights. While this advanced segmentation only gave a contemporary view of the value of GB’s customers, it demonstrated the possibilities of embedding new practices for managing and exploiting customer data in the organization. This demonstration encouraged actors to translate a visionary idea into a new business proposition: data-driven lead generation. While these predefined framings seem to be embedded into the presentation with persuasive means, it is important to recognize that CB was hired by the organization to provide expertise and guidance through this project.

5.4. DEFINING REQUIREMENTS Having the workshop and considerations in relation to the predefined framings and translative effects as an empirical foundation, I continue with a more observation-driven empirical account of data management and valuation. Here, I was able to observe how frameworks, ideas and concepts from the initial phase were embedded into the discussion of the lead generation processes. While the workshop was mainly driven by CB, the remaining part of the study is to a larger extent driven by the Marketing Manager Hillary, who was appointed the project owner. This section describes defining moments from the first observed meeting (see 2016-02-12 Lead Generation, page 80).

In early February, Hillary invited the Head of Global IT (Eric), the Customer Relationship System Manager (Fred), three employees from WB (Ken, Lars and Martin) and the Data Analyst (Carl) to a meeting regarding the next phase of the Lead Generation project. I was present in my capacity as a researcher and was briefly introduced as a student affiliated with CB. As Hillary had realized that several departments from GB had an interest in the outcome of the project, she wanted to embrace the complexity of the project by inviting all relevant parties to share their perspectives on the project process and goals. Hillary initiated the conversation by giving a short briefing of the insights generated from the initial customer analysis. As she began to explain some of the decisions that had been made regarding lead generation in connection to the website, Eric interrupted her to ask: “What is a lead? Is it a lead when the customer has been contacted? Does it need to be activitydriven?” (see Appendix C). These questions revealed how Eric (as well as most of the other participants at this meeting) had not been included in the initial phase where leads in their organizational context had been defined. Though Hillary, at first, became rather

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5. ANALYSIS I: FIELD STUDY distracted with this question, she acknowledged the relevance of a proper answer. She defined leads as “those prospects or customers that fit the qualifying criteria” (‘Workshop Reporting,’ 2015, slide 8). To make this statement more usable, she translated it into an example of how she would define a lead in the context of GB: someone who have previously bought something from us or someone who have a lot of interactions with us.

With a focus on the practical usage of the insights gained from the customer analysis, Hillary continued the meeting by disclosing some of the considerations she and CB had about weighing and scoring the customer data. Using the algorithms developed by the Data Analyst for the preliminary customer analysis, she wanted to develop a scoring system for all new leads in order to determine their value. The scope of this project could be very extensive, so she wanted the other participants at this meeting to share their views on how such a system could be built and what data sources that should be integrated. This resulted in an extensive discussion about how specific data entities could be extracted from the different systems and integrated with a scoring algorithm and how this data would be visually represented for the end-users (mainly members of the Sales and Marketing departments). While Eric expressed his concern about data security and what limitations he could identify, the employees from WB asked specific questions about the requirements of the proposed system and what integrations would be needed. An eager discussion about the scope of the system had begun, and Fred responded to the many ideas and requirements by expressing his desire to start developing right away. As Eric recognized, it was too early to initiate any development.

At this point, the meeting agenda had become unclear to the actors, which Martin from WB addressed by picking up a whiteboard marker to draw his perception of the system architectural design. While he drew, Carl, Eric and Ken continued a conversation about the algorithms that should be embedded to account for the scoring of the data. When Martin had finished drawing his conceptualization of the lead scoring architecture, he presented it as a technical solution that might be possible for WB to develop. Artefact 5 is a rendition of his drawing and can be explained in a few steps: data from several sources are imported into one database that allows a Rules Engine to analyze and score the data.

The scored data produced by the Rules Engine are exported and sent to a dashboard where the data can be represented visually.

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Artefact 5: Data Architecture drawing by WB. My original sketch made during the meeting can be seen in Appendix D. This drawing helped the actors to articulate new arguments and question specific concerns about the proposed architecture. “Where is the website in this model”, Hillary initially asked, at which Martin replied that it would be part of the Data sources. Eric was curious about why the data would not be imported directly into the Rules Engine, which Martin answered by explaining how the algorithms would need extensive processing powers for both tasks. A question about the purpose of the Rules Engine encouraged Martin to add ‘Analysis’ underneath it. By pointing at different features of the complex model, the actors were able to identify unclear details of the architecture and emphasize which implementations that might cause concern in terms of security and management.

Using this model, the actors were able to clarify which data sources they wanted to integrate. While this model was made without any pre-analysis, Martin recognized that it might be more complicated to setup than his representation indicated. He assessed that WB would be able to design and setup a solution like this, but that they currently saw too many undisclosed variables in terms of the data sources, the analytic algorithms and the output. In order for WB to estimate cost and time frame, it was decided that Carl would send them his SQL-strings and Fred would give them access to sample datasets from

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5. ANALYSIS I: FIELD STUDY the database.

5.4.1. DEFINING REQUIREMENTS; IN DISCUSSION Decision makers from different fields were gathered at the Lead Generation meeting to discuss how to embed the considerations from the initial customer analysis into practice.

They challenged the complexities of utilizing customer data and transformed these thoughts into a new project: lead generation through customer scoring. While few of the attendants had been included in the initial phase of the project specification, this meeting revealed some unclarities about the project scope and specific definitions. Thus, it was established that they needed a shared definition of a ‘lead’, which Hillary reacted to by using a CB definition in connection to examples of how she imagined leads to be defined in connection to GB. A very interesting aspect about this definition, is how Hillary presented the words and models from CB as her own. She had adapted to their framework and embedded their definitions into the project itself. While this aspect was not evident for the other attendees, they might believe that the definition itself is internally anchored. By relating the definition to contextual examples, Hillary managed to translate the broad definitions into a context that related to the actors’ conceptualization of how it might be applicable to GB. This elucidated the value of establishing and communicating these matters towards new actors that are expected to adapt to these practices and framings. I believe that the project outcome is largely influenced by how actors reach a shared understanding of these definitions.

When the practicalities of developing and maintaining a system for managing a scoring algorithm was discussed, it was found useful to translate the complexity of these matters into a drawing. This allowed actors to conceptualize the requirements of the system and to develop a shared understanding of the challenges they faced. While this drawing was introduced as Martin’s conceptualization of the required system, the artefact changed its status to become a particular framing that the other actors interacted with. This framing allowed for a more efficient discussion where actors were aided in identifying the next stages the project required. To understand how this framing changed the meeting agenda/structure, I developed Figure 2, a Document Portrait of conversational focuses throughout my field notes of the meeting (see 4.3.4. Representation of Empirical Evidence, page 19). In this figure, it is visually outlined how the focus of the conversation change from sharing knowledge and asking questions towards opinion-based concerns, fewer questions and a concluding decision making process. This model is helpful for illustrating the complexity of a meeting structure in GB, and it has provided me, as a researcher, with a deeper understanding of some of the translative effects that can be seen throughout conversations. In 6.3. Translations, page 56, I investigate this notion further as I explore the effects taking place when different worlds seek a shared understanding.

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5. ANALYSIS I: FIELD STUDY

Figure 2: Actor behaviors from the field notes represented as a Document Portrait of coded field note segments from the lead generation meeting on February 12, 2016 (see 4.3.4. Representation of Empirical Evidence, page 19).

5.5. LEAD SCORING Less than a week after the lead generation meeting, a meeting regarding the foundation of the lead scoring took place between Hillary, Judy (the International Sales Manager), George (the Web Manager) and Ann from CB. Although no agreement had been reached on how the system for the data management architecture would be developed, it was found relevant to discuss which user behaviors from the new website that should be measureable and be embedded into the lead scoring process. This section accounts for defining moments from the Lead Scoring meeting (see 2016-02-18 Lead Scoring, page 91).

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5. ANALYSIS I: FIELD STUDY

Artefact 6: An example of a wireframe of the new website design.

Using sketched drawings (wireframes, see Artefact 6) of the new website, Ann invited the attendees to discuss which user interactions from the website that should be appointed a user web score. These wireframes served as beneficial artefacts that allowed actors to conceptualize how the interactions would be manifested on the actual website. Ann argued that the interactions they chose should be generating value on the website, both for the user and GB. When Ann explained the reason for deciding on relevant entities with regard to the practicalities of setting up the scoring algorithm and having an intuitive dashboard in the end, she elucidated the importance of looking at this from both a Marketing and Sales perspective. Even though the users themselves are not mentioned here, each entity was discussed in relation to examples of hypothetical user behavior that could be seen from specific website interactions.

A brainstorm session of all forms of user interactions took place, and only one idea was neglected before it was written down; the search button. This was considered irrelevant as the scoring algorithm (at this point) would only be based on measurable actions and not contextual interactions, such as search queries. The Web Manager exemplified the complications that might be involved with this variable by asking: “is it valuable, if a student use the search engine to research information about our company?” (‘Lead Scoring’, 2016, translated by the author). This hypothetical example discouraged the actors from adding the search function to the list of valuable interactions. However, the actors

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5. ANALYSIS I: FIELD STUDY agreed that search queries could be valuable, if further analysis were appointed. Through hypothetical user stories, the attendants discussed the relevance of other interactions.

When an interaction was regarded relevant, George wrote it down on his computer. At one point during this process, they asked me, the observer, if I had any suggestions of relevant entities. The final list (see Artefact 7) was prioritized in the end of the meeting, mainly by suggestions from George and Hillary: Priority Scoring variable 1

Contact formula - both for mail and phone

1

Sign-up newsletter

1

Sign-up courses

1

Sign-up e-seminar

1

Play recorded e-seminars

2

Downloading preparation report

2

Risk profile test

2

Discovery test

3

Product folder, PDF

3

Product Data Sheet

3

Play video

4

Filter industry

4

Filter materials

4

Clicking Customized Solutions

5

ISO folder, PDF

6

Add event to calendar

Artefact 7: Table of scoring variables, with the most important user interactions in the top.

In early March, a list of all relevant scoring variables was defined by Hillary, Martin, Lars and Ann (see Artefact 8). This list consist of 7 categories of user interactions – from website behavior to personal contact with employees from GB – that would constitute the lead score. Recency, Frequency and Variation refers to different variables that should collectively define the value of a user interaction; i.e. a user who frequently purchase different kinds of equipment will be valued higher than a user who only buys a specific product once a year. The list was based on a conceptual proposition from the workshop, but the weighting and terms were defined collectively by Hillary, Martin, Lars and Ann. This simplistic list provided an overview of the weighting of the data sources for the scoring algorithm and would later be referred to as the weighted list of scoring variables.

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5. ANALYSIS I: FIELD STUDY

Artefact 8: Weighted scoring variables.

5.5.1. LEAD SCORING; IN DISCUSSION While 5.4. Defining Requirements, page 31 illustrated the complexity of designing the architecture of a lead generation process that relies on different sources of customer data, the account of the Lead Scoring meeting above provided a glance of one of the processes in determining user interactions that should account for how the lead score was identified.

Throughout the meeting, I experienced an implicit assumption about what was regarded valuable. While value was often used with regard to the measured entities, it did not seem like something the actors had discussed explicitly prior to this meeting. Ann described relevance in terms of how Marketing and Sales could use the knowledge provided by the identified user interactions, but it proved more useful when relevance was explained through narrative components. By translating user interactions into valuable behavior, the actors were able to reach a shared conceptual understanding of the relevance of utilizing specific interactions. While this illustrates the helpful usages of narration as a means for defining value, it is worth noting that this method is primarily based on assumptions about user behavior. If the users are not specifically asked about their intentions, the narratives that are created to define value might not be true. I explore this concern further in 6.3.3.

Using Narratives as Translation Devices, page 58.

Although the wireframes were introduced to frame the idea generation process, it was illuminated in several occasions that the evaluation process was regarded complex by the present actors; i.e. when I was asked to give my ideas. I experienced the meeting structure to be rather inefficient (i.e. unfocus actors and lack of a meeting structure),

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5. ANALYSIS I: FIELD STUDY which suggests that another framing could have been useful. With that in mind, I question why the customer data list made during the workshop (see 5.2. Enter the Field, page 23) was never presented at this meeting. Although such list would have shaped the idea generation, it could have been useful for constituting a checklist or as a framework for structuring the results.

The representation of weighted scoring variables (Artefact 8) inquires further discussion.

While this model might be more visually appealing than an unstructured table, it is relevant to address how the model embodies a framing provided by CB. At the workshop in September a similar overview of behavioral weighting was presented with the purpose of giving an idea of how a scoring model could look. The content was different, but by following the same template, the results could embody some of the same structural elements. Additionally, the simplicity of the model requires actors to have a shared understanding of the underlying definitions of the weighting criteria. If these intentions are not specifically documented, it might result in unintentional algorithmic designs. In 6.1.

Data valuation, page 50 I elaborate further on the evaluation measures in relation to theoretical concepts.

5.6. DATA MANAGEMENT Two meetings from the empirical evidence, had a distinct focus on the data management architecture (see 2016-02-25 Data Management, page 96); (see 2016-03-09 Data Storing, page 107), and this section accounts for some of the defining moments from these meetings.

Following the briefing in February on the Lead Generation project, WB formalized their initial architectural suggestion and visualized a revised data management architecture along with an estimate of how many hours the development of the system would require.

Artefact 9 provides an overview of the main integration requirements (Website, Pivotal, Apsis, etc.) and an illustration of how the system would be integrating the Scoring Engine, the website and a future Business Intelligence platform. The requirements of at least six integrations was weighted as a determining factor for the budget size. This architecture and budget proposal was first described to CB, who brought it to the Head of Global IT, the Marketing Manager and the CRM System Manager in March 2016. Although the new system architecture looked more elaborated, the budget estimations by WB reached far above the budgets for both Marketing and IT for 2016. While this was not well received, the actors from GB acknowledged that the reason for this unconvincing budget proposal was the presence of too many unknown factors for the project.

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Artefact 9: Data Architecture proposal by the Web Bureau.

With a certain skepticism of the estimated budget of the data management architecture WB had proposed, Bill discussed other approaches with Carl, the Data Analyst. Based on a suggestion by Carl, a bureau with a focus on data management (DB) was invited to explain how they would approach the lead scoring architecture. As when WB sketched their conceptualization of the data management architecture, DB found it useful to illustrate their proposal through a whiteboard drawing. For this purpose, DB introduced their procedures for similar assignments and asked specific questions about the limitations and possibilities that the current data management procedures of GB provided. This was translated by Nick, from DB, into the data management architecture seen Artefact 10.

With a solid confidence, Nick explained how this architecture would divide the procedures into separate environments, where 1) data entities from the GB databases would be sent to a Data Staging Area that would provide cleaning and structuring of the data; 2) this data would be collected in a Customer Data Margin where it could be accessed by marketing platforms, while scoring variables would be converted and weighted through a scoring algorithm in a separate environment; 3) the final weighted scoring variables of each lead would be sent to the Sales and Marketing dashboard (Pivotal) for visual representation. A Citrix server would be an integral part of the data management in DB’s environment. This artefact allowed actors with less technical insights to collaborate on the architectural model – i.e. Hillary were able to translate her vision of the dashboard into

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5. ANALYSIS I: FIELD STUDY particular technical requirements.

Artefact 10: Data Architecture proposal by DB. See my original drawing in Appendix K. While this architectural design was not significantly different from the proposal by WB, it is worth elaborating on the differences of how the architecture was presented. DB provided many references as evidence of their prior experiences with similar architectural designs. On the contrary, WB indicated on several occasions that they had never been involved with a project this complex before. Although Eric had a different opinion about the security measures that should be incorporated with their data, Nick expressed deep insights on the area and clarified their professional approach to the handling of data.

WB never demonstrated how they would handle data. Having the insights from similar projects that involved algorithms by Carl, DB were able to demonstrate technical insights on processes and integrations that were manageable. Additionally to showing that they would be capable of setting up this architectural design, DB provided a more realistic estimation of costs associated with building and maintaining the proposed system.

Although Eric indicated some concern of adding another collaborative partner to their IT projects, a consensus was reached at a later meeting on appointing DB as their main candidate for constructing the new data management architecture. Over the next two weeks, the specifics of the architecture setup and budget was discussed and a new architectural representation emerged (see Artefact 11). For this model, the same procedures as Artefact 10 are applicable, while it is also described how the website will be able to receive data through an API1.

1

Application Program Interface - a protocol that allows other programs to interact with or access data.

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5. ANALYSIS I: FIELD STUDY

Artefact 11: Data Architecture proposal by DB, illustrated by CB.

5.6.1. DATA MANAGEMENT; IN DISCUSSION Two architectural system designs were proposed for managing the lead score. Although the second design was not significantly different from the first candidate, DB demonstrated their capabilities, proper references and a more realistic budget estimation. While the translative effect of drawing the system architecture is similar to the use illustrated in 5.3.3. A Preliminary Analysis; in Discussion, page 30, I will refrain from repeating these considerations here. Contrary to the polished models and designs represented on presentation slides, the drawings that were developed at meetings illustrated a conceptualization that could be modelled and reshaped by the present actors. Hillary was, for instance, able to translate her vision of the dashboard into requirements that could shape the architectural model. This insinuates that all actors are given equal rights to shape the comprehension of the system architecture, although it might, in reality, mainly be shaped by actors with certain technical insights. When polished versions of the drawings were later presented, actors were able to relate to the models. Thus, I find great value in developing such conceptualizations, or frameworks, with decision makers.

5.7. ACCOUNTING FOR THE CHOSEN SOLUTION While an agreement for the architecture and project outcome had been reached between the decision makers of IT, Marketing and Sales, it had become evident that this project should be formally separated from the current Lead Generation project, as both goal, budget and time frame had changed. Following this, Hillary asked for a meeting with the Vice President of the organization where she and the consultants from CB could present their findings and discuss their proposed plan, budget and partnership. Here, I outline a few events from this meeting that relates to the analytical focus (see - 2016-03-18 Proposal).

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5. ANALYSIS I: FIELD STUDY

Artefact 12: Illustration of how the lead scoring of a customer could be represented in the Pivotal dashboard. The number above the tables indicate the weight of each entity, while the large number to the right gives an overall sum of the curated lead score.

Since Hillary initially described the phases they had been through, it became evident that David had been at the workshop in September, and therefore knew many of the details from the preliminary analysis. Both framings and categorizations were consequently known to the Vice President, and he were able to direct his attention towards the content of the proposal. As Hillary presented the two preliminary designs for the end-user dashboard (see Artefact 12 and 13), David responded with views and challenges of how this would work in a practical sense and become anchored within the organization. At first, David only saw Artefact 12 at which he showed his concern by asking if this was all the enduser (Sales and Marketing) would be able to see. As a fast response, Hillary turned to Artefact 13 that illustrated the interactions that had resulted in the score. While Hillary indicated that this was mainly a draft at this point, it became clear that David saw this as a suggestion much closer to the final result. He did not know that the idea of the bar chart was illustrated only two days earlier, when Bill saw the need for a simple illustration of the curated lead value (see 2016-03-16 Presentation Review, page 117). Acknowledging these aspects, David wanted to know how the proposed lead scoring was different from the approaches that the current tools provided. This seemed as something that should have initiated the conversation, but Hillary and Bill responded quickly: H: It has been connected.

B: Today there is no place that connects it all.

H: And today we have not prioritized the data inputs. Are some of

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5. ANALYSIS I: FIELD STUDY these customers more interesting than others? We are not able to see this. (Appendix M, page 129) The link between different databases was thus clarified as a determining factor for the uniqueness of this project. Ann clarified that the new process of lead management would both change the processes of how Sales would interact with new customers, as well as how Marketing could deploy a more relevant approach to customer communication. Bill extended on this approach by claiming that most of GB’s current customer interactions were driven by an inside-out approach (Marketing-driven customer behavior), while the new setup would allow for an outside-in approach (customer interactions drives marketing behavior). Bill clarified that the proposed architecture would be driven on a set of presumptions about how the scoring algorithm is built based on customer interests and actions.

Artefact 13: Illustration of how the lead score is derived from customer interactions.

When these visualizations had been discussed, David asked for clarification of which decision makers in the organization that had approved the solution. He specifically wanted to know how involved Eric and the International Sales Manager Tina had been. While it was evident that Eric had been part of the proposal formulation, the invisibility of Tina in the final phases became rather noticeable. However, Hillary clarified that Tina had agreed to the initial concepts and that she was not concerned with the architecture design itself.

David elucidated the importance of having all the relevant actors involved with the project design and stated that he would give no approval before everyone involved had agreed on the proposed architecture. He argued that this would allow for the final solution to be better integrated within the organizational procedures and anchored in the organization itself.

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5. ANALYSIS I: FIELD STUDY As David recognized that this was a solution they were all comfortable with, he confronted CB about the first design architecture made by the Web Bureau. He wanted to know what had changed, since the budget was significantly lower now. Bill explained how DB looked at the challenges differently and how their competences and prior experiences differentiated significantly. Concludingly, Hillary asked for David’s opinion regarding the budget, which was partly approved. A final approval would need the proposal to be refined slightly and afterwards presented to the Board of Direction.

Although it would have been interesting to follow this project through the development and integration of the solution, I believe the interactions and events unfolded on these pages provide enough empirical evidence to allow me to explore some of the questions that address data conceptualizations and framings in particular.

5.7.1. ACCOUNTING FOR THE CHOSEN SOLUTION; IN DISCUSSION This proposal towards the Vice President provided a comprehensive description of the decisions and considerations of the proposed solution. To translate the project insights and decisions towards the Vice President, the framings from earlier meetings were used.

This showed how the provided framings had been embedded into the conceptualization of the project. To account for chosen decisions, it was found useful to base arguments on these framings, since actors had already found themselves comfortably using these.

To describe the objectives of the Lead Generation project, two new artefacts were used to suggest the visual representation of the lead scoring dashboard. Both visualizations allowed the actors to translate a complex concept into a more comprehensible rendition; a representation of how the future solution would look like for the end-users. With Artefact 13, it was illustrated how the lead score had been derived from the data itself – with visual guides that helped actors to decode the messages. This visual translation gave an impression of a final solution, even though the development had not yet begun.

At this meeting the power dynamics between the actors became evident to me. Hillary presented the insights and decisions from the project towards the Vice President, who responded with questions and concerns about data representativeness and how the solution would look for the end users. Ann and Bill mainly assisted Hillary with elaborated explanations about the architecture and the process that lead to the final proposal. Figure 3 provides a Document Portrait of the contextual interactions throughout the meeting (see 4.3.4. Representation of Empirical Evidence, page 19). Here, I present a simplistic overview of how the meeting begins with a focus on sharing knowledge, while it quickly escalates into opinion-based feedback and questions. Although this visual emphasizes

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5. ANALYSIS I: FIELD STUDY the complexity of meeting structures, it is also helpful for illustrating the effectiveness of translations for efficient meetings. I found that each question-focused segment would be followed by a translative effect, either using the visuals on the presentation slides or through hypothetical examples (narratives).

Figure 3: Document Portrait of actor interactions (coded text segments) from the meeting taking place on March 18, 2016.

5.8. SUB CONCLUSION While this study invites the reader into a world of many interesting cultural and organizational challenges and procedures, I have sought to limit my analytical focus towards the conceptualization and valuation of data. This has allowed me to tell the story of how customer insights were transformed into a new business proposition; a lead scoring process. Through a chronological description of determining empirical moments, I was

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5. ANALYSIS I: FIELD STUDY able to illustrate how data entities of customer behavior were brought to life using particular framings and translations. I conclude this chapter by outlining how these concepts are made visible through the empirical evidence and how they complement each other.

Based on insightful learnings about current customers, GB wanted to embrace customer knowledge and generate similar insights on future customers (leads) on a coherent and frequent scale. The same framework and definitions from the initial analysis were embedded into a new Lead Generation project and different positions were investigated to account for the data management architecture. The process and purpose of the project were greatly discussed among actors from Marketing, Sales, Customer Relations and IT as well as consultants, and an agreement was reached about which user interactions that should constitute the lead score and how this data should be weighted and represented for the end-users (Sales and Marketing). The final data management architecture, budget and process proposal was described and elaborated for the Vice President of the organization and was initially approved, although a final decision on the future of the project could only be reached by the Board of Directors.

Framings were identified through this study as distinctions of data, data processes and data management architectures. In all these cases, framings allowed actors to conceptualize how a process should be addressed. It was, however, determined that in order to adapt to a new framing, actors were required to develop a shared understanding of underlying definitions and intentions. To account for chosen decisions, it was found useful to base arguments on pre-developed framings (such as an internally anchored Sales Funnel), since actors had already found themselves comfortably using these. This would allow actors to reflect upon current practices and relate it to new framings. In one case, a particular framing provided no significant assistance to the outcome of the process. While wireframes are useful in other processes, the lack of a clear verification of meeting objectives (i.e. establishing a list of relevant user interactions) could result in an unclear decision making process. It is thus important for actors to identify a framing format that fits the desired outcome. In two cases, framings were developed by actors at the meetings. Contrary to the polished models and designs represented on presentation slides, drawings that were developed at meetings illustrated a conceptualization that could be modelled and reshaped by the present actors. This format allowed actors from different domains to develop a shared responsibility of the outcome, to interact with the conceptualization and to relate to later iterations of the framing. When framings had been established as the main description of a concept, actors would often adapt to these either directly or indirectly. This was evident in several occasions; including the definition of leads, the categorizations of lead scoring variables and the development of data management architectures. Although these framing embodied a certain set of principles

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5. ANALYSIS I: FIELD STUDY and structural components, often defined by CB, there was no evident trace of the origins of the framings. This illustrates a concern of how framings and definitions might be seen as internally anchored, which I elaborate further on in 6.2. Framings, page 52. Another relevant concern is how categorized entities will emphasize certain properties, while other characteristics are concealed. This implies that a subject might be ‘forced’ into a particular category, which can influence how this subject is valued and defined in later processes.

Once a subject is placed within one category, I expect it will be difficult to re-categorize it.

For actors to relate to or adapt their conceptualization towards a framing, certain translative effects were identified. Translations was seen as both visual and narrative aids that allowed actors to enhance a shared understanding; to translate visionary ideas into comprehensible operations. This was particularly apparent when hypothetical user interactions were translated into valuable customer behavior. Here, actors used narrative effects to reach a shared conceptual understanding of what user interactions to weight in the lead scoring process. An important notion, though, is how the narratives are mainly based on assumptions about user behavior. If the narratives are not reflecting actual user intentions, the stories that are used to account for value might be ungrounded.

While some visual aids would mainly serve as framings, others provided actors with a translative impression of a proposal or even the outcome of a concept. How a visual aid was used was demanded by the conversation, and knowledge sharing was often initiated by framings, while perceptions and questions often required actors to interact with the visualizations or respond through narration effects.

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5. ANALYSIS I: FIELD STUDY

ANALYSIS II: EXPLORING FINDINGS

IN THIS CHAPTER THE EMPIRICAL EVIDENCE IS DISSECTED WITH A FOCUS OF HOW FRAMINGS AND TRANSLATIONS ARE USED TO ACCOUNT FOR THE GENERATION OF VALUE.

6. ANALYSIS II: EXPLORING FINDINGS

6. ANALYSIS II: EXPLORING FINDINGS The empirical evidence described on the previous pages provides an insight to different traces of data conceptualization. Through a further reduction of these events, I continue an exploration of how data are conceptualized and added value in an organizational context. In order to contribute to a discussion of the research questions provided at the beginning of this paper, I invite the reader to a discussion about data valuation and the two related concepts: framings and translations. As I seek to develop generative concepts in this paper, I extend these arguments with related literature that allows me to highlight certain considerations of my claims. A broader discussion of the relevance of these claims follows in the next chapter.

6.1. DATA VALUATION What is value? How is value constructed? When is value generated from knowledge and how is this being framed by organizational members? Value is a central part of the empirical tale explored in this paper. To understand how value is generated in the empirical evidence, I initiate this section by engaging with the emerging field of Valuation Studies and follow with a reflection on valuation in the organizational context.

6.1.1. WHAT IS VALUE? Valuation is grounded in decisions; choices about relevance and order. The core concept of valuation is grounded in the process of attaching economic value to an object, although the multiplicity of the concept suggests that it can reflect non-monetary registers (e.g. moral and ethical) as well (Kjellberg et al., 2013, p. 19). As suggested by Patrik Aspers, valuation can be seen as the process of bringing order to the ‘differences’ we encounter (Kjellberg et al., 2013, p. 17). Valuation are thus providing objects with a structure of how to see and order events. Vatin (2013) emphasizes this effect, by stating how “you have to evaluate in order to valorize” (p. 35). This translates to a relevant notion of the process of valuation: actors are actively engaging with decisions about the value of objects by evaluating criteria of the object. There is no societal standard of how to estimate value, and as Emmanuel Didier notices, the ascription of value is a dynamic process that is bound to change (Kjellberg et al., 2013, p. 20). The process of ascribing and changing the value of something is to a large degree a black box, given by the multiplicity of valuation criteria (Kjellberg et al., 2013, p. 19). By opening this black box, scholars are able to make “the social practices of valuation discussable and, possibly, thereby also accountable” (Doganova et al., 2014, p. 87). The procedural facets of valuation and the quality of the outcome they construct raise concerns among scholars such as Steve Woolgar and Jan Mouritsen, who specifically ask us to consider what these effects make us do (Kjellberg et al., 2013, p. 21). This raises new challenges, such as: how are valuations shaping

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6. ANALYSIS II: EXPLORING FINDINGS the way actors see and (re)frame something towards others?; are decisions involving causal processes where given valuations would lead to other valuations or require other valuations to be present; and who or what is accountable for the valuation choices that guides the decision making processes (Kjellberg et al., 2013, pp. 22-23)? I will not be able to provide a definitive answer to these questions, but in my exploration of the empirical evidence, I seek to identify organizational tendencies.

6.1.2. VALUE IN AN ORGANIZATIONAL CONTEXT The empirical tale of the Lead Generation project reveals many levels of valuations. In this section, I limit my focus towards the overall decision of ascribing value to the development of a lead scoring algorithm. CB initiated the project by illustrating how the organization could benefit from knowing more about their customers; a vision of a proposed future.

To cope with this vision, different perspectives of value emerged among the actors who became involved with the Lead Generation project. At the workshop in 2015, actors were shown a transactional value in determining profitable segments from their current customer database. When other departments became involved, the value of lead scoring was framed as a tool for allowing employees from Sales and Marketing to improve their work practices. And when the Vice President was asked to approve of the lead scoring architecture, Hillary and Bill ascribed the tool value by addressing the many different data sources that were gathered. This illustrates how value is not a static assessment, as actors will relate it to their own perceptions of reality through a contemporary view.

The three perspectives of the project all reflected a shared end goal by the actors, but by framing value differently, the project was found relevant for each actor. Additionally, all three aspects of value are directly related to the assimilated value: gathered and connected knowledge about customers allows for organizational efficiency that equals transactional value. In this context, value was viewed differently by the actors involved in order for them to see how the project would be useful for them. This multiplicity of valuation was reflected into the project itself and allowed actors to develop a shared goal.

With regard to the lead scoring algorithm, which processes can be seen as influential in the scoring of users? Who and what is accountable for the valuation choices that has guided the decision making processes? While I acknowledge that my perspective is limited and the system has yet to be developed, I can identify some of the valuation choices that has guided the decision making processes. The preliminary analysis provided a contemporary view of GB’s customer base and the data entities selected in this phase shaped what aspects were considered relevant in the early stage of the lead scoring algorithm. The website inputs of the lead scoring system were at a later stage selected by department managers based on estimations of how customer interactions could translate into a proposed sale. In the end these decisions will be translated into an

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6. ANALYSIS II: EXPLORING FINDINGS algorithm by the Data Analyst. While only some of the decisions that has influenced the valuation process are addressed here, it illustrates the complexity. And when the final lead scoring tool is made available for the employees from Sales and Marketing, these actors will have to translate the lead scores into insights that will define how they interact with new customers. Consequently it is crucial to understand that the algorithms that determine how a user is valuated are based on insights from a certain perspective. If there are any flaws or changes with this perspective, the algorithms will need to be updated.

But how will actors be able to reveal when such changes appear? Will valuation criteria only change when goals are not met?

6.2. FRAMINGS Through the analysis of how consumer data are conceptualized, valuated and utilized in an organizational context, I have been able to identify how framings are used to aid the decision making processes. As I elaborate on these claims, I extend my arguments through perspectives of other scholars from the social sciences.

6.2.1. WHAT ARE FRAMINGS? Framings as a concept provides no clear definition among social scientists (Entman, Matthes, & Pellicano, 2009; Scheufele, 1999). While some describe the concept in terms of the medium itself (media framings), others are more concerned with how framings are received by the audience (individual framings). Thus, Gamson & Modigliani defines media framings as a means for organizing an idea or a storyline to “an unfolding strip of events” (1994, p. 376), while Entman argued that frames are used to define problems, interpret causes, make moral judgments and support remedies (1993, p. 52). Actors on the receiving end of a framing process information based on a “mentally stored cluster of ideas” such as categories, scripts or stereotypes (1993, p. 53). As clarified by Scheufele (1999), other scholars have related framings to other concepts such as agenda settings or primings. While I acknowledge the existence of these many different conceptualizations of framings, I cannot relate my empirical evidence to all of these notions. This would obliterate the claims I seek to make, while a more comprehensive literature review would be required.

Framings can be seen in different environments and within different social domains. While I recognize this, I note that my empirical evidence are based on framings in an organizational context. This influences how framings are defined and conceptualized throughout this paper, which suggest that a delimited focus of framings in this study is applicable. How framings are defined in this paper, is thus founded in an organizational context from one single empirical study. Here, framings are mainly portrayed as particular artefacts such as distinctions of data, data processes and data management architectures. These

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6. ANALYSIS II: EXPLORING FINDINGS portrayals all rely on a visual component, which delimits my research to a certain form of framing (although other forms of framings are indirectly present, e.g. the meeting formats and setups). It is thus relevant to note that my conceptualizations do not directly relate to framings based on verbal or textual components, although some claims might be applicable to these matters as well. With these experiences as my empirical base for defining framings, I can relate to Entman’s notion of framings as schemas for interpreting events: To frame is to select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described. (Entman, 1993, p. 52).

Framings are thus embodying a perception of how to see something by emphasizing certain characteristics of a concept, while omitting others. While framings as a concept are derived from the allegory of framed paintings, I extend Entman’s description with a phrase by Mary Price: “[i]t is the act of describing that enables the act of seeing” (1997, p. 6). Although Price was describing photographs, this notion suggests how framings can embody a certain position by the actors’ that are utilizing them. Charles Goodwin (1994) expands this notion by arguing that “the ability to see a meaningful event is not a transparent, psychological process but instead a socially situated activity accomplished through the deployment of a range of historically constituted discursive practices” (p. 606).

Grounded in these claims, I argue that framings construct structures and a set of criteria of how to see and understand something. Framings are constructed representations of how actors use prior conceptualizations to organize information and shape perceptions.

6.2.2. DEVELOPING FRAMINGS Framings are developed as a response to actors’ need to make sense of and conceptualize events. In the empirical evidence, framings were developed both before, during and after meetings. Some embodied principles from other frameworks (e.g. from the field of marketing), while others had no particular external inspiration. When architecture designs were developed during meetings, they were seen by the audience as helpful simplifications that enabled them to visualize the process and focus on the fundamental aspects of the decisions they were facing. By developing the architectural design during the meetings, all actors were able to integrate their perspectives with the overall goals, which resulted in a shared understanding, rather than different individual conceptualizations. While I will describe the translation effects this provides in 6.3. Translations, page 56, I find it interesting how both bureaus used similar methods for involving the organizational

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6. ANALYSIS II: EXPLORING FINDINGS members in their framing design. Their uncertainty of the project scope might explain the main reason for this, but I believe there is one other notion that is worth addressing. The enactors of the framing knew that they needed to tailor their proposal to fit the needs of GB. By basing their conceptualization on insights provided during the meetings, DB and WB ensured that the framing was developed collectively. By allowing all actors to engage in the development of a framing, an efficient dialogue of the subject can better be reached. As Burgin noted, actors actively engage in the work of decoding matters and will thus project their own conceptualization onto the subject (Clarke, 2005, p. 209). Both the actors who develops the framings and the audience who interact with them are thus shaping the decision making process. Grounded in the empirical evidence along with this notion, I argue that this form of collective development by different conceptual worlds can reduce the translative barriers among actors.

6.2.3. ADAPTATION OF FRAMES Entman (1993) argued that framings determine how the audience notice, respond to and understand subjects, though the effect should not be expected to be recognized similarly by all actors (p. 54). Framings are thus utilized to provide a system for how to see something. This was particularly evident during the Lead Generation Workshop, where actors were given a framework for extracting data entities from their customer database (see 5.3.1. Defining Lead Generation, page 25). The framing of data as being behavioral, action-based or of core value, enforced a structure where actors had to conceptualize and describe how they found a data entity valuable. Actors were thus encouraged to see and understand events from a particular point of view, which allowed the content of the discussion, rather than the format, to be in focus. When a similar process of data selection took place half a year later, a particular framing (wireframes) provided no significant assistance to the outcome of the process. Actors were able to develop a list of data entities, but the framing chosen for providing this structure did not fit the actors’ conceptual level. The lack of a framing that could provide actors with a clear verification of the meeting objectives required actors to establish their own conceptualizations of what should be regarded relevant. In this case, I found that it was difficult for actors to establish a unified base for how the project goals should be approached. I argue that without a shared framing, a variety of different conceptualizations will emerge, which can result in unclarity about the project scope.

6.2.4. PERCEIVED FORCE OF FRAMINGS By utilizing a specific framing for defining and ordering data, actors are embracing a structure that will influence how knowledge is produced and valuated. In the empirical evidence this effect was evident when a distinction of how data should be categorized was addressed. Here, actors were obliged to decide which aspects of a data entity to

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6. ANALYSIS II: EXPLORING FINDINGS emphasize in order to place the entity within a certain category. By representing data as either Core data, Interactional data or Trigger data, actors’ conceptualizations were thus guided into one particular setting. “[H]ow people think about an issue is influenced by the accessibility of frames” (p. 116), Scheufele argued. Actors’ conceptualization of a concept is thus influenced by how the framing of data entities emphasizes specific values and facts. This provides a notion of how framings can be seen as devices for demanding a certain structure. Additionally, I find the use of framings to provide different levels of force throughout my research. When actors were facing a complex process of developing an architecture for data management, framings were developed to suggest a certain perspective. The suggestive aspects of this structure were in particular visible in how it was developed as drawings within the meeting (the format) and how actors were able to interact with and question the framing. This suggests that the level of procedural force that a framing is given is shaped by how they are represented towards the audience and how actors use the particular framings. To illustrate how the level of force can be seen in relation to framing events throughout this study, I have developed Figure 4. This model provides an overview of my personal conceptualization of perceived force of framings from the empirical evidence. The events are positioned in Figure 4 based on how they were visually embedded into the meeting (e.g. as a polished figure or a sketched drawing) and how they were verbally presented (e.g. as a guide for how something could be understood or as a solid definition of a concept). By highlighting the level of perceived force a framing is embedded with into practices, the structure provided by the framings becomes more visible, which can help determine both the influence of the audience and the enactors’ level of capability. The development of the Data Architecture illustrates these aspects, as I regard the initial drawings to be mostly suggestive (where DB demonstrated more procedural capability), while the Data Architecture from the meeting with the Vice President was presented more as a final solution (demanding).

Figure 4: Artefacts from the empirical evidence, organized in terms of perceived force.

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6. ANALYSIS II: EXPLORING FINDINGS 6.2.5. CONCERNS Although I have not directly encountered any discomfort about how framings construct the order of the conversation, it is worth addressing some of the implications that the adaptation of framings can cause. Organizations are constantly faced with new realities: new employees and partners, new challenges and new opportunities. As framings are revealed to have a structural effect on conversations (to a varying degree of force), I find it of great importance that framing enactors ensure that the framings reflects the organizational realities and are held accountable for their choice of framings. To do so, actors need to understand the original grounds for why a particular framing was initially chosen. This can ensure that the context of the framings are kept contemporary, while it allows actors better rationales for engaging with decisions by introducing insights to the original conceptualizations. Additionally, by highlighting the enactment of framings, actors are able to debate and refine how the particular framing should be adapted to their organizational needs. By embracing the influence of framings, actors are given the opportunity to think reflectively about how these framings are constructing the world from a certain perspective.

6.3. TRANSLATIONS Through the empirical evidence, I encountered several patterns of how data was described and used in conversations. When the architecture of data management was discussed, data was mostly regarded as something arbitrary and implicit. As an outsider it could be unclear when data was equal to customers in these cases and at meetings it became apparent that different conceptualizations of data usage existed. When categories and practical usage was discussed, data was mostly defined using visual artefacts and hypothetical examples (narratives). These aspects reveal that several translations occur when data are conceptualized. How and when are these translations used as enablers of the conversation? Are we able to identify patterns of artefact and narrative usages in relation to these translations? In this section, I elaborate on how translation devices and methods has aided the actors’ conceptualizations in the study. For this purpose, I add notions by other scholars who are working directly or indirectly with translations.

6.3.1. WHAT ARE TRANSLATIONS? According to The Oxford Companion to the English Language, translations are “communication of the meaning of a source-language text by means of an equivalent target-language text” (Bhatia, 1992, as cited by Haque, 2012, p. 98). This illustrates translations as something that takes place between two languages. While this is typically reflected in the domain of linguistics, translations are also seen in other domains. A common metaphor of translation is how it serve as a bridge between languages, cultures and nations (Allen & Bernofsky, 2013, p. 49). Grounded in these claims as well as the

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6. ANALYSIS II: EXPLORING FINDINGS etymology of the word, translations can thus be seen both as something that takes place ‘in-between’ subjects and as a bridge between two conceptualizations. Translations are found within many different theoretical domains of the social sciences. Within anthropology, ‘translation’ (or cultural translation) is used to conceptualize a version of one culture to make it comprehensible to readers living in another. Ethnographers use textualization or narration when translating experiences into text (Emerson, 1995, p. 8). Latour (1994) provides a more generative notion, arguing that translations are means by which we inscribe features of our social order (p. 46). I believe all of these descriptions are helpful for revealing different realities of how translations can be seen; as well as how I relate to the concept myself.

6.3.2. ENACTING CONVERSATIONS / THE EFFECTS OF TRANSLATION Just as framings are needed to provide a structure of the things we seek to order, translations are essential for contributing to the efficiency of conversations, where actors can embrace and unfold their different perceptions. I find that translations provides a better understanding between actors of different backgrounds and perceptions. This was in particular evident, when Hillary tried to conceptualize how the final dashboard visuals would look, while the remaining actors had a focus on the integrations and the overall architecture. In order for Hillary to see the value in their discussion, it was important that she was able to relate the architectural design with the end result. Allowing Hillary, with a less technical comprehension, to understand and give feedback on these matters, she introduced valid insights on what the data architecture should and should not incorporate. This illustrated how two different conceptual worlds collaborated in order to reach a common goal. Star & Griesemer (1989) described how this effect can create boundary objects; a concept of how actors from different groups cope with concepts from intersecting social worlds and translate it into their own conceptualizations (p.

393). Boundary objects can act as anchors or bridges between social worlds and are often constructed when actors from different worlds produce representations (Star & Griesemer, 1989, pp. 413-414). The architectural drawings are distinct examples of how a complicated issue of data management practices is translated into a concept that satisfies the conceptualization of actors with less technical insights. While this example illustrates the use of boundary objects, it also indicates how translations can both ease understanding and improve how decisions are made. In the empirical evidence, I found translations to be manifested either verbally or visually. I devote the following two sections to these particular translation devices.

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6. ANALYSIS II: EXPLORING FINDINGS 6.3.3. USING NARRATIVES AS TRANSLATION DEVICES To account for certain positions and ideas, the empirical evidence presented many verbal instances of actors using hypothetical examples of how they believed customers to behave or how they believed events could unfold – this is a translation device often referred to as narratives. This narrative effect is rooted in our culture and has always been part of how humans have coped with information (Gershon & Page, 2001, p. 37). Why is this effect useful? As argued by Gershon & Page (2001), “[a] well-told story conveys great quantities of information in relatively few words in a format that is easily assimilated by the listener or viewer” (p. 31). Narratives can thus allow complex situations to be described in a format that allows more actors to comprehend them. While narratives can be found in all social domains, the empirical evidence of this study reveals a certain usage within the organizational context. Before I elaborate on this notion, I will direct the attention towards two different, yet similar, cases of translations: 1) when actors were providing suggestions of lead scoring variables, they addressed each entity in relation to hypothetical examples; 2) when the Vice President of GB asked for clarification on how the lead scoring dashboard would be utilized by the end-users. Both of these examples illustrate how narratives are used during meetings to account for the rationale of decisions. In the first example, actors are rationalizing the decision of each element through hypothetical stories, while the second example shows how the Vice President uses a narrative approach to question the proposed end-result. The narrative can thus allow actors to add contextual insights to abstract and complex events.

6.3.4. USING VISUALS AS TRANSLATION DEVICES When words are not offering enough variations for explaining a concept or position, people tend to use forms of visual representations. While this notion can be reflected as specific framings, it is also useful as translative devices; as enactors of conversations.

Visual artefacts are useful for transforming information and complex knowledge into a form that “relies on the human visual system to perceive its embedded information” (Gershon & Page, 2001, pp. 32-33) as it enable actors to translate information into their own conceptual world. Goodwin (1994) extends this notion by arguing that external representations can complement the spoken language “by collecting records of a range of disparate events onto a single visible surface” (Goodwin, p. 611). These artefacts can thus incorporate the perspectives and conceptualizations of multiple social worlds. In the empirical evidence, the use of visual artefacts illustrates the effect of these claims.

While framings are used to incorporate specific structures of how matters are addressed, the influence of translations cannot be understated here. For actors to gain confidence in a provided framing structure, I argue that they need translations to be able to understand the framing from their own perspectives. This is distinctly seen when data architectures

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6. ANALYSIS II: EXPLORING FINDINGS are drawn to conceptualize the complexity of using data to generate a lead scoring that should be visually available through a dashboard. Translations can also be valuable, when actors need to introduce or clarify concepts, framings and earlier decisions.

In the organizational contexts of GB, I found that visuals often sought to provide a conceptualization of actors’ perceptions (such as a framework). The visuals manifested themselves as both immediate sketches on sticky notes and whiteboards as well as wellconsidered models and representations in PowerPoint presentations. Visualizations in these cases had either the purpose of presenting or clarifying a conceptualization. I will expand on these distinctions through a few examples from the empirical evidence, as I elaborate further on this argument.

Most meetings are initiated or driven by PowerPoint presentations. While these presentations are thoroughly prepared to provide the attendees with a guidance (or framing) of the conversation, they also serve as the presenters preliminary conceptualization of the matters at hand. These visual artefacts are used throughout meetings as translation devices among the attendees. An example of this is how lead generation was approached using both GB’s internal model of a Sales Funnel in comparison to similar lead funnels.

Similarly, the models from the PowerPoint slides were used among attendees to clarify their understanding of something (e.g. when the Vice President asked: “Is this what we need to build?”). Actors would also occasionally interact with the artefacts by asking questions, such as: “could we just cut that red part off?” when discussing a proposal of the data management architecture.

If the artefacts provided at the meetings did not fit with actors’ initial conceptualizations, drawings were developed to articulate their understandings. This allowed actors to interact with the conceptualization and clarify or even improve framings before they were further developed. If a model like this was found useful, it would become part of later conceptualizations or frameworks – both architectural drawings were for instance based on thoughts shared at these meetings.

6.3.5. CONCERNS Translations involves both concerns about perception and persuasion. Translations are reflecting the perceptive and persuasive means of its enactors and the recipient actors’ (or the audience’s) needs to conceptualize these translations in order to make decisions. With actors from multiple social worlds, this process will eventually result in translations that do not embody all (relevant) knowledge from original materials. While this is inevitable, actors should notice how the different translative effects of narratives, gestures and artefacts are used to justify the decision making processes. Star & Griesemer (1989) add an important

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6. ANALYSIS II: EXPLORING FINDINGS notion of how representations built by multiple memberships will contain traces of “multiple viewpoints, translations and incomplete battles� (pp. 412-413). Actors should thus notice how such representations embody multiple concerns and perspectives.

6.4. VALUATION AS DETERMINED BY FRAMINGS AND TRANSLATIONS The data management practices of GB revealed a distinct relationship between framings and translations. I found framings to be applied by actors in order to conceptualize project goals, while translations were used to develop a bridge between different conceptual worlds. The multiplicity of valuation perspectives, illustrated how value could be seen different by the actors involved, which were reflected into the project goals. To illustrate this relationship, I have developed a visual component (or a framing) of my own conceptualization, Figure 5.

Figure 5: The project goal is developed by different conceptual worlds through translations. This illustration represents three of these worlds from the empirical study.

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6. ANALYSIS II: EXPLORING FINDINGS This figure demonstrates how different conceptual worlds (e.g. Business, Marketing, Technical and Sales) can reflect their project vision or perspective into a shared goal through translative devices. The goal will reflect compromises between the actors involved, but will eventually become a shared agreement (which could be reflected as a boundary object or a conceptual framing). By translating perspectives and visions into a shared understanding in the early stages of a project, I expect actors to be able to focus more clearly on how to reach the goals, rather than how their own needs are fulfilled.

This figure can also be used to illustrate the effect of how actors will gain ownership of a project. By ensuring translations are facilitated between the project goal and actors from other conceptual worlds, I expect actors to consider themselves involved with the foundation and outcome of the project. This form of actor involvement is an essential part of anchoring projects into organizational practices (Bolman & Deal, 2008, pp. 378-379).

Although this model is based on reflective thinking of current organizational practices, I expect it to be useful in managing other projects as well. Figure 6 represents a more generalizable version of such model. I suggest that project managers could use this in the initial stages of a project to ensure that all conceptualizations of the project are integrated into the scope of the project. Additionally, it would be a useful notion to address, when new actors are becoming involved with the project.

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6. ANALYSIS II: EXPLORING FINDINGS

Figure 6: A model of how different conceptual worlds can translate their views into a shared vision or goal.

6.5. SUB CONCLUSION A curiosity about data conceptualization and valuation initiated this story, and it engaged me to question the influence of framings and translations in the organizational processes.

I will now assemble the main arguments about how value, framings and translations were used to provide structure, a shared understanding and to aid the decision making processes.

Valuation is grounded in choices about relevance and order. The multiplicity of valuation criteria and perspectives are reflected into the valued subjects, and the process is to a large degree regarded a black box. The value of the Lead Generation project was seen through at least three perspectives: gathered and connected knowledge about customers,

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6. ANALYSIS II: EXPLORING FINDINGS organizational efficiency and the profitability of the outcome. All of these aspects of value had a direct link to the assimilated value of the project, which allowed the actors involved to project their own views into the project. This multiplicity of valuation perspectives can be utilized by organizational members to ensure that a shared vision of the final outcome is reached.

Framings provide structures and a set of criteria of how to see and understand something by emphasizing certain characteristics of a concept, while omitting others. In this paper, framings are mainly portrayed as visual artefacts such as distinctions of data, data processes and data management architectures. Framings are developed as a response to actors’ need to make sense of and conceptualize events – this can both be an individual and a collective process. It is argued that a collective development by different conceptual worlds can reduce the translative barriers among actors and ensure clarity about the project scope. Framings were identified to have different procedural goals, which related to their perceived force. Suggestive framings were thus used when particular goals had not been clarified, while a clearer procedural outcome would require more demanding framings. It was recognized that the perceived force of framings was shaped by how actors use the particular framings and how they were represented towards the audience.

By illustrating the perceived force of framings, it was possible to indicate the influence on the audience and the framing enactors’ level of capability. As organizations are constantly facing new realities and framings provide a structural effects on conversations and decision making processes, it was found important for the enactors of particular framings to account for their choice of framings.

In the empirical evidence, translations were identified as significant means for developing a bridge between conceptual worlds – a shared vision of value. As argued by Star & Griesemer (1989), when groups from intersecting social worlds developed shared conceptualizations, they would create boundary objects as means for translating their different perspectives into a shared vision. Two aids of translations are especially evident in my field notes: the use of visuals and narratives. The latter was most clearly seen through hypothetical examples of how actors believed customer interactions would translate into their conceptualization of value. Narratives were used to add contextual insights to abstract and complex events. When words were not sufficient for such conceptualizations, visuals artefacts were found useful for embedding information into a visual format. In the empirical evidence, translations were used to build bridges between actors of different social worlds by either introducing or clarifying concepts, framings and decisions.

The data management practices of GB revealed a distinct relationship between framings and translations, where framings were applied to conceptualize project goals, and

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6. ANALYSIS II: EXPLORING FINDINGS translations were used to develop a bridge between different conceptual worlds. The multiplicity of valuation perspectives, illustrated how value could be seen different by actors involved, while the recognition of these different conceptualizations allowed actors to develop a shared understanding of the project goals and aid the decision making processes. A model of these effects was proposed, which was suggested to be a useful tool for project managers to ensure a shared vision on the project scope.

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6. ANALYSIS II: EXPLORING FINDINGS

DISCUSSION

THIS CHAPTER ACCOUNTS FOR MY CONTRIBUTION TO THE FIELD AND PROVIDES A CRITICAL REFLECTION ON THE EMPIRICAL EVIDENCE.

7. DISCUSSION

7. DISCUSSION This paper has explored some of the influences of framings and translations in data management practices of one particular project. Although I relate my insights to relevant literature, all claims are founded in one empirical account. In this chapter, I will reflect critically on my contribution through a discussion of my claims, the empirical evidence and ethical concerns.

7.1. CONTRIBUTION The impact of value in organizational practices has not been of much concern within the field of social science. However, questions about valuation is a growing interest in society, according to scholars from the (relatively new) field of Validation Studies (Kjellberg et al., 2013, p. 13). While framings and translations are both concepts that has been studied at length (e.g. Entman, 1993; Star & Griesemer, 1989), the interrelations between these concepts in relation to value generation, does not seem to be a matter addressed specifically by other scholars. As organizations are relying increasingly more on datadriven decisions, this field requires more research.

My contribution to the field is limited to a particular study, but through an exploration of the empirical evidence, I introduce two emerging hypotheses: one of procedural force regarding framings and one of conceptual involvement in the development of a shared project goal.

Based on how framings were utilized and represented by enactors, a perceived force of framings were recognized. This helped in determining the influence on the audience and the enactors’ level of capability. The perceived force of framings was represented as a linear scale were a member from the audience could position a particular framing as being between suggestive and demanding framings. The latter were typically used when a conceptual goal had been determined, while suggestive framings were used when a procedural outcome had yet to be determined.

This study proposes a specific way for project managers to engage with projects, by describing how actors from different conceptual worlds need to translate their views and perspectives to the others in order to develop a shared project goal. By allowing all conceptual worlds to engage with the project goal, I argue that actors will become more involved in the foundation and outcome of the project – and thereby also the decision making processes.

I invite scholars to engage with these hypotheses: to question the intentions, to prove

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7. DISCUSSION or disprove them and to enhance them. My claims are limited to one study, and I would be intrigued to learn if the theoretical framings could be used in other context and within other domains.

7.2. EMPIRICAL EVIDENCE There was no predefined theoretical focus of this study as it was important for me to gather my information in a naturally occurring environment in order to reduce the influence of my own bias. This allowed me to observe events from the business setting during an ongoing project with all the messiness such process involved. A quantitative study, an interview or focus groups would have required me to conceptualize more aspects of the studied project beforehand – and thus shaped the project scope significantly. The research questions have not changed remarkably throughout this study, and I believe the empirical evidence have aided my research in developing claims about how actors conceptualize and represent data. There are, however, limitations with the data gathered.

As suggested by Glaser (1994), analysts should use theoretical sampling for collecting, coding and analyzing data, which can be utilized by using initial findings as a foundation for gathering new data. With only six meetings to develop my theoretical concerns, it was not possible for me to review my empirical focus during this study. Theoretical sampling from the same or new domains as well as triangulation of the empirical evidence from different perspectives could have improved the foundation of my arguments notably.

The study is based on events that I was able to access through my employment at CB. This is a relevant notion, since this have shaped my research design in several aspects. I have been able to gather insights (e.g. PowerPoint presentations) that would have been difficult for other researchers to access. I have also been introduced to particular viewpoints and internal discussions about organizational procedures, which could indirectly shape how I would later focus on these aspects through meetings (i.e., I was told about the large budget proposal by WB before the meeting where it was discussed). This can both be reflected in what aspects of a meeting I focus on and how actors and views were represented. Additionally, I found that I was sometimes considered as an employee from CB rather than a researcher, which could influence how actors behaved around me.

Occasionally, I experienced actors mentioning my presence as a researcher, e.g. through questions such as “Did you get that part?” and “Can you make that sound more clever?”.

As recognized by Emerson (1995), these reactive effects should be seen as a source of learning, rather than a contaminating effect (p. 2).

I intended my presence to be as non-influential as possible for the other actors. All notes were thus written on a tablet, rather than a computer, since I believed my presence behind a computer would have created a form of barrier between me and the other

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7. DISCUSSION actors in the environment. Based on the same intentions, I sat at the meeting table, next to the other actors, rather than positioning myself in the corner and act as a fly-onthe-wall observer. I was, however, reluctant to involve myself in the conversations of the meeting, as I relied on participant observation. As described in 4.2. Methodology, page 13, the empirical evidence was inscribed in several stages (including translations and codes), but through my analytical description of the events, I often looked at my original notes in order to clarify my conceptualization of the events. Although Glaser argues that researchers doing Grounded Theory should refrain from recording empirical data (Randall, Harper, & Rouncefield, 2007, p. 184), I made audio recordings of two meetings, which provided me with further clarifications of emerging theories. The use of MAXQDA allowed me to explore the empirical evidence through patterns and quantity, which aided me in identifying emerging concepts. Other digital methods such as Gephi were used in the analytical phase, but as they provided no significant new (relevant) insights, I refrained from presenting them in this paper.

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7. DISCUSSION

CONCLUSIONS & REFLECTIONS

AN OVERVIEW IS GIVEN OF HOW FRAMINGS AND TRANSLATIONS INFLUENCES THE ORGANIZATIONAL VALUE GENERATION PROCESS. THIS IS CONCLUDED BY A REFLECTION ON HOW FUTURE RESEARCH CAN ACCOMPANY THIS STUDY.

8. CONCLUSIONS

8. CONCLUSIONS This was a story about value; how value was shaped and defined by organizational members. But as the story unfolded, it also became a story about how data was framed and how translations were used to aid decision makings. Inspired by the explorative approach of Grounded Theory I did not seek out to approve or disprove any specific theoretical concern. This allowed me to maintain my initial curiosity while avoiding a particular theoretical focus during the inscription of events. Guided by an interest in how actors conceptualize and represent data, I studied data management practices during a Lead Generation project of the company named GB. This provided me with empirical evidence of some of the influences of framings and translations as particular discourses of how data was conceptualized among actors.

Framings were identified as specific means for providing structures through the decision making processes. This was a notion explored by other scholars, including Goodwin and Entman. Grounded in the empirical evidence, I extended this concept by providing a framework of how the influence of framings could become more visible by highlighting the perceived force the framings were embedded with. The perceived force of framings was represented as a linear scale were a member from the audience could position a particular framing as being between suggestive and demanding framings. In the empirical evidence, framings were typically perceived as mostly demanding when a conceptual goal had been determined, while suggestive framings were used when a procedural outcome had yet to be determined. This helped indicating the audience’s influence on the structure and the level of capability by the enactor of the framing. Framings direct the attention towards a specific structure of how a subject should be seen. While organizations are constantly faced with new realities (e.g. new actors, new challenges, new opportunities), I questioned how the enactors of particular framings ensure that the framings reflected the organizational realities.

In order for actors to reach a shared understanding of complicated issues, visualizations and narratives proved to be helpful translative devices. These effects were especially evident when actors sought to account for decisions about what data entities were selected and how these entities could be valuable for the organization. Star & Griesemer (1989) provided a notion on how translations could create boundary objects between conceptual worlds, which I recognized to be reflected in the empirical evidence of this paper as well.

The interrelations between framings and translations (in this paper) became visible, when it was discovered that decisions mainly were based on actors reaching a shared

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9. REFLECTIONS understanding of the project goal. Grounded in the empirical evidence and arguments by Star & Griesemer (1989), this study proposes a particular framework for project managers to engage with project members. It was suggested that actors from different conceptual worlds would need to translate their different views and perspectives into a shared goal, which would allow for more actor involvement and influence in the foundation and outcome of the project – and thereby also the decision making processes.

Valuation is a complex concept that is not only grounded in particular choices about relevance and order. This study proposes that valuation in an organizational context is determined by how different conceptual worlds engage with project goals through framings and translations. Framings can be utilized by actors to conceptualize project goals, while translations are used to develop a bridge between different conceptual worlds.

9. REFLECTIONS The story is not finished, but my account of it ends with this chapter. As Glaser & Strauss (2006) clarifies, the generation of theory is a never-ending process (p. 40). Through this study, I have been in a process of constant discovering and revising perspectives.

The empirical evidence has introduced many interesting insights and concerns about organizational practices, including cultural and behavioral. While these aspects could have been intriguing to pursue, these matters did not relate directly to the story I wanted to tell. By limiting my focus to explore the processes of giving data value and identifying the influences of framings and translations, I believe the story has served its purpose.

But could the story have been told differently? It could, indeed. I personally believe value is a subject that can be explored in depth in all other domains of the scientific studies. I would especially suggest to direct the attention towards the empirical evidence. A longer timeframe of the study could have provided greater insights to how conceptualizations of data and usages of framings changed in the organization. I am especially interested in how the actual lead scoring would be anchored within the organization of GB and how value would be generated and conceptualized among new actors. With my main empirical evidence founded in field notes from meetings, it would have been useful to expand my perspective by observing actors (such as the Data Analyst) during their work.

How is the selection and valuation process for instance managed by the Data Analyst?

How will the web manager translate the lead scoring variables into an action from the website? And which barriers will be revealed in the data management processes by DB? An ANT approach to these considerations might even be applicable.

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9. REFLECTIONS This thesis provides more questions than it answers, so I invite other scholars to expand on my findings. Value, framings and translation devices are concepts that I expect to be influential in many other domains and in many other constellations. How is value generated, framed and represented in other contexts, e.g. political and environmental?

How can my findings be related to similar organizational practices? And can the use of framings and translations be seen as reflections of organizational practices?

This study touched upon topics that calls for further research. In the following, I present a few of the most noteworthy. A lead scoring model was created based on how actors conceptualized and framed customer insights. This process could help scholars identify some of the relations that influences the development of algorithms. This process is highly black-boxed and I believe the formation of these can be related to how actors define value. As the lead scoring was based on preliminary insights of past knowledge, this opens up other concerns about data-driven projects in general. By only exploring and determining value based on known variables, the algorithms developed will inherit any discriminations that might be present in the source data (i.e., Winner, 1980). The social practices of valuation are still black-boxed and this study have only presented a few of the processes related. Social scientists (and in particular scholars interested in the field of Valuation Studies) might benefit from engaging with Marketing literature in order to explore how businesses ascribe and define value. I invite scholars to interact with the claims this study presents and explore new ways to tell the story of value.

As a final remark, I would like to remind the reader to maintain a critical mind of the claims of this paper. This thesis makes extensive use of the same concepts it describes. The story is thus represented through a particular academic framing where translative effects are used to account for the claims I seek to make. However, this can also illustrate the usefulness of narratives and visual components in sharing and developing knowledge.

Stories are a useful means for providing easy access to a complicated issue and the story of value was thus described in this format.

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10. LITERATURE

10. LITERATURE AUDIO RECORDINGS Lead Scoring. (2016). Denmark.

BOOKS Allen, E., & Bernofsky, S. (2013). In Translation: Translators on Their Work and What It Means. Columbia University Press.

Bolman, L. G., & Deal, T. E. (2008). Reframing organizations: artistry, choice, and leadership (4th ed). San Francisco: Jossey-Bass.

Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. Chicago: University of Chicago Press.

Clarke, A. (2005). Situational analysis: Grounded theory after the postmodern turn.

Sage.

Price, M. (1997). The Photograph: A Strange Confined Space. Stanford University Press.

Walters, D. (2015). Behavioral Marketing: Delivering Personalized Experiences At Scale. John Wiley & Sons.

BOOK SECTIONS Emerson, R. M. (1995). Fieldnotes in Ethnographic Research. In Writing ethnographic fieldnotes. University of Chicago Press.

JOURNAL ARTICLES Aral, S., Brynjolfsson, E., & Wu, D. J. (2006). Which came first, it or productivity?

Virtuous cycle of investment and use in enterprise systems. Virtuous Cycle of Investment and Use in Enterprise Systems.

Doganova, L., Giraudeau, M., Helgesson, C.-F., Kjellberg, H., Lee, F., Mallard, A., … Zuiderent-Jerak, T. (2014). Valuation Studies and the Critique of Valuation.

Valuation Studies, 2(2), 87–96. http://doi.org/10.3384/vs.2001-5992.142287 Entman, R. M. (1993). Framing: Toward Clarification of a Fractured Paradigm. Journal of Communication, 43(4), 51–58. http://doi.org/10.1111/j.1460-2466.1993. tb01304.x Entman, R. M., Matthes, J., & Pellicano, L. (2009). Nature, sources, and effects of news framing. The Handbook of Journalism Studies, 175–190.

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10. LITERATURE Arrojo, R. (1994). Fidelity and The Gendered Translation. TTR : traduction, terminologie, rédaction, 7(2), 147. http://doi.org/10.7202/037184ar Garrison, W. A., & Modigliani, A. (1994). The changing culture of affirmative action.

Equal Employment Opportunity: Labor Market Discrimination and Public Policy, 373.

Gershon, N., & Page, W. (2001). What storytelling can do for information visualization.

Communications of the ACM, 44(8), 31–37.

Goodwin, C. (1994). Professional vision. American Anthropologist, 96(3), 606–633.

Hansen, H. F. (2005). Choosing Evaluation Models: A Discussion on Evaluation Design.

Evaluation, 11(4), 447–462. http://doi.org/10.1177/1356389005060265 Haque, M. Z. (2012). Translating Literary Prose: Problems and Solutions. International Journal of English Linguistics, 2(6). http://doi.org/10.5539/ijel.v2n6p97 Kjellberg, H., Mallard, A., Arjaliès, D.-L., Aspers, P., Beljean, S., Bidet, A., … others.

(2013). Valuation studies? Our collective two cents. Retrieved from http://papers. ssrn.com/sol3/papers.cfm?abstract_id=2285491 Knoblauch, H. (2005). Focused Ethnography. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 6(3). Retrieved from http://www.qualitativeresearch.net/index.php/fqs/article/view/20 Olughuyi, A. O., & Okafor, V. C. (2015). Translation Bias: Impact of Gender in the English Translation of the Bible. International Journal of Culture and History (EJournal), 1(2), 91–94. http://doi.org/10.18178/ijch.2015.1.2.017 Scheufele, D. (1999). Framing as a theory of media effects. Journal of Communication, 49(1), 103–122. http://doi.org/10.1111/j.1460-2466.1999.tb02784.x Star, S. L., & Griesemer, J. R. (1989). Institutional Ecology, `Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-39. Social Studies of Science, 19(3), 387–420. http:// doi.org/10.1177/030631289019003001 Vatin, F. (2013). Valuation as Evaluating and Valorizing. Valuation Studies, 1(1), 31–50. http://doi.org/10.3384/vs.2001-5992.131131

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10. LITERATURE Winner, L. (1980). Do artifacts have politics? Daedalus, 121–136.

NEWSPAPER ARTICLES McElheran, K., & Brynjolfsson, E. (2016, February 3). The Rise of Data-Driven Decision Making Is Real but Uneven. Harvard Business Review. Retrieved from https:// hbr.org/2016/02/the-rise-of-data-driven-decision-making-is-real-but-uneven

PRESENTATIONS Workshop Reporting. (2015, December). Presented at the Lead Generation Workshop Reporting, Copenhagen, Denmark.

Lead generation Workshop. (2015, October). Presented at the Lead Generation Workshop, Copenhagen, Denmark.

Whittaker, Mike. 2015. ‘Global Funnel Management Workshop’. presented at the Global Funnel Management Workshop, London, June 18.

WEB PAGES Eurostat. (2015, November 30). Business economy - size class analysis - Statistics Explained. Retrieved May 12, 2016, from http://ec.europa.eu/eurostat/statisticsexplained/index.php/Business_economy_-_size_class_analysis Kierlanczyk, K. (2016, February 4). A Brief History of Market Research | Kelton Global | Research, Strategy, Brand + Design. Retrieved from http://keltonglobal.com/ blog-post/a-brief-history-of-market-research/ MAXQDA. (2014). Document Portrait: Visualizing a Document [Manual]. Retrieved May 20, 2016, from http://www.maxqda.com/support/help/maxqda-11/index. htm?turl=Documents%2Fcodingtextandimages.htm Vasquez, J. (2011, January 27). The History of Marketing Research. Retrieved May 12, 2016, from http://www.marketresearchworld.net/content/view/3754/49/

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APPENDICES

APPENDICES

APPENDICES APPENDIX A - PROJECT DESCRIPTION FOR PARTICIPANTS PURPOSE OF THE STUDY This is a research study on data management. I seek to investigate the process of giving data value. I want to understand how data becomes stories and how these stories are amplified and brought into a larger context. While businesses rely on these insights, it is essential to understand how actors are managing, shaping and analysing the data prior to delivering insights to decision makers. With this in mind, I seek to observe the interactions between the parties involved with data management, including company representatives, consultants and data analysts.

TIME FRAME I will be conducting empirical evidence in February and March of 2016. If you agree to participate in this study, I will need to be present at a minimum of 5 meetings.

DATA COLLECTION One aspect of this study involves recording some of the meetings. These will only be accessible by me and the involved supervisors from ITU and will never be shared with anyone else. In the paper all names and companies will be anonymised. At the request of participants, I will delete any recordings.

QUESTIONS If you have any questions regarding the study, please contact me on mmad@itu.dk.

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APPENDICES APPENDIX B - RESEARCH LOG Week

When

Topic

Description

Type

48

23/11/2015

New job

First day at new job at the Consultancy Bureau. Initial introduction to case access was presented.

Event

48

25/11/2015

Meetup w. Irina

Preliminary thoughts of a study based on case access (that I only knew very briefly). Suggested that I contacted Marisa

Supervision

49

04/12/2015

Meetup w. N from CB

Insights given to the current and prior data process of GB

Meetup

50

09/12/2015

Meetup w. Marisa

Marisa pointed out that I would need a project that was manageble. While I knew that my competences were not within data analysis (although I find it very interesting), I suggested that I would get empirical data by following a data analyst doing his work. Marisa liked this approach and suggested that I looked into the process of creating stories from data. With this in mind I showed her some of the powerpoint slides I had Supervision already been given by N. She asked me to put my thoughts into writing and to send her a 1-pager description of the thesis before January 6. Further, she suggested that I contacted Ingmar for a possible cosupervision position, as his insights into accountability could be valuable.

53

31/12/2015

Handin of 1-pager thesis description to Marissa and Ingmar

I delivered my preliminary thoughts on 'Tracing Data' and made a timeline of my suggested approach.

2

12/01/2016

Meetup w. Ingmar

Ingmar was very interested in my thesis topic and would gladly supervise me. He suggested that I would talk more with Marisa about the percentages of the supervision, as this would influence how the meetings would be structured, the exam (and their payrate). Further, he pointed me towards 'Grounded Theory' and Etnomethodology and suggested that I looked into the book 'Doing Qualitative Research' by David Silverman.

Supervision

3

21/01/2016

Meetup w. Marisa

I met with Marisa and talked percentages of the supervision. I received brief feedback on my 1-pager description and she agreed to review my notes for the Project Agreement before I would upload this to mit.itu.dk

Supervision

3

22/01/2016

Started reading 'Doing Qualitative Research'

by David Silverman

As a preparation for my qualitative research, I decided to read Silverman's book on the topic, which reminded me of important stept to have in mind prior to doing qualitative research. One of these aspects was to write a Research Log, which I started on this day. I had, however, made notes earlier - so I could easily write the main decisions in the log at this point.

Method

4

27/01/2016

Wrote an 'Informed consent' for study participants

After reading about ethical concerns in Silvermans book, I decided to develop a 'Informed consent' form for Writing study participants.

4

28/01/2016

Meeting w. R and N

R (data specialist) and I were formally introduced and R got a brief introduction to my research study.

Before we finished talking this through, A and C showed up.

4

28/01/2016

Preliminary GB2 meetup

A (GB2) wanted to get insights to how data management was handled by CB when analysing GB. While CB seek to use their knowledge in terms of data analytics and consultings, they want to help GB2 understand and use their data warehouse with a more strategical future oriented focus. A (apparently) seeks to do most Meetup of this himself, and was generally not interested in collaborating on such project. His interest at this meeting was primarily in the competences of R, who had done the data analytics on GB.

4

28/01/2016

Lunch meeting w. N

Informal meeting while eating lunch. N explained his disappointment with the meeting earlier - “I should have discouraged C even more to avoid this meeting!”, he expressed. I acknowledged that it seemed unrealistic that CB would be doing any data business cases for GB2 in the near future. We talked further about the case of GB and N told that they actually had finished most of their data analysis at this point.

While I expressed the need to gain understanding of the process, he said that he had actually thought about Meetup going towards “the next step” which would involve trying to understand one of the current identified clusters of customers within GB. With this in mind, he told me that he would setup a meeting next week with [someone] from GB.

5

01/02/2016

Met with students at the Ethos Lab.

I shared my study approach with fellow students and got contact details on a student doing qualitative research that might relate slighty to my study.

5

02/02/2016

Rewrote the "Informed Concent" paper to a Project description

Based on my meetings with N, I realised that I needed a less formal approach for getting approval to the research. Basically, he is very open to the project and has agreed to all aspects of the study verbally. So to Writing put it in writing needs to be more like a statement describing what we have agreed upon and what I will need throughout my research.

6

08/02/2016

Meeting w. N

N and I had different ideas about the goal of my project. He presented his view of how I could do a detailed cluster analysis for the client, while I told him how my focus mainly was on following a process (where it would be difficult for me to become my own empirical source). We discussed two new approaches, and he Meetup told me that he would contact me when he had established which of these would be realistic within the limited time span.

6

09/02/2016

Finished reading 'Doing Qualitative Research' While reading this book, I made a structure of my thesis with headlines and considered topics, questions

by David Silverman and ideas. After reading about the value of being able to trace my notes, I decided from this point of to make multiple versions of my notes: the initial notes from the meeting should be kept intact and an edited version Method would be a more elaborated 'readable' document. If a recording is being made, I will make this as a separate document as well.

6

11/02/2016

Briefly talk w. N

N told me that he had talked to D about a planned meeting w. GB. She should contact me very soon

6

12/02/2016

Meetup - Lead generation

Received a text 8:15 in the morning about a meeing taking place at the office of GB less than two hours later. While my contact person from CB (D) apparently had to cancel, I did not get a formal introduction. This meeting, was however very interesting as it turned out to involve decision makers from two departments of GB (BI and Marketing), the team from WB and the data specialists R and J. The topic was lead generation Meetup and scoring, but the meeting essentially concerned how to use the data insights made by R and J in an interactive web-based context (making the proof-of-concept go 'live' for the sales teams), integrating this solution with current platforms and making it compatibile with a future BI platform.

6

14/02/2016

Writeup of notes from 'Lead generation'

Finished making more well-written field-notes from the previous meeting

Writing

6

14/02/2016

Project Agreement delievered to ITU

Rewrote the 1-page description to meet the formalia of ITU's Project Agreement. Together with Marisa and Ingmar, we decided on a 50/50 share of my supervision.

Writing

7

17/02/2016

Read 'Robert et.al. - Writing Ethnographic Fieldnotes'

While doing so, I made a lot of notes regarding my methods and process.

7

17/02/2016

Received details on the next GB meeting

Contacted D about an upcoming meeting I heard about the week before. Was ultimately invited to the meeting. I asked D through e-mail if she could make sure I was introduced formally for this meeting.

7

18/02/2016

Car ride to GB

I drove to the office of GB w. D where we first spoke a bit about the meeting last week (where she couldn't go). She noted that R had told her that their general impresion is that she is the project manager for the whole process regarding data management (which apparently isn't true) - S is the one responsible, although she might want to "hide" behind D. Following this, we spoke about data management practices in general.

She had discovered that very few companies bother to actually look into their dataset in order to gain Event knowledge about their market position. They prefer to either look to other data sources or to simpy make assumptions about their customers' needs. Her view was in general that companies needed to understand how data can be used for focused areas (indsatsområder) within their businesses (to improve their business). Further, we talked about my thesis - and she went on to describe how she wrote her thesis about Georg Jensen's market positioning in France.

7

18/02/2016

Meetup - Lead scoring

For this meeting I had asked D to introduce me formally in order to make sure everyone knew my role and why I was present. D and I arrived in the meeting room at 12:58, T arrived 13:00, N shortly after and S around 13:10. Until S arrived there was a bit of small-talk about the purpose and focus of the meeting.

When S arrived, she had a few things she wanted to small-talk about as well, and around 13:15 the meeting Meetup could begin. I was formally introduced by D - and I followed her introduction up with a brief description about my thesis and how I wanted to conduct my research. There was a few questions, but they where generally very interested in helping me - and allowed me to record the meeting from this point of.

8

24/02/2016

Transcription of 'Meetup - Lead scoring'

Began transcribing the meetup from the 18th of February (as this was 1 hour long and difficult to hear everything, this process is very extensive and will become something I do as a side-project)

Writing

8

24/02/2016

Was invited to a meeting on the following day regarding 'data'. I was not sure what the meeting specifically would concern.

Event

8

25/02/2016

8

25/02/2016

8

25/02/2016

Invited to a meetup Car ride to GB

Meetup - Data

78

Writing

Car ride to CB

Meetup

Event

Event

Method Event

While driving from the office towards the office of GB w. D, I was introduced to the topic of today's meeting.

As a follow-up to the meeting on 12th of February, WB had estimated a budget and how many hours they believed they would need to develop the solution proposed on the 12th of February. Costs: 1,000.000. DKK, Event including 1000 work hours. D was very much aware that GB would be quite shocked to hear this new budget. N met us at the meeting room.

The proposed data management architecture was presented to GB - and based on the estimates from WB, it was generally decided that WB would probably need real access to current data and SQL-querries in Meetup order to revise their proposal. C amplified that he understood the estimate, but that the organisation currenly would not be able to pay for such solution.

Drove back to the office w. N where we talked about the meeting and whether this was useful for my thesis.

I told him that it was very useful. I suggested that it would be very useful for me to 'follow' that USB to WB in order for me to understand how WB uses the data. N thought that was a good idea and told me that he Event would make the neccesary arrangements to make this possible. Further, he agreed to share some of the internal resources that I had been presented with for some of the meetings (wireframes, presentations, etc.)

APPENDICES Week

When

Topic

Description

8

27/02/2016

Mapping the actors

At this point, it seems I have been in contact with 15 people regarding data management. While even more Writing people might become involved, I decided to make a diagram of them all to show the relations.

8Week

27/02/2016 When

14 8

05/04/2016 28/02/2016

Read Topic'Ethnography and How To Do It' by Randall, Harper, and Rouncefield, 2007 Meetup w. Ingmar and Marisa Transcribing: Meetup - Lead scoring

Description Type Method Presented my current impression of the case and which aspects I found interesting to focus on. While While transcribing I realise one thing, that has seemed very general for the meetings so far: For people to 'algorithms' and 'prototyping algorithms' could be interesting to pursue, it was suggested that I rather focus Supervision understand the infleunce of their the choises, they rely on usercases that aspects represents their actions. but Writing on 'translation' patterns among actors.

This might later reveal/ stories interesting about algorithms, While I have notwill yetbecoded meetings, believefrom this aspect toabout be very this approach muchthe more directly Iderived the data theimportant. actual meetings I have attended.

914

29/02/2016 07/04/2016

Arranged a meeting R and N Translation processw. (notes)

While talking I was invited to a among meetingthe onactors. the following with the R. Wrotebrifly notes aboutwith the N, translation process Added Wednesday a few extra codes in data MA analyst QDA and The meetingthe would cover differentabout approach to the data warehouse challenge GB are facing. structured notes into a thoughts 'perception', 'using visuals' and 'using storytelling'.

914

01/03/2016 10/04/2016

9

02/03/2016

9 915

03/03/2016 12/04/2016 05/03/2016

15 10

12/04/2016 08/03/2016

Methodology: oninto my the readings aboutmade Ethnography and Grounded sorting my notes and writing The actors in sorted detail notes and began writing Based Looked field notes, a few overviews of the Theory actors toI began understand when their pressence Writing about my theoretical framework. mattered to the context being dicussed. Began writing general notes about the actors in terms of how I had encountered them. Tried to link these notes to actual parts of my field notes as well. It became evident that I Writing Meetup w. R and N Ncould forgotnot to just bring methe to titles the meeting.

Maybe he did notthis findreport, my appearance afterfor all.all the relevant Event use of the actors throughout so I made relevant alias names actors.and reading Methodology Writing Writing Meetup w. Johanna Had atrying brief to talk with a fellow thesis student, whom I first talked to in January.

We presented work and Methodology While formulate my use of Ethnography, Ethnomethodology and Grounded Theory I our realise how processes to each other.people I was especially inspired by her use of other. relational mapping (Clarke),an Meetup they differ - and to some are considered to contrast each I believe, however, that I approach would be Writing that to I will definitely more into. able integrate thelook approaches with each other for my paper.

Signup for the Poster Session with Geoffrey Decided to signup for the Poster Session with Geoffrey Bowker.

Reformulated my Project Agreement to fit Invited to meetup N invited me to a meetup at GB's office on the following day. If I can manage to fit my office schedule for Event Event Bowker the Icurrent this, will go.project.

15 10

13/04/2016 08/03/2016

10

08/03/2016

10

09/03/2016

15

14/04/2016

10 15 10

09/03/2016 14/04/2016 09/03/2016

15

15/04/2016

15 10

15/04/2016 09/03/2016

15

16/04/2016

15

17/04/2016

16

19/04/2016

16

21/04/2016

10 17

10/03/2016 25/04/2016

17 10

26/04/2016 11/03/2016

17

26/04/2016

11 17

14/03/2016 28/04/2016

17

30/04/2016

17 11

01/05/2016 15/03/2016

18

02/05/2016

18 11

03/05/2016 16/03/2016

11

17/03/2016

18 11

04/05/2016 17/03/2016

18 11 18

05/05/2016 18/03/2016 05/05/2016

11 18

19/03/2016 06/05/2016

18 12

07/05/2016 21/03/2016

18 12 19

08/05/2016 24/03/2016 09/05/2016

Type

Event Writing

A note about data silosmethodology' by Read 'Grounded theory Strauss and Corbin

While reading some of the conversations about the challenges with integrating databases with other platforms, I realise how many data silos that probably exist. There are so many tools available that gathers Method user data while providing a service. Why is it so difficult to extract this data to other environments Are the Thoughts Methodology Refining description Grounded Theory Writing companies afraid ofof'losing' the data Do they consider the data a valuable asset to store in their closed environment (probably) Are they afraid of what could happen by opening their services through an API or Driving towards the meeting Dthrough gave me insights on why DB be) was invited to this meeting (they have made the infrastructure of similar other methods (might solutions many times before). Further, she talked about how she had realised the following regarding Anonymised all structured notes While I within realised that I would be of relying even more onpart my of structured than to originally projects companies: a lot people want to be the idea notes phase,from but MA whenQDA it comes Event anticipated, I ideas decided anonymise names-and organisation notes new in MA QDAabout (the the Coding data implementing it istodifficult to findallanyone so she often use right a lot within of timethe briefing people original is still available, though). I print of these notes will later be used to visualise the relations of the projects. notes (using relational mapping).

Meetup - Data storing and analysis Meetup Meetup w. Sara Had a brief talk with another thesis student, who were working on a similar case study. We shared our Meetup Driving home from the meeting Nstories explained the have suggested approach

by DBforwould and that it might fostered a few ideas both cost of us.around 50,000 DKK - way cheaper than the current offer by WB. However, he suspects that C are afraid of allowing more external partners to be part of Signed up for the Poster Session w. Bowker. this Ingmar: "I look very seeing howhad youbeen present and discuss project.

N told memuch how forward another to Web bureau managing all aspects of the website prior to the Event Received feedback from Ingmar particular data fragments and how you theorise them as project, instantiating new site. When CB and WB was considered for this new it had been highly weighted to only have Event particular categories." one external parter for building the website. Now C is probably afraid of telling the direction that a new partner could be entering the project.

Master's Party Went to a party celebrating a friend, who just finished his Master's. This occasion allowed me to talk to other Why do weand talkgain about data at students insights onalltheir studies. Further, I was given very relevant feedback on my own thesis. I When you have more than regular customers, it becomes necessary to storewe them somewhere.

At had some interestion talksjust withaafew fellow student about how we saw the organisations were involved with.

Event some point, a company decides to gather customers in a me Customer Management System.

Michael Hockenhull listened to my thesis these context and advised to look Relations into articles on Evaluation Design.

Later, it is realised that these insights are not sufficient.

Before going to bed that night, I had several thoughts about what would constitute data value - I ended up More data is suddenly other methods purpose". / systems. How to connect / cross-relate these data writing a note, saying:gathered " alue --through Defined by context entities And what if you have more than 200,000 customers stored As it becomes more complex store as these datasets make it useful forstructual both marketing Restructuring the thesis I restructured the thesis oncetoagain, I had realisedand some overlaps and issues. and sales Writing departments, a data warehouse is considered. What is rather complex is all the integrations from the data Writing mostly in the chapter entitled Read through myother field systems notes on that howcollect data was two mainthe contexts warehouse to the the conceptualised data as well as by theactors. internalI realised systemsthat for showing 'Conceptualising and representing data' defined how data was defined / represented, one being the implicit use of data and the other being Writing insights.

Thoughts representation through visualisations and storytelling (this will probably related to 'translations').

Insights: How can the knowledge obtained about the customers become be useful Lead generation.

Qualitative basedwhere on insights.

Are we just going to make a profound guess He on suggested the value ofthat data Supervision w. Ingmar Had a talk scoring with Ingmar, we discussed my approach with the data analysis.

I test it, and make about a prototype Or(Mol), are we using data to(Goodwin, actually establish the values looked intothen literature ontology representation Coopmans, Latour) and some of his Supervision To some degree it is possible use current but other times we have not yet collected the data (e.g. own articles (focusing on the to structure of thedata, analysis and methodologies). website behavior) - and can therefore not yet use these insights for scoring the data. So at this point, I believe that aRpage ( other relevantmy actors) will suggest someI values (basedtoon priorities and current Restructuring the thesis Wrote half describing approach to the study. had decided use the case study as ainsights) frame for for the lead scoring. This willand serve as a prototype lead scoring for a andoom will afterwards be studying evaluation design translations.

With on thisthe approach, I would bewhile, able to in on relevant modulated fit case the realities.

Maybe it would at the same timebroadly be useful to estimate usefulness of the parts fromto the study, and oom out again to talk more about the casethe study.

In the end I would Writing scoring inrelevant relation to current take the parts frominsights. the ' oomed in' analysis and sub-conclude why this is relevant. Afterwards I would draw my learnings into a conversation about how it relate relevant literature.

Supervision w. Ingmar (online) Had a Skype meeting of around 30 minutes where we talked about my current ideas about the meetings where it mightforbring me. Initially my mixture of Grounded Theory Ethnography wasdesign. accepted.

Approved for the Poster Session w. Bowker and Being approved the Poster Session w. Bowker, I looked more intoand details of the poster This Event Further, whereoftowhat go from coding of theconcerning meetings takes a lot time, so I should definitely Supervision allowedwe mediscussed to regain focus washere: actually relevant my study. start with this soon.

Reading Goodwin Reading some of Goodwins articles, I realised that he would become a very important part of the second Theory Considerations How is the meetings used phase of my analysis (related litterature).

Re-coding with a focus on actor behavior

Brief talk with w. NGephi and Google Sheets Playing

Case study writing

a while I have been considering how to map different behaviours of the actors and how they interact - For Time used on decisions project.

Todaya Icommon decided ground to go through my field notes and re-code allproject notes with the following codes: - with Timethe used on finding / common understanding about the questions', 'Deciding', 'Opinion', 'Giving insights / Sharing knowledge' and 'Generating ideas'. Each - 'Asking New people, new companies of these actions was accompanied by the actors name. This would hopefully allow me to export an overviewThoughts - Considering the format of the meetings Coding data howare each relates the different aspects ofbackground the process. IWhat currenly have ando idea of posses who is - of Who the actor people at thetomeetings What is their knowledge they decision makers who On is influencing the base project in other ways - but these are only ideas - considered How do they view their targetand group what do they their knowledge taken from my basic perception. By doing this thorough coding, I believe that I will be able to see relations more directly.

Had shortbehavior talk withcodes N. WBwas hadbrought revealed they would able toSheets. give a realistic bestructuring able to Theaactor tothat life using Gephinot andbeGoogle I used aoffer lot ofand time build any Data Warehouse before the deadline (1/5). Friday received a call from Session GB stating that the data in order to make itsolution possible to create visualisations.

A final N poster for the Bowker was sent Analysis they wouldLab. like an offer from DB. He gave them this 1.5 hour later (around 50,000 DKK). On Wednesday he Event to Ethos have a meeting w. S from GB regarding this proposal and with a focus of preparing all details for a meeting Combined two sections of the case study and wrote combining paragraphs.

Writing with the CEO Friday.

Goodwin Coding started

Read Goodwin paper, wrote analytical parts of the case study.

Writing This gave me a lot of categories.

After just finishined coding one meeting, some categories seemed to appear more often than others. Transferred my notes to MA QDA and started coding my notes. Although Coding data Wrote analytical ofcompany, 'Defining aI coded lead', based onjust a Presentation from September. the first an meeting was description with another this one to get an idea about which categories that Writing might be relevant to use for the remaining meetings.

Ethos Poster Session Presented and received feedback at the Ethos Poster Session. This gave me a bit of clarity of the focus I shouldstrategies pursue and me to behind chosing a specific Meetup - Reviewing the presentation Talked forreminded the proposal forconsider the CEOthe andreasons reviewed the presentation N had graph. made. While the graph Meetup Event being presented gave a simple overview of actor behavior throughout the meetings, I recogni e that there are so many other interesting parts I am not covering.

Meetup w. Ethos Lab We were only a few people present, but I had some nice talks about my project and listened to their Event proposals (I was the only one present who is actually writing the thesis now).

Read 'Choosing Evaluation Models' by Hanne This gave me a very thorough introduction to some of the concepts and ideas regarding data evaluation Theory Foss. continued models. wrote many of notes about this, whichThis will undoubtfully be part theidea (related Coding Done withI the codings the first 5 meetings. has already given meofan aboutliterature) the mainanalysis. aspects Coding data discussed and what I should probably be looking more into.

Case study writing Analysis of the first three meetings restructuring other parts of the thesis.

Writing The proposal for buying the technical solution from DB was brought to the CEO. At this meeting the whole Meetup Document outlinesfor the CEO isuali ed document outlines based on actors and behaviors, whichextenseviley allowed me to Meetup - Proposal lead generation concept in relation to the data project was explained asgain well.a better analytical Analysis overview of the meetings.

Writeup of the field notes from the previous I ended up writing 4700 words (around 11 pages), so this field note was quite extensive - and very useful!

Case study writing AnalysisI added of the last meetingsfromformulating a sub conclusion the case analysis.

Writing day Further, illustrations the presentation to make theoffield note even more understandable later Writing on.

Theori ing concepts As I reviewed the sub conclusion for my case analysis, I saw two general concepts that I found interesting to investigate further: Framings and Translations.

Both in my for a while, but as I was Coding finali ed and visuali ations created By coding the field notes with relevant 'tags' I was ableconcepts to gatherhad an been overview ofmind the overall discussed Writing able to and highlight of the influences that in the Looking empiricalatevience, it wasations obvious to me content how itsome related to the purposes of occured the meetings. these visuali along withthat theI Coding data could field startnotes, theori Iing about these concepts in relation thewhich study.hypotheses and considerations that actual have started questioning what I seeto and might be 4 relevant to pursue further.was sent to both.

Analysis draft sent to Ingmar and Marisa Section and 5 from the analysis Writing Considering manyliterature hypotheses. This is one of Hypothesis: Algorithms and numbers rule decision making in modern organisations. With scoring we seek to Analysis Looking through Improving analysis them. amplify which data entities is worth more than other entities. Focused data mining techniques allows for a Thoughts Supervision w. Ingmar Advicetransparent on structure, use of visal and specific on moving outmore from usable the equation the analysis mores approach which willabe easiernote to maintain andmyself in reality for theinend-users Supervision (my perception should be saved for Reflection / Discussion purposes).

(Sales and Management) Defining a lead

19

10/05/2016

19 13

11/05/2016 28/03/2016

Analysis section improved Writing 'Case description'

19

12/05/2016

Analysis section improved

Looked through from Marisa between and Ingmar andtoimproved thewhat sections accordingly.

For the last week feedback I have been shuffling trying understand I might and might not see in the datasets - but to become more aware of what has actually occoured, I found it beneficial to start writing a Improving analysis few pages about theintro. case from a linear perspective. While this focus is on the goals of the companies, it might help me to reveal is the the case.

I acknowledge that I might have to rewrite this Mainly structural advicewhat on how theessence analysisofcould become more reader-friendly. section later, when I have defined my focus.

Writing Writing Writing Supervision

19

13/05/2016

19 13 19

14/05/2016 29/03/2016 15/05/2016

20

16/05/2016

Methods

20

17/05/2016

Analysis 5.1

20

18/05/2016

Analysis 5.2

20

19/05/2016

Analysis 5.3

20 13 20

20/05/2016 31/03/2016 21/05/2016

20

22/05/2016

Analysis 5.7 and 5.8: Sub Conclusion

Writing

21

23/05/2016

Framings

Writing

Supervision w. Marisa Introduction Meetup w. Marisa Methods

Analysis 5.4 Finalised my main thoughts about the 'Case Study' Analysis 5.6

I gave Marisa a short recap of my current considerations about the case study. Two of my main perspectives was presented: 1) The 'evolution' of data architectures and processes 2) Assumptions about data and measures for weighting.

Refining significantly Further, she suggested a few approaches I could follow: - Refining Empiricalsignificantly studies of design process. How do reg.s are Different stakeholders.

- STS. isuals in terms of interactions and meetings - Refining Politics ofsignificantly algorithms Refining significantly Sent Ingmar and Marisa 6 pages on the case, including figures representing different architecture proposal for data management Refining significantly and indicating some of the different phases that this project has gone through.

Writing Writing Writing Supervision Writing Writing Writing Writing Writing Writing

79

APPENDICES Week

When

Topic

Description

Type

21

24/05/2016

Framings, Translations

Writing Framings. Reading Translations

Writing

21

25/05/2016

Translations, Conclusions, Reflections

Writing

21

26/05/2016

Conclusions, Reflections, Framings

Writing

21

27/05/2016

21

28/05/2016

Sub-conclusion

Writing

21

29/05/2016

Discussion, aluation as Determined by Framings and Translations, refining other parts

Writing

aluation, Translations

Writing

Writing Writing

APPENDIX C - 2016-02-12 LEAD GENERATION 1

9 people present:

2

Setup

3

Ida 0% (laptop)

Carl

Fred (GB)

10% 10% (laptop)

(notepad)

Lars (WB)

Hillary (GB) 20% (laptop)

5% (laptop) Ken (WB) 10% (laptop) Martin (WB)

15% (laptop)

Eric (GB) 30% (laptop) Me 0% (iPad)

4 5 6 7

Power relations E in charge. Are focused on money and time constraints.

H leading the meeting. Almost like a project coordinator.

K is a decision maker within WB.

8 9

Description I got a text message at 8:13 in the morning: “Good morning Mathias. There is a meeting regarding leadscoring at GB at 10. I have talked to Ann who have agreed to let you come by. Their address is ___ and you are free to take a taxi from the station.

Just ask for Ann, when you arrive. Would that suit you? Kind regards, Bill.� Knowing nothing more about this meeting, I decided to go.

10 11

I arrived 9:50 at the reception. Met Carl shortly after checkin.

Fred came to get us at the reception at 10. On our way upstairs C told F that he had received a call from A (project manager) at 9, saying that she couldn't be there for the meeting.

12

We arrived at the meeting room, greeted the others and went for 1/7

80

APPENDICES v

13

..Lead genera

a cup of coffee and a glass of water.

While waiting for coffee, I talked to a woman (H) from GB and introduced myself and my reason for being there. She said that we probably should have a formal introduction in the meeting room. Shortly after this was forgotten as we sat down.

14

H began talking about lead generation - and the meeting had suddenly begun. Presenting a few slides about lead generation in GB based on the data analysis C had made with F.

15 16

Lead generation

17

The above slide was presented and E initially began asking about specifics.

18

With the development of the new website, the following has been considered: - Classification of users based on buying patterns of consumer goods - Arbitrary value setting, rather than transactional value

..Hillary ..Knowledge sharing ..Data usage ..Data analysis

..Using visuals

..Eric ..Asking questions

..Lead genera

19

..Data usage ..Classificatio

20

..Value setting ..Knowledge s

21

Weighting of data is an important factor, that needs to be founded in specific knowledge about the different channels.

22 23

Data management If we need to store all our data one place, access to specific systems are important to investigate further.

..Hillary

Data manageme ..Data structure

2/7

81

APPENDICES

..Lead generatio

24 25

Leads What is a lead? Is it a lead when the customer has been contacted? Does it need to be activity driven?

26

The dashboard will need to be setup for showing specifics on each lead (so the sales team can estimate the true value of the lead and make their approach fit the user needs). The dashboard will probably be an integration from Pivotal.

27

Where should this data be stored? External database? On of the current setups?

28

At this point the employees from WB starts speaking more. As they have developed a website which demanded such database, they currently has a database setup that would be evident to consider.

29

It would, however, be important to regard mapping of where and how the data should be stored as well as the technical integration. As the website is considered to be finished by 1st of May, one part of this database will be ready by then.

30

Right now WB is working with integrating current data management with Sitecore. But they have not considered developing the actually technical setup for a larger solution where all data management will be implemented. If such a project would be decided upon, they will not be able to be done by the proposed deadline (1st of May)

31

It could be considered to initially focus on just delivering weekly data dumps for the database “motor” until a full integration has been implemented.

32

What is the essential goal with our data integrations? Focus is on having targeted communication. If the costumers like our company, do they engage with the offers we promote?

33

Now I realise that the three technical guys are from WB.

..Classificatio ..Eric ..Asking ques ..Data represe ..Marketing ..Sales ..Hillary ..Knowledge sha ..Data structure Data management / Arch ..Hillary ..Asking ques ..Data structu ..Knowledge s ..Martin ..Lars ..Ken ..Data origin / ..Integration ..Knowledge s ..Asking questions ..Martin

..Knowledge sharin ..Martin ..Lars

..Idea generat ..Martin ..Data structure / storage ..Integration Data manage ..Asking ques ..Eric ..Integration ..Sales ..Communication ..Thoughts

3/7

82

APPENDICES

..Integration

34

Absis will be phased out, so an integration with this platform might not be relevant.

35 36

C talking about his role with the data management of WB: Some of the data entities are just snapshots. A proof of concept showing what is possible with the current data. there has been a lot of cleaning data involved.

37 38

E: GB needs are data based on transactions and relations.

C: A final setup needs to be developed so the data input can be sent to validation and analysis within an SQL database.

E has a focus on Business Intelligence. Should this project be related to the new BI report platform?

He has a focus on what is manageable, timeframe and budget.

..Data origin / sourc ..Knowledge sharing

Data manage ..Carl ..Knowledge s ..Data analysis ..Data usage ..Opinions an ..Eric

39

..Data origin / so ..Validation / Tes ..Carl

40

..Knowledge sharing ..Algorithms

41

F. I am just looking forward to get started. For this is a challenge that is solvable, but might take some time.

42

E is a bit nervous about how WB can support the implementation.

43

K: maybe we should consider where the scoring algorithm is placed? At Sitecore or in a separate “motor”?

44

M begins drawing their current database approach.

45

Where should the data scoring be placed? Pivotal. WB analytic SQL database. Or a new SQL database?

46

E mentions that this is his first meeting about scoring, so this is all news to him.

H tells him that the meeting is called “Scoring and data gathering” (this is news to me)

..Data structure ..Eric ..Asking questio ..Business Intelli ..Integration ..Fred ..Opinions and v Data manageme Important reactions ..Integration Data management ..Eric ..Opinions an ..Ken ..Asking questions ..Algorithms ..Integration ..Using visuals ..Martin

47

..Idea generation ..Asking questions ..Martin

48

E would like the data to be stored in the analytical SQL.

49 50

The Business Intelligence Platform In 4-5 months, the new BI platform will be ready, where they would love for the SQL to be incorporated.

..Integration ..Value setting (sco ..Data structu ..Eric ..Knowledge s

4/7

83

APPENDICES

51

F and C has developed such algorithms in their SQL database and made table for it. This would be easy to copy to a new system.

52

M presents the drawing:

53

..Algorithms ..Fred ..Carl ..Knowledge sharin ..Idea generation ..Martin ..Using visuals

..Using visuals

..Integration

54 55

arrow down. Dashboard / adminÂ

56

---------

57

F loves the setup WB are proposing.

58

An important aspect of this proposal is that WB are (currently) only covering parts of this.

Ex analyse, ex categorisation, ex Web and more data.

..Data structure / storage ..Opinions an ..Fred Project (Plan ..Knowledge s

59

..Martin ..Budget

5/7

84

APPENDICES

..Knowledge s

60

Pivotal and LM integration is what has been budgeted at this point.

61

K: Should it be possible to change some scores (or how they are calculated)?

H. No. We only need visuals that tells you something about the leads and why certain values has been given.

..Martin ..Integration ..Data usage ..Algorithm

62

..Asking qu ..Ken ..Opinions an Important rea

63

It is however, necessary that the setup is dynamic so administrators can be given access to change how values are given. A scoring at 35 could for instance turn out to be invalidated.

64

Local differentiation could be a possibility as well (although it was a rather difficult task for the data analysis C made)

65

WB has been developing a similar solution for DSB.

66

It might be necessary to develop a sync/export function for the BI platform.

67 68

M: Where is the current data stored?

F: for the users, it seems as if all data is in Pivotal. However, a lot of the current data is a lot of different places.

69

E towards WB. Now is the time, where you should say that this was not part of the original project plan.

70 71

A solution could be: WB should create a setup which is going to be the ‘motor’ for data processing. This could later be integrated with the BI platform.

72

The logic for many of the data integrations and scoring has already been developed by C and F.

..Hillary ..Data represe ..Lead genera ..Algorithms ..Knowledge sharin ..Hillary ..Data structure / storage ..Hillary ..Knowledge sharing

..Knowledge sharing ..Martin

..Business Intelli ..Eric ..Idea generation ..Integration ..Martin ..Asking questions ..Data structure / storage ..Data represe ..Fred ..Knowledge s ..Eric ..Knowledg ..Budget Project (Pla Important ..Martin ..Idea generation ..Data structure / st Data management / Arch ..Integration

6/7

85

APPENDICES

73

E. It is important to remember that we need to create services, and not just SQL-scripts that is receiving data. It should not be point to point integration. We need to construct it to take account for new sources and new approaches to data management.

74

E. Well, we have the data now - så what are we waiting for? I am guessing that A (project manager, who is not present) would say that the next step would be to gather on overview of: Specifications Comparing expectations Timeplanning and limitations Budget

..Opinions and views ..Eric Data manageme ..Integration ..Decisionmaking ..Eric Project (Plan / S ..Budget

75 76 77 78

79

..Eric ..Asking ques

80

..Martin

E. Having all these aspects in mind, it is meaningful to consider whether it is essential to launch on May 1st?

Should we find someone else to develop this?

81

WB: It would not be possible for us to send the proper hooks for their solution until March / April (which is necessary for developing the rest of the platform)

82

E: Would it be possible for WB to develop this?

83

M: It would not be possible within the given timeframe, but maybe within 2-4 months extra.

84

Based on her knowhow, H would try developing a plan for what is possible to develop by the website launch and when the rest might be possible to develop. Further, she is going to contact A to check which direction we should take the project from this point on.

85

F will gather his SQL strings in a document and send these to WB (he was actually doing it while the meeting took place).

86

This concluded the meeting. Afterwards, C, F and the guys from WB had a brief talk about how to share their SQL strings, scorings and data insights in a proper way.

..Knowledge sha ..Development / ..Eric ..Asking questio

Project (Plan / S ..Knowledge sha ..Martin

..Involvement ..Decisions Project (Plan ..Hillary ..Decisionmaking ..Algorithms ..Decisionmaking

Data management ..Value setting (sco ..Algorithms

7/7

86

APPENDICES APPENDIX D - ARCHITECTURE DRAWING BY THE WEB BUREAU

87

APPENDICES APPENDIX E - DATA ENTITIES IDENTIFIED AT THE WORKSHOP

88

APPENDICES APPENDIX F - ADDITIONAL CONCEPTUALIZATIONS Gephi illustration of the relationship between different actor interactions throughout meetings.

89

APPENDICES APPENDIX G - PRESENTATION SLIDES 2015-10-05 Lead generation Workshop, slide 35-40.

90

APPENDICES APPENDIX H - 2016-02-18 LEAD SCORING

1 2

Setup Ida 2% (laptop)

Ann 30% (laptop)

George 40% (laptop)

Me 2% (iPad)

Hillary 26% (laptop) Slides

3 4

..George ..Opinions and view ..Lead generation and ha

5

Informal talk (J, G, A) G. I believe we need to look closer into lead scoring of.. for instance newsletters.

A. Automation

6 7 8

G. Local markets?

A. No, that is a bit too complicated.

G. Yes, we should move away from this approach.

9

(A bit internal talk about text writing for the new corporate website...)

10 11

(H arrives - ďŹ rst: small talk, then: meeting begins) A introduce me - and I follow up with a brief explanation about my work and my project. I ask for permission to record some of these meetings and to gather relevant data in general. Being given the permission to record, the remaining part of this meeting has been recorded.

12 13 14 15

How to (lead) score on the Internet?

Actions that are considered valuable should be in focus.

Isolated scoring variables.

Later, it could be interesting to look into the possibilities of using scorings to improve user groups.

16

[Looking into the speciďŹ c wireframes of the website]

..Ann ..George ..Asking questions ..Opinions and views ..Ann ..George ..Opinions and views

My presence

..Asking questions ..Ann ..Value setting (sco ..Idea generation

1/6

91

APPENDICES

17

..Using visuals

..Ann

..Hillary

18

The front page. Contact. Search.

19

H: we know the most obvious (data entities to score).

20

A. Academy (newsletter), contact formula

21 22 23

H. Should ‘contact’ be regarded in terms of the collected score? (makes an example of when it could be considered valuable) Several levels of contact is possible -

24

G. At some point we will be analysing how the contact formula is being used. But right now, we need to look into the basics.

25

H: A lot of people are writing about the price range (this is not available on the website itself). If people write about this, it should be regarded very relevant. So currently, we should make a high ranking of the contact formula.

..Opinions and views ..Value setting (s ..Ann ..Opinions and views ..Decisionmaking ..Classification ..Hillary ..Asking questions ..Opinions and view ..Knowledge sharin ..George ..Data origin / source

..Knowledge sharing ..Hillary

2/6

92

APPENDICES

26 27 28 29 30 31 32 33 34 35

..George ..George ..Ann ..Hillary ..George ..Ann ..George ..Idea generat ..Classificatio

36 37 38

..Decisionmak ..Ann Important rea ..Culture / Org

(everybody starts mentioning elements which should be scored) G suggests that they start writing some of these elements down.

• Contact Formula • Signup newsletter • Signup courses • Dividing signup newsletter in two: phone and mail • E-seminar signup • Search • Download report. (H gets a phone call, leaves) • Industri selection (this could be regarded a filtering, so to say) • Process (this will become ’total solutions’ later on) • ’customised solutions’ • Social media should be ranked low. This is not so relevant currently, as their presence is not so valuable.

..George ..Ann

39

..George ..Ann

40

..Classification

'Signup for product' is not going to be on the site at launch. But this could be very interesting to measure.

(H returns after almost 5 minutes. G update her with which elements that has been chosen)

..George ..Knowledge s

41

..Opinio ..Culture / Org

G believes it could be interesting to measure the impact of ‘aftersales’. A asks what the value of such would be?

..Lead genera

42 43

..George ..Opinions an

• •

Risk profile test Discovery test

..Data usage ..Asking ques

44

..Ann ..Ann

A. How about the use of filtrerings? Would that not make you specifically interesting?

..Decisionmak

45 46

..Hillary ..Idea generat

• •

Playing recordings Adding e-seminar to the calendar

..Classification ..Asking questio ..Ann ..George

47 48 49

H. If you chose to download the ISO certificate...?

G. Where is that in the process (of a sale)?

H. You seldom look into this after a purchase.

50

The e-shop. After one click, he is forgotten. We are not able to follow the behaviour on this site.

..Decisionmaking ..George ..Asking questions ..Idea generation ..Hillary ..George ..Asking questions

3/6

93

APPENDICES

51 52

[Shortenings present at the wireframe]

53 54

[SDS. Safety Data Sheets] [PDS. Product Data Sheets]

55

The manuals are not present here....? [Confusion arises about this, as this does not seem to be intentional] Should this go into the library?

..Using visuals

..Hillary ..Asking questions

..Decisions

56

57

A and H talks about a possible decision about this that was made earlier. They do not dare to take a new decision about this now...

58

PDS requires passwords, so these files should not be too accessible - so not under ‘products’ for instance.

59

Do anyone actually look into these sheets? Why is it relevant to measure / score?

G says that a lot of people check these pages - go into Google

..Hillary ..Ann ..George ..Knowledge sharing

..Classificatio ..Ann ..Asking ques

60

4/6

94

APPENDICES ..User insight

61

..Knowledge s ..George

62

Important rea

Analytics to check the statistics.

[Google Analytics have now been opened and G are filtering the results to cover only one specific document] 15-30 unique visitors a day on this specific document.

..Using visuals

..Classificatio

63 64

G. We now have 16 defined scoring variables. How should these be weighted? Are there too many? Should we shorten it down?

A. There are not too many scoring variables.

65

Phase 2 is going to be personalisation of the site.

..Asking ques ..George ..Value setting (scoring) ..Decisionmaking ..Opinions and view ..Ann ..Ann

66

..Knowledge sharing

•

Opening videos?

..Data usage

67 68

..Ann ..Idea ..Clas ..Use

69

..Classification ..Asking questions

[Bashing of a competitor homepage] Search should possibly be removed, as they can’t see the value of this (currently users mainly use the search because it is too dificult to use the navigation).

Proposal for a future stage. After x amount of search requests a chat should appear (but who should answer?).

..Opinions and views ..George ..Ann ..Decisionm ..Data usage

70 71

(Looks into the list - prioritises the scoring variables) In general G are suggesting values of some of the entities, while H and A are addressing each scorings in relation to examples and thereby rationalises the value of each element (and how it should be scored in relation to other elements).

72 73

The final list:

..Idea generation ..Ann ..Value setting (scoring) ..Classification ..Hillary ..George ..Ann

2 2 3

Contact formula - both for mail and phone Sign-up newsle9er Sign-up courses Signup e-Seminar Play recorded e-Seminars Downloading prepara>on report Risk profile test Discovery test Product folder, PDF

3 4 4 4 5 6

Product Data Sheet Play video Filter industri Filter materials Clicking Customised Solu;ons ISO folder, PDF Add event to calendar

1 1 1 1 1

..Opinions and views ..Decisionmaking ..Storytelling ..Idea generation

2

5/6

..Decisionmaking ..Value setting (scoring

74

Having scored the variables, it should be possible at a later meeting to decide on the actual scoring system.

95

APPENDICES APPENDIX I - 2016-02-25 DATA MANAGEMENT

1 2

Setup Bill 10% (laptop, mainly closed)

Project (Plan / Scop

..Ann

Hillary 20%

Fred 15%

(laptop)

(laptop)

Me 0%

Ann 20%

Erik 35%

(iPad)

(laptop)

(laptop)

3

This meeting is extending upon the thoughts shared at the meeting two weeks ago (12/2-16).

4

Now, we should start sketching where the data entities should be extracted from and so on.

5 6

A has received inputs from WB, which she would like to present.

Their estimates are based on prior knowledge to the project as well as the SQL-strings F and C has sent them.

7

A. Before we begin, it is essential to consider what the actual needs are. Because the numbers you are going to see are probably a bit larger than you expected.

8 9

H. Let’s take a look at it.

F: It is essential that we prioritise the different aspects of our project.

10

Slides from WB appear.

..Knowledge sharin ..Data origin / sourc

Project (Plan / Scop ..Knowledge sharin ..Ann

..Decisions ..Ann ..Opinions and views ..Budget ..Hillary ..Data origin / sourc ..Fred ..Opinions and views

1/12

96

APPENDICES

11

12

..Value setting (s ..Knowledge sha

First: a description about the scoring engine - everyone present already know this concept, so A is skipping this part very quickly.

..Ann

..Using visuals

2/12

97

APPENDICES

13

..Fred

14

F: A calculating dashboard would be better than a direct integration.

15

E. On a later stage, it would be interesting to pair this project with the BI project.

16

H: Historical BI data. Would it be possible to trace the development?

According to the KPI’s it is relevant to pull out the historical data.

..Knowledge sha ..Opinions and views ..Eric ..Opinions an ..Integration ..Business Intelli ..Hillary ..Asking questions

17

..Data usage ..Hillary ..Opinions and view

18

E: This data should be stored somewhere. But where should that be? The complexity is the same. We have the need for such system. But it is relevant to consider that as many needs we have, the more complex the system architecture will become.

19

E notes that we need a professional data warehouse.

20

H. I need to be able to see how a score has been derived from the

..Eric ..Opinions and view ..Data structure / st

..Eric ..Opinions and views

..Hillary

3/12

98

APPENDICES

..Opinions and v

21

..Data usage

data (what has resulted in the given score?)

B: That we can easily do. But that is not BI.

..Value setting (scoring) ..Bill

22

..Knowledge sha ..Lead generatio

23

B notes that we are now considering lead generation and the BI project.

B. Actual data.

..Business Intelligence ..Bill ..Knowledge sha ..User insights

24 25

'Mongo dB’ stores data on user click behaviour.

[E are using a lot of articulation and makes it clear tha H seems to have a different idea about what the system could/should be able to do]

26 27

E use concrete examples while questioning the relevance...

A asks questions about what data would currently be relevant for the sales department.

H answers that she is probably the only one within Marketing who would be considering to pull such data

..Data structure Perceptions / Co ..Eric ..Knowledge s ..Hillary ..Asking ques

28

..Eric ..User insights ..Storytelling ..Ann

29

B. Be careful of making integrations with systems that might be out phased

30

A. New slide. Architecture of the proposed system.

..Asking questions ..Sales ..Data usage ..Knowledge s ..Hillary ..Marketing ..Integration ..Opinions and view ..Bill ..Data structure / st Data management ..Knowledge sharin ..Ann

4/12

99

APPENDICES

31

..Using visuals

32

Hill

5/12

100

APPENDICES

..Hillary

33

H. What is necessary?

34

E: Speaking in funny terms towards me: “S-k-r-å-t" (as a comment to something that they were curious if I would write down)

35

A, H and E begin talking about how several of the data integrations proposed by WB might be irrelevant.

36

Data from Apsis is currently only partly relevant. Many of these things are gathered from different systems.

37

A is reading aloud from the slides concerning the requirements proposed by WB

38 39

[F talks about “real-time” usage] Text string of 240 MB takes three seconds to load.

40

A announce the estimated hours proposed by WB (1000 = 1,000,000-1,500,000 DKK).

41

Now they are talking about BI and Ln should be crossed off the list (of integrations).

42 43

E. That is not the direction it should take.

BI people are sitting with this kind of stuff (pointing at the architecture-slide)

44 45

Slide: overview.

Purple: scoring engine, dashboard.

..Asking questions

..Eric My presence Important reactions

Data ma ..Integra ..Eric ..Hillary ..Ann ..Opinions and views ..Data origin / sourc Project (Plan / Scop ..Knowledge sharin ..Ann ..Idea generation ..Fred ..Data usage Data management ..Budget ..Ann ..Knowledge sharing ..Integration ..Business Intelligence ..Opinions and view ..Eric ..Knowledge sharin

6/12

101

APPENDICES

46

..Value setting (scoring ..Using visuals

47

..Strategy ..Developmen

48

Data manage ..Eric

E mention how one of the strategies within GB is: “we do not develop”, as this is not one of their main competences.

However, we should recognise if there is any knowledge inhouse that could be useful to use.

..Knowledge sharing ..Development / Impleme

..Integration

49

Mentions the board of directors in terms of how development should be considered.

50

B: Lm, pivotal, web

51

E. C is involved because we precisely doesn’t have that profile internally.

I guess we have learned from prior experience that we need external partners in order to solve these kind of issues.

..Bill

..Eric ..Knowledge sha

52

..Algorithms Data management ..Eric ..Knowledge sharin ..Strategy

53 54

E. We need the dedicated people F It is important for a tailored solution for our needs

55

E tries to problematise the challenge in solving it externally.

..Eric ..Opinions and v ..Culture / Organ

7/12

102

APPENDICES

56 57 58 ..Development /

Afterwards E talks in favor of WB since they should know the extent of the project and are able to document it fully.

This is not something we would get if we develop it internally.

Internally it is often very easy to be cutting corners on such a project.

Project (Plan / S ..Opinions and v

59

..Eric

60

..Eric

E: That was the problematising. Let us start by plainly writing down the things that makes sense now. Pivotal, Ln, web H: The black sheep APSIS should be removed.

..Opinions and views ..Integration Data management / Arch ..Hillary

61 62

[looking at the slides]

63

Purple in the middle: Company Scoring Engine

64

[F pointing at Web when talking about ‘format']

65

F. Is it valuable to build this?

66 67

[E pointing at slides].

2 out of 3 parts will still need to be re-made, even though we are able to remove an external data source. Apsis demands a

..Opinions and views

..Using visuals

..Integration ..Fred ..Opinions and view ..Value setting (sco ..Fred ..Asking questions ..Using visuals ..Eric ..Knowledge sharin

8/12

103

APPENDICES

technical integration and this makes it a lot more complex.

..Data origin / sourc

68 69

[New slide. Silence]

70 71 72

Project overview.

E. Ahh, all right… (now the pricing makes a bit sense) E: for comparison they have a budget of 750 hours for IT Bureau [for the BI project]

73

A. We might be able to talk them down to around 700 hours.

Project (Plan / Scop

74

Everybody present are aware that there is a buffer because of the unclear needs/specs from GB.

..Idea generation

75 76 77

F sketches examples of how concrete it could be done F: Shouldn’t we just get started?

Let’s specify what they should develop!

78

I should be able to setup a place where the data can be stored, so

..Using visuals

..Opinions an Project (Plan / S ..Eric ..Knowledge sharin ..Budget ..Ann ..Knowledge sharin

..Asking questio Project (Plan / S ..Fred Important reactions ..Opinions and view

9/12

104

APPENDICES

..Data structure

79

..Knowledge sha ..Fred

it is ready for a system we could build upon.

F wants to make this project more explicit and try figuring out what is possible now.

Data management / Arch ..Opinions and v ..Opinions and v

80

E. I do not want to just pick something and get started without knowing the process.. Don’t want to get out into the wild / the swamp...

81

H. I agree..

82 83

E. I am terrified of I want a more streamlined solution rather than the quick fix

84

GB would be able to deliver raw data

85

We have to remember that WB has already made an integration for Lm (so we should not pay for this again)

86

E. Suggesting that WB gets access to the current raw data. With this they might be able to estimate what would be possible with the data and how much time would be needed.

And then there is the cleaning of the data.

F: Data quality would become better by doing so..

..Eric

Project (Plan / S ..Strategy ..Opinions and views ..Hillary ..Opinions and v ..Eric ..Eric ..Knowledge sharing ..Data origin / so Data manageme ..Knowledge sha ..Eric ..Integratio ..Budget ..Eric

87 88

..Idea gene ..Developm Project (Plan / S Data manageme ..Data origin / so

89

Raw data would be sent out weekly for a certain period, which is the data WB should be estimating upon.

90 91

E. What happens if we are not ready by 30/4?

H. Nothing. But it needs to happen soon. Nobody expect it to be ready by now. But at Q4 it is a must. Data needs to generate leads

92

When?

93

B. We need to remember that the project originally was to generate a web score. Later it became about generating lead scoring.

..Opinions and views ..Fred ..Knowledge sha ..Eric Project (Plan / S ..Asking questio ..Eric ..Hillary ..Opinions and views ..Knowledge sha ..Lead generatio ..Knowledge sha ..Bill Project (Plan / Scope) ..Value setting (s

10/12

105

APPENDICES

..Knowledge sha

94

B. We need to recognise that leadscoring is a business proposition. If we are to do something like that, it is a different project.

95 96

B is a bit afraid that we are about to start building quick fixes….

If there is no grants to build that kind of scoring, we should be careful.

97

E. We could maybe try prototyping the case in order to generate specifications for the technical requirements.

98

Recognition of the fact that they do not yet know the complexity of the project. We need to become more clear on what the project would cover.

99

F [points towards the scoring of result data] Maybe we have reached that point?

100

Discussion around price range. E: Would it be possible to get it down to 450 hours?

101 102

Plan: • It is decided that WB would get a data dump in order for them to redefine their budget • F will give A a USB key (hopefully within the next week). This, she will give to WB while describing what is needed by GB.

..Bill ..Lead generation and ha Project (Plan / Scop ..Opinions and view ..Bill ..Budget ..Development / Im ..Eric ..Idea generation

..Eric ..Opinions and v Important reactions Project (Plan ..Value setting ..Fred ..Asking questions ..Opinions and view ..Budget ..Eric ..Ask

Data

103

..Dec ..Bill ..Eric ..Hillary ..Ann ..Decisions

104 105

WB would need to establish: Estimated hours needed

106

- Buyer history would be valuable to include as well.

107 108

On March 9 there is a status meeting with WB where we might hopefully know more about what is required.

Week 11 we might consider having a meeting with D (CEO)

109

—— End of meeting — —

110 111

[After the meeting:] 11/12 E: In terms of integration we should further consider: Who is the key contact?

Project (Plan / Scope) ..Budget ..Idea generation ..Hillary

..Integration ..Asking questions ..Eric

106

APPENDICES APPENDIX J - 2016-03-09 DATA STORING

1 2

Setup Eric 30%

Carl (CB) 20%

(laptop)

(notebo ok)

Hillary 10%

Me 0%

(laptop)

(iPad)

Nick (DB)

Owen (DB)

20%

10%

Bill 10%

(laptop)

Data manageme

3

B: Gathering data was the focus of the autumn 2015

4

Owen and Nick from DB has been invited to give their proposal for how they would be able to help GB out.

• Their core competences is about handling and managing data • They help customers to collect and understand their data • They have an IT department for marketing as well.

• They do not work with communication (they are 'communication enablers') • They make IT infrastructures • They have been doing a lot of projects together with René.

• Some of their main/biggest projects • Tryg and other pension associations • Falck • Retail • The company employ 30 people in Copenhagen.

• Have existed for more than 10 years • Safety is a main concern, especially from the financial sector.

• 80% of their solutions are hosting and running systems for their clients.

Project (Plan / S ..Bill ..Knowledge sha Purpose w. mee

5 6 7 8 9 10

..Nick

..Knowledge sharing

11 12 13 14 15 16 17 18

19 20 21

Nick • •

Partner, responsible for the IT group Focus on the technical aspects of the company 1/9

107

APPENDICES

22

As mentioned, they have a marketing service department with the main purpose of supporting clients (especially the clients' marketing teams). They help marketing departments to use their tools.

23

B mention what he and C had been talking about after the last meeting (the one where DB’s proposal was mentioned).

It seemed that the main concern last time was regarding the challenges with integrations.

C: We have that already. Now we mainly have to consider if we want to build this internally or with the help from someone else.

DB is one suggestive partner for this purpose.

..Nick

..Bill

24

..Knowledg ..Opinio

25

Purpose ..Inte Data manage ..Data structu ..Knowledge s ..Carl ..Opinions an ..Developmen

26 27 28

..Knowledge s

C. There are some challenges with making scalable solutions.

More generic solutions are needed, so we are prepared for new challenges.

We need a motor that can receive and send inputs to other things/ platforms.

..Carl

..Eric ..Opinions and view

29 30 31

E: The main challenge is to build it and automatise the setup.

Last time, the challenge was considered to be the price range.

This is still to be considered a prominent factor.

32 33

The current system is in Azure. There is an existing SQL setup.

Would it make sense to build a mini data warehouse here?

34 35 36

B: Have we confronted this with David yet?

H: No. It is better for us to become a bit more realistic before we talk to the direction - with a concrete project.

37 38

E: Originally it was mainly the customer behaviour that should result in the scoring.

Later, we decided upon 5 extra parameters.

Then we wanted to integrate the BI project.

..Budget

..Knowledge sha Data manageme ..Data structure / storage ..Eric ..Asking questions ..Asking questions ..Bill ..Knowledge sharing ..Hillary ..Decisions ..Hillary ..Opinions ..Value setting ..User insight

39 40

..Eric ..Business Intelli ..Lead generatio

41

Now we need to consider the best architecture to make it all ďŹ t together.

Knowledge sharing

2/9

108

APPENDICES

42 43

Data manageme ..Integration

44

..Eric

The original data project from autumn 2015 w. C covered 2 sources - and now we need to integrate web as well.

This approach would be easier to build now. Batch processing is manageable.

It is a less complex approach.

..Opinions and views ..Development / Im

45

E mention how WB probably looked at the issue at first (how they considered the integration problematic and the complexity of the project to be huge)

..Opinions and v

46

..Development /

47

If we are only talking about a pragmatical solution, it is easy to build in-house.

Could we settle on this? Use the existing approach as a prototype? Build the engine and then develop parts for it dependingly?

Project (Plan / Scop ..Opinions and view ..Eric

..Idea generation ..Eric ..Asking questions ..Value setting (s ..Lead generatio ..Eric

48 49

Challenge. When the scoring reach a certain level it needs to create a lead.

In Pivotal DB are doing integrations.

..Opinions and views ..Integration

50

..Knowledge sha ..Eric

51

..Data structure

Personal data and financial transactional data may not be stored in Azure.

However, it is some kind of a grey zone as we are gathering customer-related data in Pivotal.

..Security ..Value sett ..Opinions

52 53

It is closer to a calculating score with access to transactional data, that is needed.

We need background access to the underlying algorithms.

..Eric ..Algorithms ..Data usage ..Knowledge s ..Carl

54 55

Data management / Arch

C: we will store the raw data and make it accessible when specific questions such as “why is X at 17?”.

Further, if we decide to tweak the scoring one day, we are able to change the original scorings accordingly.

..Value setting ..Data represe

56

Visualisations of the scoring is something that is made available in a table that is made accessible for a BI platform.

57 58

B. As I see it, there are three levels: • Pivotal. Automatic processes. Dashboard

..Business Int ..Value setting (scoring) ..Carl ..Knowledg ..Bill

3/9

109

APPENDICES

59 60

..Knowle ..Opinions

• •

BI platform. What is the reason for the scoring?

Raw Data.

Representation Data manageme

61

E. Currently I would not recommend an internal solution, because the BI is not yet mature for this. That is where our fundamental challenge lies. It would probably be ready within half a year.

62

The challenge is that there is currently mainly old SQL servers accessible in-house. And GB are not prepared to buy any licenses for new ones now.

63

N: DB sometimes setup mixed solutions. Could this be something we should look into?

64

E. Now we have an interface towards WB - should we also establish one for DB? This is what makes it more complex

65 66

(C and N talks about the approach on their end) C usually splits his scorings into different fields in order to make it easier for the typical user to filter and understand the basic reasons behind a scoring (e.g. 2+6+5=13)

67

C. Further, it would be necessary to look into the specific dataset in order to establish errors in the importing, the cleaning process and the algorithms.

68

H mainly wants what is described on slide 38. Marketing and sales relevant insights.

69

E. Would it be possible to construct a model where you are hosting and we mainly are going to send you flat files?

We don’t want a new integration to go inside - again. That is what WB has already made (implying security issues).

..Data structure ..Business Intelli ..Opinions and v ..Eric ..Business Intellige ..Knowledge sharin Data management ..Knowledge sharing ..Eric ..Knowledge sha Data manageme ..Asking questions ..Nick Data ma ..Integration ..Knowledge s ..Asking ques ..Eric Important reactio ..Opinions and v ..Carl ..Nick ..Knowledge sha ..Algorithms ..Data representation ..Carl ..Knowledge s ..Algorithms ..Validation / T ..Knowledge s ..Carl ..Opinions an

70

..Hillary ..Marketing ..Using visuals ..Sales ..Asking questio

71 72

N Flat files are difficult to manage (a lot of errors usually shows up)

..Eric ..Integration

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APPENDICES

73 74 75

Typically they have establihsed a secure VPN to other clients.

And they have been revised by Price Waterhouse Coopers.

Because of this revision, they are not allowed to have data sent to their main servers.

76 77

E. We do not want to setup a VPN because we have transactional data stored.

78

Just to give WB access to Pivotal, has taken 8 hours.

79 80

E: Soon we will have the BI platform ready.

What we are afraid of is that we will need to build the same thing again. w. customer ID etc.

E. By the way, you should not even be recommending such types of approaches.

Data manageme ..Knowledge sha ..Nick ..Opinions and v ..Knowledge sha ..Eric ..Knowledge sharin ..Eric ..Budget ..Knowledge sharin ..Business Intellige ..Opinions and view Data management

81

..Eric ..Opinions and v ..Security

82

..Knowledge sha ..Opinions and view ..Nick ..Owen

83 84 85

N. We have done so for many people and it is a very secure approach.

O. As mentioned, we have been validated.

E: Well, I’m just saying this is a very risky solution...

N. [towards O] It is basically a question of religion

..Knowledge s ..Opinions an ..Eric

86 87

..Opinions and view ..Nick ..Algorithms ..Eric ..Asking questio

88 89 90

..Carl

E. How is the data cleaning made?

C. [towards O+N] Avoid storing historic data in a lot of different tables.

N. describes the technical approach, that he has considered (for updating the data accordingly) Further, we need to think about our proposed solution as being more like a motor than a Data Warehouse solution. A Data Mart.

Would this become a temporary model?

..Knowledge sharin ..Nick ..Knowledge sharin

91 92

E. If we consider that this is the scenario (flat files)..

Api access would be a possible approach for sending data back to GB.

93 94

N We need flat files to be stored at our servers every day at a certain time. Then the data will go towards the algorithms made

by C. And afterwards the data insights will be sent towards GB

Data management ..Asking questions ..Opinions and view ..Eric ..Development / Im ..Knowledge sharin

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APPENDICES

95 96

through an API. I do not believe this to be a big task.

WB is used to sending data to DB.

97

N. Would it be possible to access customer data through Azure?

98

E. Yes. But we would like to avoid point to point integration

99

E: We should be aware that this approach would be a discount solution. It is not beautiful. In four months we would need to change the architecture. If something is made now, we would afterwards need to customise and fix it to fit our needs.

100

E: The GB method has usually been the following: we fix something quickly and it works. But we never write the documentation so we are able to update and fix the quickly made solution.

101 102

C. I always write documentation inside the code. This can (easily) be transferred into a readable document.

- business rules, scorings and coding rules should be described throughly.

..Nick ..Opinions and view

..Nick ..Asking questions Data manageme ..Integration ..Eric ..Opinions an Data manage Important rea ..Opinions an ..Eric ..Culture / Organisa ..Opinions and view ..Eric

..Carl ..Knowledge s

103

..Algorithms ..Data analysis ..Validation / Testin

..Idea generation

104 105

N. A concrete approach now would be for us to receive 200 lines of sample code per dataset. We would be able to test this and buid the scoring on top of this in order for us to update the code and algorithms continuously.

106

It is mentioned that Marketing is often the department who indicates what knowledge they are seeking - and the IT department will find a solution for it.

107

H ask about data cleaning. Where does that fit in this approach?

108

N starts drawing a model

..Nick

..Marketing ..Processes ..Culture / Organisation Data manage ..Hillary ..Asking questions ..Using visuals ..Nick ..Knowledge sharing

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APPENDICES

109 110 111 112

..Eric Important reactio ..Opinions and v

..Hillary

113

H. It is worth noting that WB was very far away from the goal with their proposal.

114 115

O. Yes, we recognise that this would become a new partnership.

But we want to make this work.

116

E. I know I am very much in doubt if this is really the best solution

117

B. Nobody knows what solution would be the best. But as you recognise youselves, it might take a year before the BI platform is ready.

118 119

E. My focus is on a well build and considered architecture.

I do not have a complete overview of our business in order to see if this is the best solution.

120

[N’s drawing is being discussed]

121

Validation. Transformation

122

Ftp-> data staging area -> customer data margin -> marketing/ lign

..Opinions and view

..Owen ..Opinions and view

E. Ok, this all sounds very good. But now I have to play the devils advocate.

We are now about to accept a third partner (excluding licenses).

And this hurts a bit.

Now we are furthermore going for a simpler and cheaper solution.

Project (Plan / Scope) ..Eric ..Opinions and view

..Strategy ..Bill ..Opinions and views Data manageme ..Eric ..Opinions and v

..Knowledge sha ..Opinions and views ..Nick

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APPENDICES

123 ..Using visuals

Data management

124 125

DB VPN access and API is possible

126 127

N: It is not that difficult.

If we are going to do this for you, we need to know what kind of files should be accessed and what GB would like to see from this Further, there would be some licenses you would need to buy (however, you only need some customised licenses)

..Nick ..Opinions and view

128

..Budget

..Opinions and view ..Nick

129 130

Data management ..Opinions and view

131

..Eric

..Opinions and view

132

N. On a key customer there is no reason why they should not be able to send more data than one number

133 134

Table drawn by N: Score Score Score 1 2 3 5 2 4

..Nick

..Data represent ..Knowledge sha ..Idea generation

N: The obvious choice would be to export the scorings to GB: E: =To the GB Corporate Data Warehouse (which is not ready yet) If we are using this model, the scoring knowledge will be stored at DB (such as specific insights on customers and the reasons for the score)

Score 4 6

Score 5 2

Score 6 1

Sum 20

..Nick

8/9

114

APPENDICES

..Decisionmak

135

These numbers should be possible to gather in Pivotal

136

B. Well, first we need GB to consider the principal decision about whether they would accept yet another distributor.

137 138 139

142

H: If this is going to be decided. Who needs to be involved?

- C, F —> DB. E: Ok, what will you need in order to clarify that you are capable of this?

C: It should be enough to just send flat files w. 200 rows at first.

N: Yeah, and if we accept such files, we should probably sign a SLA. E: No, a SLA is not needed. Not with only 200 rows.

143

E: Let’s see what you can find, and then decide based on this.

144

N: I am still a bit uncertain what the concern is regarding access to the ‘core’. This is the approach we normally design the architecture from.

The problem with access to the data ‘core’ is closer related to the operational plan, rather than the security aspect.

Project (Plan ..Culture / Organisation ..Bill ..Opinions and v ..Hillary ..Asking questio

140 141

Project (Plan / S ..Processes ..Opinions and v ..Eric ..Carl ..Knowledge sha ..Nick ..Asking questio ..Eric

145

..Knowledge s ..Decisionmak Data management / Arch ..Security ..Nick ..Asking questions ..Eric ..Knowledge sharing ..Integration

9/9

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APPENDICES APPENDIX K - ARCHITECTURE DRAWING BY THE DATA BUREAU

116

APPENDICES APPENDIX L - 2016-03-16 PRESENTATION REVIEW

1

Prior to this meeting: B told me that this meeting was about structuring a presentation of the proposal for the management.

2 3

Setup: Bill

..Knowledge s ..Hillary

4 5

Data manage

Hillary Me

H: Yesterday I talked to F and he believes he have already built a solution on one of our own servers. He want to present this to E. B: This is to be considered only half a solution.

..Development / Impleme ..Culture / Organ

6

B: so, I’ve made a proposal for the presentation for D for Friday.

We can talk through this and refine it accordingly.

7 8

This (the presentation) is what we need to reach our target.

The focus is on the data and lead-setup in relation to the existing and illustrating how the system receives the web data

9 10 11

And then there is: - Time - Economy

12 13

We need to facilitate a reflection about our lead handling - Insight sales

14

The sales process is important to talk through

15

H: Shortly after Easter we need to have a talk about what we have in Pivotal and what it should be able to do with our new integrations B: F is obviously of the old school where you build something just because you believe you have the ability to do so.

B: He throws himself at it because he likes to do so. He finds it exciting. However he does not have the business oversight that is needed.

..Opinions and v ..Bill ..Framing ..Knowledge sharin ..Bill ..Knowledge sharing ..Bill ..Bill ..Opinions and view ..Processes ..Decisionmaking ..Hillary ..Opinions and v ..Decisionmaking

Representation ..Hillary ..Opinions and v

16

..Culture / Organ

17

..Bill ..Opinions and v

..Opinions and view ..Bill ..Development / ..Hillary ..Opinions and views

18 19 20

B: We can’t just build something. We need to have the certainty that it would work after the implementation.

H: This would be relevant to bring up this Friday.

B: Yes, we need to oppose the two proposals (F’ concept vs the DB proposal)

..Decisions

1/12

117

APPENDICES

21

..Hillary ..Opinions and views

..

22

23 24 Project (Plan / Scop

25

..Hillary

H: It would be better to assign F to build the modules for Pivotal.

..Opinions and view

[Looking at the presentation] We need to create a TIP that regards the implementation. Make it evident that it is from this date to this date.

And we need to illustrate what goals we will be able to reach at the first phase (for the web deadline) and what we will not be able to reach - and why.

B: This is my agenda proposal.

..Knowledge sharin

26 ..Bill

27

Project (Plan / Scope)

28 29

..Integration

30 31

SLIDE2: TIP 'Data Management', ‘Scoring’ and ‘Mapping' we have been through Now we are at ‘Development’ (development of the lead scoring) Process (we should consider renaming this to ‘integration’.

Because this is what it is) And then there is the ‘execution'.

32

33 34

Project (Plan / S ..Culture / Organ ..Hillary ..Opinions and v

H: I have a concern regarding J. I believe she is slowing the process down knowingly. She does not see the need for rushing this project with regard to her own department. She should back the marketing team. H needs for J to join her ‘quest’ - it should became something they do together as a pair. “then we can become a pair like Dupont & Dupont at the sales meetings in the autumn”. H needs her to take the Sales Excellence role into action [it seems this is a rather new title for her].

H acknowledge that this is some challenges she is facing on her

35

2/12

118

APPENDICES

119

APPENDICES

of where we are now - how and why we have reached this stage. ..Bill ..Opinions and view ..Development / Im

51 52 53

[SLIDE6 - Development] So, then we are at this stage. How should this ‘machine’ look?

H: Maybe we should rename it to ‘Implementation’?

54 55 56

B: We have two data sources (Ln and Pivotal) H: Yes. B: and Apsis should probably be set up manually at first.

57 58 59 60 61

B: We have been looking a three solutions for the webinars: • Webex • Gotomedia • Adobe Connect - And right now none of these have an API making it possible to integrate these with a CRM system.

62

B: Apsis should probably be regarded as a third box, if we are not able to pull it into Pivotal.

[Describes the process] So, from the first sources, it is transferred through an sFTP —> DSA —> CDM —> and backwards to Pivotal through an API.

..Asking questions ..Bill ..Hillary ..Idea generation ..Bill ..Knowledge sharin ..Knowledge sharin ..Hillary ..Bill ..Opinions and view ..Bill ..Knowledge sharin ..Data origin / sourc

..Bill ..Knowledge sharin

..Data represent ..Hillary ..Asking questio ..Value setting (s

63 64

65 66 67

..Integration ..Bill ..Opinions and v

First of all web data is pushed to Pivotal.

H: The question is how we integrate this in the best way possible to account for the scoring system.

B: We could easily place it at DB, but we are interested in the web data to be syncing with Pivotal - it needs to go both ways (data obtained in Pivotal from other sources should be able to push insights on the users towards the web platform in order to personalise content).

..Knowledge sharing

Data management ..Hillary ..Opinions and view

68 69

..Asking questions

H: Somehow we need to combine these two platforms in the architecture.

B: would this be possible to do in the first phase? Without the full integration? B will ask C.

..Bill Project (Plan / Scop

70

..Bill ..Opinions and v ..Asking questio ..Hillary

71 72

B: As long as we are only pushing flat files, we can probably not get any further in phase 1.

H: When you are talking about flat files, you are talking chinese to me.

B: Excel-sheets, basically.

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APPENDICES

73

H: Okay, that makes sense to me.

74

B. It does not make sense to have realtime on web as long as we are only receiving flat files (e.g. weekly updates) H: It would be great to have realtime updates, but there are probably not many changes within a week.

Perceptions / Confusions

..Hillary

75

..Opinions and v ..Bill ..Opinions and views

76

..Data structure / storage ..Data usage ..Opinions and views ..Hillary

..Bill ..Knowledge sha ..Asking questio ..Hillary

77 78 79 80 81

..Bill ..Opinions and v Perceptions / Confusi

82

[SLIDE8] B: So, this is the list from web H: Is this the one WB has decided upon?

B: I believe so.

[H are looking more intense at the list]: it looks a lot like the original one from our first meeting where we prioritised the web elements. Asks me: wouldn’t you agree?

Me: yes, that is the same identical list.

..Hillary ..Asking questio

83 84

..Hillary ..Knowledge sha ..Processes

85

H: [refers to a meeting]: We had a meeting where we discussed several scenarios based on this list and regarded the most important aspects from this.

We made some assumptions and hypotheses about the users, stating that: “if a user clicks here, we can assume that…"

M and L from WB wrote these down. However, H had not seen the final list from this meeting yet.

..Classification

5/12

121

APPENDICES

86

87

6/12

122

APPENDICES

88

..Integration

89 90

[SLIDE9-11 - DEV] A lot of this is not possible before we have built the actual integration

91

92 93 94

[SLIDE12 - Building data] This one we drew together with the management.

H: We should not include this one in our presentation, as this would shape the context of the meeting. Then they are going to ask where we are in relation to this model, which is not relevant at this point.

95

B: D told us at a meeting how “we have to go with what we have now, and then enrich it continuously".

96

H. At this point it would be relevant to illustrate when this will change.

So when this is shown, we will have to look at how this does not ďŹ t with the TIP B: Are we allowed to revise a TIP?

..Hillary ..Decisionm ..Opinions ..Culture / Organ ..Bill ..Knowledge s ..Processes ..Opinions and v ..Hillary ..Hillary ..Opinions an Decisionmaking

97 98

7/12

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APPENDICES

99 100 Project (Plan / S

H: Usually not, but this is very different. It has transformed towards a separate project.

H: Now we are actually making a TIP that concerns implementation

..Knowledge sharing ..Hillary ..Decisionmaking

101

..Bill

B: Actually, we should make a TIP for all the elements within this [‘Building Data’], primarily the 360 Data Integration

..Opinions and views

102

103

..Hillary

104 105 106

..Opinions and view Data management / A

107

..Opinions an

108 ..Hillary

[SLIDE13+] Then there is some things like the lead handling H: we need to have x amount of slides that shows how far we are. Next step would be an overview of what we need to do afterwards.

Within this we have the aspect of pushing all the relevant data entities into Pivotal.

We should sketch what needs to be done in Pivotal to F

..Decisionmaking

8/12

124

APPENDICES

109

..Bill ..Opinio

110

..Hillary

111

..Opinio ..Decisio ..Processes ..Decisions

B: I would actually say that we need to put F (GB), M (WB) and N (DB) together [afterwards referred as ’the technical guys] H: I would like to be included in the discussion about what should be pushed to Pivotal.

B: Actually, J and H should go together and decide upon what they want in Pivotal at first.

112

..Involvement / Ownersh ..Bill ..Opinions and views

..Bill ..Opinions and v

113 114 115

..Opinions an

116

..Hillary ..Data usage

[SLIDE15] B: Some things need to be able to go backwards again.

H: And if leads are not caught (received by International Sales or Sales rep) H should get a ‘ping’ H will find a time in her calendar to meet with J in order to estimate what their actual needs are.

..Involvement / Ownersh ..Decisionmaking ..Hillary ..Lead generation and ha

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APPENDICES

117

..Bill ..Asking ques

118 119

B: [Draws a radar chart]. Does it look like this?

Or will we only receive a number? Or like this? [a bar chart]?

120

B: We need to be certain about what we are able to in Pivotal and how we would like it to be visualised. The bar chart is probably most likely what is doable at this point.

121

H: I need ‘ping’s when there is a lead, so I can assign the leads to Sales Representatives.

If they (the technical guys) can give an idea about this..

..Opinions and views ..Data repr ..Decisionm ..Bill ..Opinions and view ..Integration ..Data usage ..Opinions an ..Hillary

..Decisionmaking ..Bill ..Hillary ..Decisions

122

123 124 125 126

Decided to schedule a meeting shortly after easter (in two weeks).

H: We need to defined the journey for leads and Pivotal.

H: It might be that F should be at such a meeting as well.

B: F need to get ownership for this project, so that would be a 10/12

126

APPENDICES

very good idea.

..Lead generatio ..Involvement / O ..Bill ..Opinions and v

127 128 129

Representation

[Talking about the meeting on Friday and the presentation] The most important part of this meeting is “Done” and "Next step".

We need to elucidate what we have done already and what the next steps are.

Project (Plan / S

130

..Hillary ..Opinions and v

131

..Hillary

H: Somewhere in here we need to show the costs and describe how we have arrived at this number.

B: Yes, I just wanted to talk about this with you at first.

..Opinions and views ..Bill ..Knowledge s Project (Plan

132 133 134

The scope for data processing and API: DB has said they would need approximately 15 working days.

Further, they have made a ‘posting’ regarding safety, running costs, service etc.

135

DB would like access to these API’s and sheets in order to be more precise with their estimates.

Since we are sending numbers back to Pivotal, it would not be possible for F or anyone else in GB to access the original numbers and algorithms immediately.

This is mainly because of the safety issues that creates a barrier between the two setups.

..Budget

..Knowledge s ..Bill

136

..Budget ..Validation / T ..Algorithms

137

..Security ..Data usage

..Budget

138

H: [with regard to the budget] We can probably take some of these numbers from other budgets, e.g. running costs, hosting, licenses.

139

B: We would like to transform this into a pedagogical structured overview H: And then we need F to be included in it, so he will obtain ownership.

..Opinions and view ..Hillary

..Bill ..Opinions and view Representation

140

..Involvement / Ow ..Opinions ..Hillary

141 142

..Asking qu

143

B: Did you hire a customer journey specialist?

H: Yes, I think so. They had a meeting last Friday with the candidate. He presented a well-structured plan for the management.

I believe they talked about a contract as well.

144

[3 min small-talk about the website implementation]

..Bill ..Lead gen ..Culture / Company ..Hillary ..Knowledge sharing

11/12

127

APPENDICES

..Hillary

145

(…) ‘History’ should be removed from web. But we might integrate it later. It is definitely not important for the launch in May.

146

[10 min small-talk about something else]

147 148

B: Then there is something I have kept to myself for awhile.

H has decided that we should focus on three things (from the roadmap).

149 150

B: There are some tactics in terms of how we react to it.

H: I have arranged with D that I will revise the roadmap during the summer.

..Opinions and views

..Knowledge s ..Bill ..Knowledge s ..Hillary ..Strategy ..Culture / Org ..Processes ..Opinions and views ..Bill ..Knowledge sharing ..Hillary

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APPENDICES APPENDIX M - 2016-03-18 PROPOSAL

..Data representation

1 2

Project (Plan / Scope) ..Decisions

..Hillary ..Opinions and view

3

4 5

Before the meeting: (A, B and I arrive at 12:50. H arrives at 13. Tells us that D is supposedly 10 minutes late)

6 7 8

11 12

[Looking at the presentation] H: We need to clean it more up.

B: Actually there are a lot of hidden slides. But we can hide more.

B: Let’s remove ‘objectives’ and then just keep the ones underneath.

H: I am going to remove 'objectives' at mapping too, then. But not ‘objectives’ at ‘Scoring'.

B: No, that is pretty central (for the project).

B: And then we can ‘blind’ these three kinds.

13 14 15

H: The days [SLIDE 29] we do not need to show.

H: And then you have a “Scoring$Dashboard” (spelling error) B: Whoops, I’ll remove that one.

16 17

H: There is no reason we both edit the slides. Can you send me the final presentation when we are done?

B: Of course.

18 19

H: The orange lines, what are these?

B: The orange lines represents ‘Sales Executives'

20 21

H: Lead generation?

B: Lead generation according to mails are the 'Next Steps'

22 23

The meeting: (D arrives at 13:35)

24 25

Setup:

..Bill

9

..Knowledge sharin ..Bill ..Opinions and views ..Hillary ..Bill

On our way to the meeting (car, both A and B present): Ann has mentioned for David that the web project won’t make it for the deadline. He has not replied yet.

Hillary has had a meeting with Eric and Fred. They are onboard with the proposal.

10

..Opinions and views ..Bill ..Strategy ..Hillary ..Opinions and views ..Bill ..Hillary ..Opinions and view ..Asking questions ..Bill ..Knowledge sharin ..Asking questions ..Hillary ..Bill ..Knowledge sharing ..Asking questions ..Hillary ..Knowledge sharing ..Bill

Ann

Bill 1/29

129

APPENDICES

Me

26

(Davi d) (10%) (lapto p)

Hillar y 40% (lapto p)

David 20% (by the slides)

[D mostly moves around, close to the slides - but I have placed him at two positions with an indication of how much talking took place from each position]

27

28

H: This is the proposed structure of the leadgeneration project and how it could be implemented. There are some estimations about the price range, an updated TIP and a response to some of all the unknown factors - which we have talked about earlier (towards D)

29

H: Today we have hidden a few slides, but if you would like it afterwards, you are free to unhide them.

..Knowledge sha ..Hillary ..Budget Project (Plan / Scope)

..Hillary

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APPENDICES

..Knowledge sharing

30

not relevant to consider now.

31 32

[TIP]

33

..Using visuals

..Hillary ..Knowledge sha ..Value setting (s ..Asking questio

34 35 36 37

H: This is where we are now D: So, the scoring has been decided upon now?

B: Yes. The implementation is where we are struggling now.

But we are done with the weight and algorithm.

38 39

[Looking at the TIP once more] H: Some of these has been given new titles. We have done things differently than what was originally scoped.

Implementation is not part of the original TIP. So this is what we are proposing now.

..David ..Development / Impleme ..Knowledge sharin ..Bill ..Weight ..Algorithms

40

Project (Plan / Scope) ..Hillary ..Knowledge sharing

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APPENDICES

41

Data management / Arch

..Hillary

42 43

..Knowledg

H: What we have here is the parts of the data process we see.

We have gained an overview of the existing data entities and realised where there might be some absences.

..Data structu

44

..Data analysi Data manage

45 ..Hillary ..Knowledge sha ..Value setting (s

46

H: The analysis has been conducted - both in terms of qualitative leads and where the data is located.

This is done to give an oversight of where we are in the scoring process.

4/29

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APPENDICES

47

48

49

5/29

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APPENDICES

50

..Using visuals

..Lead genera ..Classificatio ..Hillary

51 52

..Knowledge sha

H: This is how we have defined the terms used for our work with qualified leads.

This is not something we have done before in GB. Not with the same level of detail, nevertheless.

..Culture / Organisatio

53

[2 Activity Planning Framework]

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APPENDICES

Project (Plan / Scope)

54

55 ..Knowledge sha

56

..Hillary Data manageme

H: We have built a framework for how this could be implemented.

This is not settled, but this is needed for having an idea about how it should be built.

..Opinions and views ..David ..Asking questions

57 58

..Hillary ..Knowledge sharin Project (Plan / Scop

59 60

..David

D: What is the focus for these areas?

H: This is the same graph that we showed a the management meeting.

D: Yes, I know...

H: It is examples of what could be integrated into each of the steps.

..Hillary ..Opinions and view ..Hillary ..Knowledge sharin

61 62

[Scoring] H: The goal for this purpose was to define ‘Lead scoring variables’, ‘lead scoring criterias’ and ‘lead scoring weights'

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APPENDICES

63

..Lead generation and ha

64

..Weight

..Knowledge sha

65

These are the lead variables we decided upon and this is how they where weighted.

66 67

[Implementation] And this is where we are now. We have finalised some aspects of the project. But now we are at the stage where we need to implement it.

Now we need to establish some goals for what we can get out from this and make the lead generation project ‘come to life’.

And then we need to make a timeline for how this should be setup.

..Hillary Project (Plan / S

68

..Lead generatio

69

8/29

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APPENDICES

70

71

So we have been given a few inputs to how this could be solved.

WB made a proposal and DB has made a proposal. We have settled upon a solution, we recommend (E, H, F and the CB), a budget and a plan.

72

Before we start explaining this, it is relevant to mention the two data structures we have: - data created on our own platforms - data created using other services

..Involveme Important ..Knowledg ..Hillary Data mana Project (Pla ..Data orig ..Bill

73 74

..Knowledge sha

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APPENDICES

75

..Development / Data manageme

..Data represent ..Integration Data manageme ..Bill

76 77

B: [The setup is explained briefly]

78 79

- We have Pivotal as we know it.

- And the web data will be integrated with this system and (through flat files) transferred to the WB infrastructure.

- Data sales are uploaded and sent back to Pivotal

80

..Knowledge sharin ..Strategy

81

B: This method has been talked through by WB, E and C. F has taken a look at it as well.

..Asking questions

82 83

D: Why this solution?

B: WB gives us thorough documentation about how they are going to solve it - and when the BI platform is built, we are able to move the setup in-house.

84 85 86 87

[Budgetary framework] H: For such a solution we are a bit below 300.000 DKK WB will get 180 DKK C gets a part and then CB are given a bit, too.

..David Data management ..Bill ..Knowledge sharing

..Budget ..Hillary ..Knowledge sharin

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Data management

88

..Knowledge sharin ..Hillary

89

90 ..Hillary ..Knowledge s ..Using visual ..Data representation ..Opinions and views

E are very confident with this solution as well. He highly recommends this.

91 92

H: This was how we approached the solution - and this is how it could look.

This is mainly an illustration of how it could look It could look like this as well:

93

94

H: I would say that we should go for both views.

95

H: What we have not yet talked about is how this should be structured further.

96 97

B: The previous slide tells us how the score has been aggregated.

But if we want to gain even more insights about metal-specific numbers, it should be structured differently.

..Opinions and v ..Hillary ..Data structure ..Asking questions

..Value setting D

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..Data structu

98

That would be a job for the marketing team to elaborate further on.

99

H: In the daily routines that kind of numbers will not be relevant.

So this should be gathered differently, when needed.

100

D: So, if I look at this from the Sales Executives point of view, he will see a number and then have access to a quick overview of each sub-unities.

101

H: [continuing the example] Based on this score, he would have reached a point where there should be something to get (business-wise). This will require additional effort (from the Sales Executive).

D: This picture would not be enough [the bar chart]. If that is all the Sales Executive would be able to see-

..Knowledge s ..Bill ..Marketing ..Hillary ..Opinions and view ..Data usag ..David ..Asking qu

..Hillary ..Knowledg ..Lead gen

102

..Sales ..Storytellin ..Data repr

103

..Opinions ..David

104

..Opinions ..Bill

B: The Sales Executive would also need to know that web is weighted by 30 D: Do we tell the sales Executive that he has downloaded this report?Â

..Asking questions ..David

105

..Knowledge sha ..Bill

106

..Integration ..Development /

B: We are not yet aware how the two systems would be integrated. Right now Pivotal is in real-time, while the remaining data would be transferred in batch views.

H: It should just be possible to access these dashboards through Sitecore.

..Opinions and views ..Hillary ..Sales

107

D: We need to get one of the people who represent Sales to be part of this discussion.

108

H: The process has been talked through with T in great details.

109

D: I am mainly thinking about the Sales Executive who need to use this view with a score on 50 in great detail.

110

When he (the Sales Executive) calls the customer, what should he say? Which words would he use?

The only thing that gives the SE an image of what he should say is the gathered score from the web activity.

..Opinions and view ..David ..Decisions ..Knowledge sharing ..Hillary ..Opinions and v ..David ..Data usag ..Sales ..Opinions ..David ..Asking qu

111

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..Knowledge s

112

B: From this [looking at view 2] we are able to see what he has been doing on the web platform, e.g. if he has downloaded a Service Guard Report.

113

H: From this image an experienced Sales Executive would be able to see what should be discussed when calling the customer.

A: When this person has taken a service test and downloaded these documents, he would be considered to be in a general interest of what is going on in the company/industry.

..Bill ..Data usage ..Storytellin ..Sales ..Hillary

114

..Opinions ..Knowledg ..Ann ..Knowledge sha

115

..Lead generatio ..David ..Opinions and v

116 117 118

..Hillary ..Knowledge sha ..Bill ..Knowledge sha Project (Plan / S

119

Data manageme ..Hillary

120

..Knowledge sha ..Knowledge sha ..Ann

121

..David ..Asking questio ..Storytelling

D: Now I often consider myself to look at things from a practical sense. What is the difference between what we have today and this solution?

H: It has been connected B: Today there is no place that connects it all H: And today we have not prioritised the data inputs. Are some of these customers more interesting than others? We are not able to see this. There is no ‘ping’ that makes us aware of these key customers.

With this solution, he will get a collected score where we are able to see what would be relevant to talk to him about.

A: It is not only a change in how we will be able to do Sales, it is also relevant for the Marketing department. They have no such tools today.

122

D: Are we able to see which of the more than 20 e-Seminars he has looked at?

B: Yes, that is possible to see right there [pointing at slide]

123

D: Don’t mistake my intentions. I just want to understand it.

124

B: This is not an academic challenge. We are aware that the Sales Executive should be able to actually use it.

125 126

D: What are we calling this?

B This is the dashboard

127

D: I understand the value of connecting it all… But there are a lot of unclarities.

128

B: We are making the presumption that a score is automatically

..Data usage ..Bill ..Knowledge sharing ..David ..Opinions and view Important reactions ..Culture / Organisation ..Bill ..Opinions and views ..David ..Asking questions ..Bill ..Knowledge sharing ..David ..Opinions and v

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..Storytelling

build based on interest and actions. When a customer gets the score of 60, the SE will get a ‘ping’ and the lead is afterwards sent to the Sales person.

..Data usage ..Knowledge sha ..Bill

129

..Knowledge s ..Asking ques ..David

130

D: We are also gathering data in MACD (Marketing Account Cockpit Data). What about this?

H: That is loyalty data.

131 132

H: We have x amount of services that are placed separately.

D: Is this a parallel for this platform (MACD)?

133

B: This is not something that the Salesperson should look at before a lead become relevant.

134 135

B: This gives us an overview of all leads MACD gives the Salesperson an idea about the current engagement of customers

136 137

A: It sounds more like MACD is used for campaigns This is more relevant and dynamic over time.

138

H: MACD is part of his sales qualification. So this is where he will look when he is about to make a call.

Right now the SE’s have no access to the full picture.

Data management / Arch ..Data origin / sourc ..Hillary ..Knowledge sharing ..Hillary ..Knowledge s Project (Plan ..David ..Asking ques ..Lead genera ..Data usage ..Opinions and views ..Bill ..Sales ..Lead g ..Data usag ..Opinions ..Bill

139

..User enga ..Opinions and views ..Ann

140

Project (Pla ..Sales

141

..Data usag

142

..Knowledg

D: I think we will have to restructuct this big time [talking about the lead handling approach in general].

I think there is a problem in that we are developing too many tools.

We need for all of this to be anchored in the organisation.

..Hillary ..Lead genera

143

..Lead genera Project (Plan

144

..David ..Opinions an

145

..Culture / Org ..Data usage

H: I believe that if you have used one tool, you will become better at using the next tool.

I see this as we are putting more tools down your toolbox so you (the SE’s) will become more qualified at doing their job.

D: We could make this so it will be embedded in the other dashboard or that the loyalty data will be placed next to the scoring.

..Hillary ..Opinions and view ..David

146

H: When it is presented inside Pivotal, more information 14/29

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147

becomes visible.

D: We need to consider how this is going to be presented.

..Data representation ..Knowledge sha

148

..Hillary ..David ..Opinions and view

149

D: We are giving the SE’s a tool that is useful for the first qualification, but after this there is the after-qualification by the SE’s. They are responsible for qualifying the leads.

B: Yes, we have this funnel for lead qualification.

..Sales ..David

150

..Opinions ..Lead gen

151 152 153 154

..Bill ..Knowledg ..Sales

D: The whole thesis about customer loyalty is how is should generate sales.

We have a current plan stating: • Yearly touch point analysis (driver analytics) • Daily sales optimisation Are we now adding another bullet point to this list?

..Data usage ..User engagement ..David ..Knowledg

155 156

..Opinions ..Asking qu ..Hillary

H: It is important that we are not going to confuse the whole sales situation.

B: We are basing all this on what the customers are doing. The Sales Team are not made aware of the customers before they reach a certain score. And this score is based on customer interaction.

..Opinions and views ..Sales ..Bill ..Opinions an

157 158

Currently, most of the actions are driven inside-out Now, we are turning it around: outside-in.

159

B: If we decide to use MACdata in some way, we are focusing on a specific group of people.

What we are doing here is that we have gathered an overview which we can use independently of campaigns.

..User engagement ..Lead generation a ..Value setting (sco ..Sales

160

..Lead generation a Important reactions ..Opinions and view ..Bill ..Sales ..Opinions and v

161 162 163

..Bill ..Opinions and v ..David ..Bill

164 165

..Opinions an ..Sales

166

..Hillary ..Opinions and v

167

D: I believe that is a very valid point.

B: I think so too. But I acknowledge that we need to be more clear when we approach the process itself.

H: Yes, we have to think about how we are able to make it approachable. I will gladly take T on a journey to the sales teams during the autumn.

D: Before we get this far, we need to talk this through.

We need for this to be anchored in Insight Sales so the methods are well-considered. This needs to be anchored within the whole organisation.

D: My starting point is that this should make sense for the people who are going to use it.

I understand that it would be too extensive, if we should be

Project (Plan / S

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allowed to go into details.

..Data usage ..Opinions and view ..David ..David

168

D: Let me see some of the next slides

169 170 171 172 173

[Next steps] H: Next: • The setup should be developed fully by C and DB. • Design. How should it look?

• And testing of course

174 175

D: To develop a webscore.. Is this a big task?

B: This is already done. It has been identified based on hypotheses about user interactions which has been embedded into the Sitecore environment. That is part of the web project.

D: So this is not Google Analytics?

B: No, this is our own infrastructure. This is more like a one-onone Google Analytics, where we are able to dig into details to a larger degree than usual.

..Development / Impleme ..Hillary ..Knowledge sharin ..Data representatio ..Validation / Testin ..Asking questio

176 177

..David ..Value setting (s ..Bill ..Knowledge sharing Project (Plan / Scope) ..David ..Asking questions

178 179

[5]

180 181

H: Lead handling is directly embedded into this Content generation is part of the operations.

..Data usage ..Knowledge sharing ..Bill

..Knowledge sha ..Hillary

Project (Plan / Scope)

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182

..Knowledge sha

183

H: This is the only concrete idea we have about this currently.

184

D: Can you put on some words about how this will look in practice?

H: This is basically the same format as we have not. However, the template will look different.

..Hillary

..David ..Data usage

185

..Asking questions ..Hillary ..Opinions an

186

..Asking ques ..David

187

..Lead genera ..Sales Project (Plan / Scope) ..Bill

D: Today we have another form of leads. What is the difference between those and the new type?

B: We should call these leads Marketing QualiďŹ ed Leads, because it should not be regarded a lead before the Sales Team has taken a look at it.

188

..Opinions and views

..David

189 190

..Asking ..Sales

191

D. [Pointing at the green element to the right] From here we are assuming that there has been a call to the customer?

H. Only if we estimate that it is necessary to call.

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..User e

192

I am not that aware of the processes among the Sales Team. I guess that sometimes they know their customers beforehand.

And then they are able to qualify their leads even more using the tools they are already in possession of.

193 194 195

H: This is what we need to define in detail together with F and J. B: This is based on the first sketches we made together with J The deal with J was that we would meet with her to revise this model further, when we had a clear overview of the score inputs.

196

D: Right now we have some issues with how leads are used in the organisation. Everything and nothing is currently used. This solution might take over the current (failing) processes.

We might need to prioritise this.

How about new leads from the website. Will they have to go through this process or are they able to skip some parts?

B: They are not going directly to this process at first.

D: We need to look at this from a more unified perspective. And we should not be scared of changing the current processes.

I would like to see where our opportunities are derived?

When we have a lead-form, we could add a field for mentioning where the lead comes from.

H: We have that today, but it is not mandatory D: That is why we are here today. To talk about these things and to define what should be mandatory.

..Data u ..Hillary ..Opinions and views ..Knowledge s ..Hillary ..Knowledge s ..Knowledge s ..Bill ..Sales ..Involvement / O ..Opinions and v ..Bill

197 198

Project (Plan / S ..David ..Opinions and v

199 200

..Asking questio ..Bill ..Knowledge sharin ..Processes

201 202

..Opinions and v

203 204

..David ..David ..Asking questio ..David ..Opinions and views

205

D: Do you have more slides?

..Knowledge sharing ..Hillary ..Culture / Organisation ..Opinions and views ..David ..David ..Asking questions

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206

..Knowledge sharin ..Hillary

207 208

H: Yes, we have some data qualification.

B: Now we have to map what should be created.

209 210

B: Data compliance: this is a tiresome, but necessary task.

D: Yes, that is to a large degree my ‘fault’. What is needed in terms of this is leadership. The reason we have not worked on this part yet, is the missing initiation and focus on the task. This needs to be improved!

B: Yes, however, it should not be considered a barrier for us to get started on the other phases.

Project (Plan / Scope) ..Knowledge sharing ..Bill ..Bill ..Knowledge sha Important reactio ..Opinions and view

211

..David ..Culture / Organisation ..Bill ..Opinions and view ..Sales ..David ..Opinions and view

212 213 214

Project (Plan / Scop ..Hillary ..Opinions and view ..David

215 216

..Opinions and view

..Hillary ..Knowledge sha

217

D: When we are talking about rolling it out, we need to define what we are talking about.

And we need to get T in on the project. We need to do the legwork at first.

You need to be able to talk on the foundation of your matrixmembers. Either you need to get out to them or you need them to get into this office.

H: We are probably not capable of getting all the Sales Teams in here, but maybe the Marketing and Sales Executives.

D: Now T is not present, but I would like to elucidate that the core of this project is here [pointing at Insight Sales]

H: The GMs was given an update a few weeks ago, but they should not be involved again before we have something solid to show them, such as how the dashboard is going to look.

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..Culture / Organisa ..Opinions and view ..David ..Opinions and view

218 219

..Hillary

..Opinions and views ..David

220 221

..David ..Lead generation a

D: Hmm. I would generally say that it is better for them to be as involved as possible.

H: I would argue that it is important to show them something concrete each time. But other than that, I agree.

D: J will have to make a template or something similar for the Insight Sales.

And we still don’t have a distinct job description for how we should work with leads.

..Knowledge s

222

..David ..Asking ques

223 224

..Hillary ..Opinions an ..Sales

225

Project (Plan

D: And then you mentioned [towards B] that there is not anyone who uses the MAC-data.

H: I wouldn’t say that..

D: Well, I think you are right about that. And we have to bear this in mind.

Even though you are probably not too fond of it, I think we need to have some KPI’s included.

..Culture / Organisation ..David ..Opinions and v

226

B: It is important that the Insight Sales are qualified.

227

D: When we are working with funnels, we actually have a lot of different sales processes going on: • one of these is project-oriented • one is customer-personalised • ...

..Bill ..Opinions and view ..Sales ..Lead generation a ..David ..Knowledge sharin

228 229 230

231 232

[Looking at Lead Handling again]

233

D: Well, this is the REP. The REP also has the responsibility for

..Lead generation and ha

..David

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234 235

..Knowledge sharin

..David ..Opinions and view

236 237

The A-customers And the transactional: Insight Sales

238

Here, I believe we actually need a model.

B: It has not been defined, but we probably need a ‘Large Account’ / 'Key Account' D: We probably need some engineering to make this.

239 240 241 242

B: Yes, we are very aware of this D: But we need to anchor this within the organisation.

When we reach the end, we need the to use it!

Right now they are doing a lot of things they shouldn’t.

243 244

D: That’s it?

—

245

H: Yes, but then I would like to talk about this again. The budget.

..Lead generation a ..Opinions and view

• •

..Bill ..Opinions and view ..David ..Bill ..Knowledge sha ..Data usage ..David ..Opinions and views ..Culture / Organisation ..Asking questions ..David ..Hillary

246

..Budget

..David

247

D: Yes, I understand that. Can you go a few slides back? Yes, that one.

..Opinions and view

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248

..Asking ques ..David ..Hillary ..Bill

249 250 251 252

Is this what we need to build?

H,B: Yes D: And this is the solution E is comfortable with as well?

B: Yes, there has been very positive feedback from IT as well.

253 254

D: I had the impression that there were several solutions?

B: Yes, originally there was. But in reality, we see only one solution.

As E focused on, the solution F proposes there will be no documentation for and no stable and efficient support afterwards.

Further, if it should be working optimally, we would need to buy a server at a price range of several hundred thousands. F actually said to a meeting yesterday that "with these conditions, I won’t be able to solve this”.

..Knowledge sharing Data management ..Developm ..David ..Asking qu

255

..Involveme ..Decisions ..Bill ..Knowledg ..David ..Opinio Project

256

B: Originally we got a proposal from WB, which was way above what we expected.

257

D: How has this changed? How did we end up with this solution?

B: After we received the first proposal, we had a talk with C. He

..Bill ..Knowle ..Culture ..Data s Data ma ..Securi

258

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APPENDICES

told me than he basically had built all the neccesary algorithms.

So all that was needed was a stable host. For this, he recommended DB 259 ..Decisions Important reactions

260

D: Ok. The thing about the BI platform. How are we going to do this?

H: With this setup we are able to integrate it afterwards.

..Knowledge sharing ..Bill ..Developmen ..Algorithms ..Asking ques

261 262 263

..David

D: What is it they [DB] are actually building?

B: They have a DW. Together with R they are going to setup the right algorithms.

And the way they are doing this is founded in security measurements that GB (E) has defined.

..Business Intellige ..Knowledge sharin

264

..Hillary Data management ..Asking questions ..David ..Bill ..Knowledge sharin ..Development / Im ..Security Data management

..Value setting (sco ..David ..Asking ..Data o ..Knowle

265 266 267

D: These four boxed, are they defining all the scoring parameters?

B: What you might miss from this drawing would be WebEx, but that will become integrated through Pivotal.

All the scoring parameters we have defined has been integrated using this architecture.

..Bill ..Value sett

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268

..Opinions ..David

269

..Opinions ..Hillary

D: Basically, it is not appropriate that we have WebEx integrated in this setup.

H: It actually doesn’t matter that we have to access the data through Pivotal, since we are going to find another solutions for e-Seminars soon.

..Developm ..Integratio ..Bill

270

B: And that is actually the same case we experience with Apsis.

This is going to be integrated through the web for now.

271

H: What is interesting with the e-Seminars is that there is not any integrated solution available at this point.

272

B: Optimally we would only have three data sources (Ln, Pivotal, Web) A: But the benefit of this approach is that we are able to extend the amount of data sources, whenever we need to.

..Knowledg

..Hillary ..Knowledg

..Bill ..Opinions

273

..Ann ..Opinions

274

..Data origin / source

..Asking questions ..David

275 276

D: What are these shortenings?

B: I actually don’t remember the specific terms, but basically it covers- [describes the process again]

277 278

D: There was originally something about ‘clusters'?

B: Yes. But this is not part of this solution. It would typically be something for the BI platform.

But as we are bringing historical insights into the architecture, it would be possible later on to cover this aspect as well.

..Culture / Organisa ..Knowledge sharing ..Bill ..David ..Asking ques Project (Plan ..Bill

279

..Knowledge sharing ..Business Intelligence Data manage

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..David ..Asking qu

280 281 282

D: What if we are not going to finance this solution?

B: Well, then we won’t have any scoring Then we would only have inputs in Pivotal without any scorings and insights on the leads.

283

A: You will still have some kind og score on the web platform, but it will be isolated to web behaviour

284 285

B: So it would only be useful for Marketing, So we will not be able to tell Sales when there is a lead. We can not see if he has attended a e-Seminar, whether he had downloaded anything etc.

A: We will be able to see that he has signed up, but that’s it.

..Budget ..Bill ..Knowledg ..Value sett Project (Pla ..Knowledg ..Ann ..Market ..Bill ..Opinions and view

286

..Knowledge sharing ..Lead generation a ..Sales ..Knowledge sharin

287 288

D: Well, I am not shocked regarding the price range B: I was very relieved as well, especially after seeing a number that was ten times as big.

289

D: However, this does not change that we don’t have the funds at this point.

B: So what are you suggesting?

H: Our suggestion is that we ask DB to design the architecture for the proposed solution. But this demands a budget.

..Ann ..David ..Opinions and view ..Bill ..Opinions and view ..Budget

290 291

..Knowledge sharing ..David

292

..Asking questio ..Bill

293 294 295

..Opinions ..Hillary ..David ..Asking qu

296 297

Project (Pla

D: Let us say that we have the money. What would the process be henceforth?

H: • Then the test-data will be sent to DB • Afterwards DB, C and F will talk together about the scope • DB will setup the system and implement the processes • Me and J will sketch how it is going to look in Pivotal

..Knowledge sha ..Hillary ..Decisions

298

I would actually believe that they are able to setup the whole architecture before we have defined how the end-result should look in Pivotal.

299 300

H: Then there is a new TIP [TIP]

..Data stru ..Processe ..Data representatio Data management ..Hillary ..Opinions and views ..Data representation ..Hillary ..Knowledge sharing

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301 Project (Plan / Scope)

..Hillary

302

[H describes the process]

303

D: We need to consider how we are going to involve Marketing and Sales Before we are going to say yes to this project, we need to have some sort of ‘workshop’ where Paula and J will look at the proposal and say yes.

H: I don’t think we need that for the current project, but for the continued work.

D: But I need for J to be able to ask all the annoying questions D: Where is P at this overview. Oh, there - 'Content Generation Process’.

B: I share your point of view in terms of involving and giving J and P ownership.

D: And then there is the Customer Experience Manager who is starting in april..

D: We need to consider the ressources we have at our disposal

..Knowledge sharing

..Marketing

304

..Sales ..Involveme ..David ..Opinions and view ..Hillary ..Opinions and view ..Decisions

305 306 307 308

..Opinions and view ..David

309

..Asking questions ..David

310

..Opinions and views ..Bill ..David

311 312

..Opinions and view ..David ..Asking questions ..Bill

313

What do you think, when you are saying content?

B: they need to deliver content based on the filters on our website, e.g. materials and industry specific content. I believe we need to be better at estimating when a customer is positioned within a relevant industry.

D: This plan needs to be defined more in details

..Opinions and views ..Content ..David ..Opinions ..Opinions

314 315 316

..Knowledg ..David ..Decisions

317 318

..Decisionmaking

P has 30 employees throughout the world who needs to be involved in order to take action on this.

So this needs to be talked through.

We need to establish what needs to be done, who are going to do it and how many ressources are needed.

We need a plan.

D: I believe that H expect to receive content from P - and this process should be illustrated.

Project (Plan / S ..Opinions and v

319

B: It should be defined together with J, P and her matrix26/29

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..Opinions and view

320

..Bill

department.

A: It is very much connected with what is illustrated on the site right now,

..Involvement / Ownersh ..Decisions ..Opinions and view ..Ann

321 322

..Opinions and view ..David

323

D: Let’s focus on the automobile-segment instead of trying to focus on everything.

B: These are two different aspects, right? Content and lead handling?

H: Yes

..Content ..Bill ..Asking qu ..Hillary

324 325

D: We can not initiate this without involving J We should not start any of this before everyone has been agreeing on the premises.

326 327

The product-executives should be included as well.

D: It is the process that is going to make this project ‘live’. Is this ready?

A: For the operations? Yes. D: It is the operations that are going to make this project solid.

..Knowledge sharing ..David ..Opinions an ..Involvement / O ..Decisionmaking Project (Plan / S ..Decisions

328 329

..Opinions ..David

330

A: I suggest that P’s part of content would be to concentrate on when you are a bit further into the sales process. Marketing would be either in the first part or just at the end of a sales process.

331

D: Christina (new customer experience manager) should lead this.

332 333

D: 300.000 DKK. How are we going to find this?

H: Well, I have some part of my budget that I might not need for my current estimations.

D: We need to stick to the fact that this is a new project. We should get the budget based on these conditions.

..Decisionm ..David ..Asking qu ..Knowledge sharin ..Ann ..David ..Opinions and views ..Ann ..Opinions and v ..Decisionmaking Project (Plan / S

334

..Content ..Sales ..David

335 336

..Opinions an ..Decisionmak ..David ..Asking ques

337

..Opinions an

D: Should we ask a new company rather than DB? H: We have reached a setup which we and the IT guys are very comfortable with. So I would prefer if we stick with this proposal.

A: And if we are just calculating using your internal hourly wage, the money will quickly disappear.

..Knowledge sharing ..Hillary Budget

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338

B: Further, there are not many companies on the market who have the same approaches to security as DB. The whole Citrix architecture is very important to acknowledge.

339

D. Actually my minimum number would be 300.000 DKK - I almost suspect you knew that [saying with a smile] - but we need consensus at first. I think we need to present this to the rest of the management.

340

B: Actually it is not uncommon that you would setup such solution in-house.

But as E has revealed, the current systems in-house are too old and we would need to renew the licenses.

That is important to bring to the table as well.

With this solution we can bring it in-house with the BI platform later on.

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Project (Pla ..Bill

342 343

..Opinions ..Budget ..Decisions ..Knowledge sha

344

..Opinions and v ..David ..Bill ..Knowledge sharin

345 346

D: Well, you could start with a slide stating some of the different ways this could be solved.

H: That would influence our TIP. D: We are just going to push that part.

Project (Plan / Scope) Data management

347

D. Generally, I would say I am very reliefed about what I have heard today.

348

So, I would say - the week after Easter would we be able to meet again?

H: That would be week 14.

..Opinions and view ..Knowledge sharing ..Bill ..Business Intellige ..David ..Opinions and view Data manageme

349 350 351

D: I would ask for a revised TIP and a new 1-pager. And then I will talk to Q before we talk to the management.

I will make a meeting summoning for the rest of you afterwards.

352

--

353

[sub-meetings afterwards about the web project among other things]

354

[Talking about the web project]

355

I will not tell the rest of the management about missing the deadline until we are absolutely sure the new proposal is gonna 28/29 stick. WB needs to be 100% certain that this new deadline is gonna stick. Because I don’t want to go tell them we are late more than once.

Project (Plan / S ..Hillary ..Opinions and views ..David ..Opinions and views ..Culture / Organisation ..Opinions and views ..David ..David ..Asking questions ..Knowledge sharing ..Hillary ..Decisionmaking

Project (Plan / S ..David ..Opinions and v ..Knowledge sharing

156