loupe
Making Knowledge Visible
Report on research into the use of data visualization to enhance user experience in online platforms July 3 2009 Table of Contents
The Social Life of Visualization
2
What opportunities emerge when you mix visualization with the social web?
Social Visualization Patterns
7
How can you design interfaces to leverage social visualization online?
How do teams really work?
18
An overview of Deloitte practices, based on interviews and embedded research in a Growth Solutions team
Online Accounting: 2012 Two scenarios describing user experiences with an online accounting platform, based on field and desktop research
1
20
The Social Life of Visualization
1. Tim O'Reilly on data as the 'Intel Inside' driving web 2.0 http://www.oreillynet.com/pub/a/oreilly/tim/ news/2005/09/30/what-is-web-20.html?page=3
Data visualization is emerging as one of the hot topics of 2009. Data itself is rapidly becoming a driving force on today's Web 2.0 powered Internet, or as Tim O'Reilly has put it, data is becoming the 'Intel inside' for today's World Wide Web1. Sites like data. gov are about to make huge amounts of it available as part of an effort for the United States government to be more open and accountable, and no doubt other governments and nongovernment organisations will follow suit and make exabytes of useful data available to anyone who cares to look for it. In order to make sense of all this information, it will help to be encapsulated in the medium of choice for presenting data; visualization. Data visualization has been around for a long time. Indeed some of its earliest users were the medicos John Snow and Florence Nightingale. Snow visualized the data from cases of cholera in Soho, London in 1854 to identify the source of the outbreak, which was found to be a public water pump. Nightingale visualized data from the causes of death of soldiers in British army field hospitals in the Crimean War to argue to the authorities back home that better hygiene and standards of care could prevent otherwise necessary deaths. In both cases these early employers of visualization were using it to tell stories from the data; Snow to tell the story of where the cholera was coming from and Nightingale to tell the story of what better sanitation in hospitals could do the life expectancy of British soldiers. Since the advent of spreadsheet programs like Visicalc and Lotus 1-2-3 in the 1980s, it's been possible for most users to easily tell stories about the data they've collected. Information entered into any spreadsheet can be visualized using some stock standard techniques like bar charts, pie graphs and scatter plots and these can be used in reports and presentations to present insights into otherwise myriad data, and consequently tell stories about it in much the same way that Snow and Nightingale did.
Shared Storytelling However this has always been a uni-directional exchange between human and computer. Human enters data into computer; asks computer to visualize it; computer outputs the result. From this uninspiring interaction an artefact is created that can be used to tell a story about the data. Then again, this story is just an individual perspective of the data, and history demonstrates that storytelling often works well as a shared process. This has been demonstrated through cultures of people who use oral storytelling to pass on their histories, and in the contemporary setting of the watercooler, where news, opinion and gossip is exchanged. Previously we had no computer-based analogue for the type of interactions found in these examples of oral storytelling, but this has all changed with the growth of the social and participatory web. It already powers tremendously rich sources of information like Wikipedia, which is now so ubiquitous that it recently forced the long running multimedia
2
encyclopedia Microsoft Encarta out of business. The process that Wikipedia employs is known as collective intelligence and although the end product is achieved through consensus over what individuals might know about a certain topic, there is also much knowledge to be found in the areas where this is not achieved. Or in other words, where dissensus exists.
Making Sense, and Sensemaking This is the basis of the process called sensemaking, that challenges the Western ideology that everything must have a right answer. Sensemaking is the human ability to make sense of an ambiguous situation, or a motivated, continuous effort to understand connections in order to anticipate their trajectories and act effectively2. It's the very process that John Snow used to find the outbreak of cholera. He was not convinced by the pervading belief that the disease was spread by noxious air and by using a data visualization of where people had died he was able to prove that the disease was spread
2. Klein, G et al. (2006), "Making sense of sensemaking 1: alternative perspectives" in IEEE Intelligent Systems 21.4., pp.70-73.
by contaminated water. This was only one person's challenging of the status quo, and a collective challenging or community sensemaking approach is a more powerful force. The challenge then is constructing an interface around visualization so that the processes of collective intelligence and sensemaking can take place, and storytelling through this medium can become a shared experience. To encapsulate shared storytelling through visualization the ACID Loupe Project has developed a set of interaction design patterns, which as Molly Wright Steenson wrote in Interactions March - April 2009, create some replicable frameworks and building blocks for designing systems that better afford these kinds of shared experiences. We will refer to these patterns periodically, and for explicit details on how to implement them please go to our website at http://socialvizpatterns.info/
CREATE
001101011100110101 110101110011010101 011100110101010111 001101010101110011 010101011100110101 010111001101010101 110011010101011101
INTERPRET
CAPTURE
TWEAKING
n MAPPING
SNAPSHOT
DECORATION
ANNOTATION
The Social Life of Visualization This diagram visualizes a journey from data to storytelling, involving processes of: Mapping: where communication objectives are translated into visualization approaches Decoration: creating an identity around the visualization and placing it in the social space Tweaking and Annotation: interfaces to interrogate & mark up the representation of data Snapshot: enabling storytelling to grow around a data visualization
3
Object and Ego Centered Networks At the basis of the Loupe Project's proposal is the distinction between an object-centered social network and an ego-centered one. The idea of object-centered social networks come from the work of sociology professor Karin Knorr Cetina's theory of object-centered sociality. The work establishes the individual and the object as central elements in social interaction. Cetina proposes that objects, around which discussions takes place, help focus and start conversation and other social interaction between and among people. In this case visualizations are the objects within the network, and because it prioritises these rather than the relationships between people in the network, it focuses attention on the process of shared storytelling.
Decoration Of course for the process of shared storytelling to occur around an object (which in this case is a visualization) , it needs to have an identity within the social network so that it can be easily recognised and its unique properties can be retained. Identity in this context is created in much the same way that it is created in other scenarios; through a combination of visual and textual information about the object. Providing these contextual clues about the object ensures that people within the social network can easily locate it and establish whether or not they can make contributions to it. The specifics of giving a visualization an identity within the network is found in our pattern called decoration, and through its use the object becomes ready for the process of shared storytelling to occur around it.
n
Mapping However the process of shared storytelling through visualization can only take place when there is a shared understanding of the properties of the medium. Visual communication does not have the same level of shared understanding that written communication has. So in addition to presenting the visualization to begin the process of shared storytelling, a visual language has to exist that is well understood so that the properties of the data can be communicated. When Florence Nightingale presented her statistics on mortality in her field hospital during the Crimean War, she used a coxcomb, equivalent to a modern circular histogram or rose diagram, and she understood that this would be an appropriate way to communicate this data to others because she was gifted in mathematics and could talk through the data in person. People within a social network may not have the necessary knowledge to understand what sort of visualization technique to use but may still have interesting data to share. So the shared storytelling process has to begin with the creation of the visualization itself and people wishing to share their data need to be guided through the process of choosing a visualization technique. This is in case they don't possess the necessary knowledge themselves to begin that conversation. This process has been encapsulated in the pattern of mapping which can help people to visualize a dataset so that a shared conversation around it can begin. The conversation around visualization takes place because it has become a social object within the network. A social object is anything around which discussion takes place. A movie is a social object because it has a plot, cast, crew and a mise en scene, all of which can be discussed by fans, critics and other interested parties. On the other hand a visualization is a social object because it is a representation of a data-set.
4
Tweakability Consequently it is really the data-set that is being discussed and a set of tools needs to be in place that allows it to be explored using the visualization as an interface to this process. These tools should transform the data-set in some way, whether it is by flipping the axes to gain a different perspective on the data, or by mapping some new axes onto the visualization to look for correlations in the underlying data. Implementing this process within the social network is explained in the pattern of tweakability, which reasons that users need to shift and reformat a visualization in order to make sense of the whole dataset.
Annotation Users within an online social network also need a way of documenting their sensemaking activities, and consequently a simple form of annotation needs to be used so that people can use the visualization interface to highlight the insights they have drawn from the data-set and continue the storytelling process. In the model that the Loupe Project is proposing, this will be a non-disruptive form of annotation that will leave the original visualization in tact and make the annotated version a derivative. This type of process maintains the analogy to storytelling, where an original story might be fleshed out on subsequent tellings, but it remains clear where the derivation came from. In exactly the same way, the visualization interface needs to make it clear what the original visualization was and its intention, yet support derivations of this. The design implications for this are explained in the pattern called annotation.
Snapshot These annotations on the story need a way of being captured to retain the benefit of the community driven story telling process around the visualization. Just as the original visualization is a capture of the underlying dataset, subsequent versions of the visualization need to be captured and attached as well to preserve the benefits of collective intelligence. So another part of the model is having processes built into the interface that allow annotations to be preserved, commented on and subsequently reviewed by other members of the community. Consequently, it is this process within the Loupe Project's proposal that provides shared storytelling through visualization with the ability to create knowledge artefacts around data. The pattern to implement it is called snapshot, and with this, extra data created around the visualization is preserved. Significant amounts of data can be generated, because the entire process of shared storytelling through visualization is a lifecycle that repeats infinitely. As data becomes increasingly prevalent on the World Wide Web, it is the ability to tap into the processes of community sensemaking and collective intelligence that is a more useful experience for people than new methods of visualizing data. For designers, this is a change in approach towards visualization; no longer is it about making the most visually appealing and sophisticated representations. Instead this creativity should be constrained to giving back people control over the manipulation and control of their data, and providing a good experience along the way. John Snow and Florence Nightingale were just two people who told important stories with data visualization, but with these technologies discussed it's impossible to imagine how many important stories will be told by groups of likeminded people in the future.
5
SNAPSHOT
6
PHOTO: W
Social Visualization Patterns Visualization can take complex data and present it in a way that makes it easier to understand. As the amount of data available grows exponentially, and the average amount that a user has to deal with on a daily basis goes the same way, it makes sense to present it in a medium that makes it more simple to understand. This makes it easier for users to ask questions of the data, and consequently gain new insights from it, that may not have been possible when it was in textual form. Creating visualizations within a social space opens up the possibility of collective intelligence occurring. This is a process where the strength of many contributions help to create an artifact that is greater than if it was created by one individual. Knowledge generation in this environment is created by debate and consensus, and in turn these processes create added information along the way. This pattern library explores the idea that visualizations can be social objects (which are any thing within a social network that interactions can occur around), and collaboration can occur around them. Some social networks exist around people and their relationships, while others exist around objects and their affordances. These are known as object centered social networks, which comes from the theory of object centered sociality. In this case we are providing the templates for building a social network around visualizations.
7
Mapping One of the most common problems that users experience when they present a dataset as a visualization is that they don’t always know the best visualization technique that fits with the data and achieves their communication or analysis goals. Users need to learn the inbuilt strengths and limitations of different visualization techniques and how these might fit onto the data they are seeking to present. Use when People need to choose the most appropriate way to visualize a dataset.
Solution Help the person determine their analysis or communication goals and then suggest a visualization approach that maps most closely onto their stated objectives and is appropriate for their dataset.
Why Rather than forcing people to concentrate on learning the merits of different visualization approaches (which can seem esoteric), guiding them through their communication and analysis goals helps people to focus on what they already know about their data and context they want to present it in.
How Attempt to determine the communication or analysis goals the person has for their data visualization, including: •
who they will be sharing the visualization with?
•
what kind of data they will be visualizing?
•
what outcomes they want the visualization to create?
Based on these factors, suggest a visualization approach for the data, explaining why that approach is best suited to their goals. Also present a range of other visualization approaches to the person, stressing their individual strengths and weaknesses.
Issues This requires users to have a good understanding of the original data to be able to choose an appropriate visualization approach that communicates the dataset in the visual medium. An alternative approach by Many Eyes Wikified automatically chooses the visualization approach by performing a textual analysis on the dataset and choosing the best approach based on keywords (e.g. world uses a world map – heat map visualization), thus eliminating this problem.
http://www.flickr.com/photos/juhansonin/407874864/
8
Many Eyes provides many different visualisation approaches and groups them by headings such as ‘analyse’ and ‘comparison’.
Dan Roam describes a framework for choosing what visualization approach to use in On the Back of the Napkin (2007)
9
Decoration People need to attach visual meaning and identity to a visualization so that it can exist within an object centered social space and its meaning can be quickly transferred to others. Use when Creating a visualization within a social space for the purposes of attaching an identity and communicating the meaning of the object in a visual way.
Solution Let people integrate imagery and other media into their visualization to better communicate that visualization’s relevance and context.
Why Decorating a visual representation provides it with an identity in much the same way that an avatar provides a user with an identity within a social network. This provides extra information about the visualization to other users, and contextualizes its place within a social space. In turn, this objectifies the visualization and allows it to exist on its own within the social environment. It also reduces the cognitive load on other users, and allows the inherent meaning in the visualization to be communicated and consequently transferred to the community with greater ease.
How Integrate with the search APIs of user generated content communities to access images and media that relate to the content of the visualization.
Issues Assigning absolute meaning to media can be tricky, and often fails to communicate effectively across different cultures. People can ‘read’ images and media very differently.
http://www.flickr.com/photos/sindesign/298786928/
10
Swivel queries Flickr with the chosen title of the visualization to provide images that can be used as a background for the visualization
People use this to help communicate what their data set is about
If they do not choose an image to give their data some identity, a default image is used
11
Tweakability Users need a way of shifting and reformatting a data visualization so that they can make sense of the whole data set by understanding how it responds to dynamic changes. Use when •
One or more of the visualization parameters is variable (eg profit margin, unit cost)
•
One or more of the visualization parameters is ordered (eg time, scale, amount)
Solution Instead of making the visualization a snapshot, make it an interface that lets a user playfully explore the data. •
Create ways for users to change how a dataset is represented in a visualization allowing the impact of any changes they make to be immediately reflected.
•
With ordered data, allow users to sort the data. eg: by labels, values and data order.
•
Give users the ability to reconfigure a visualization schema. eg: swap the X and Y axis on a two dimensional graph.
•
Pay attention to usability when designing visualization interfaces, eg: clearly communicate which parameter is selected, and what visualization elements it affects.
Why Being able to tweak a parameter value and see how it affects a visualization helps communicate the relationship that parameter has to the whole visual analysis. This can help people see trends and make sense of complex datasets more quickly than with static visualizations.
How Build controls into the interface that enable users to perform actions such as resorting the data, excluding certain parts of the data, or changing a variable that reflects the outcome of the data. This can be done through the use of drop down menus, radio buttons, check boxes and sliders.
12
Gapminder enables a user to change or swap axes to look for correlations, tweak other aspects of complex datasets, and play the data over time to see how changes evolve.
13
Annotation People need to comment on, or draw attention to specific elements of a visualization without compromising legibility of that visualization. Use when Wanting to promote discussion of visualization details and sub-elements.
Solution Give people the ability to make annotations that are consistent and are not disruptive in any way to the underlying visualisation.
Why Being able to create non-disruptive annotations adds knowledge to the visualization, the use of non-disruptive annotations means that all members of the community are talking in the same visual language which makes community sensemaking an easier process.
How Instead of giving people a set of drawing, arrow and box tools as can be found in some desktop software, provide them with a single method of annotating a visualization that is in keeping with the visualization approach used (eg. highlight bars in a bar chart, show the height of ranges in a flow graph).
http://www.flickr.com/photos/jakecaptive/49915119/
14
Wikinvest allows users to annotate a company’s share price performance with non-disruptive annotations.
15
Snapshot When people can interact with the parameters of a visualization, they need to be able to store ‘snapshots’ of the visualization in order to communicate their understanding of a specific visualization configuration. Use when Interactive visualization is used to support discussion of a dataset.
Solution Allow people to store and retrieve configurations of a data visualization.
Why Being able to see what another person saw is an important way of understanding what they are trying to communicate. Collecting snapshots along with discussion is a good way to illustrate the evolution of understanding around a dataset.
How When commenting on a data visualization, attach a ‘snapshot’ of what the visualization currently looks like to the comment. When selecting a comment, configure the visualization to reflect the ‘snapshot’ associated with that comment.
Issues Snapshots do not provide a good overview of the insight that a community has extracted from a visualisation. It is necessary to look through each snapshot and comment to get a sense of what has transpired.
16
PHOTO: WWW.FLICKR.COM/PHOTOS/ROLAND/
Many Eyes allows a snapshot of the current visualization state to be saved, and attached to a separate text based comment.
17
How do teams REALLY Work? 1. Young, I., 2008. Mental Models: Aligning Design Strategy with Human Behavior 1st ed., Rosenfeld Media.
During the first half of 2009, ACID researchers interviewed and 'shadowed' a Deloitte Growth Solutions team, to learn more about what a team actually does, and to understand how these actions fit into a larger Mental Model1 of practice. Our research has uncovered interesting practices, notably identifying the role of Senior Analyst as an important lynchpin in the success of a team. We found that social ties were very important not only between team members, but also between teams and clients. We also found team members had many different ways to accomplish the same task or goal, with standardisation and methods being predominately shared with colleagues via informal and social relationships.
mental spaces
ENGAGE THE CLIENT
DATA DRUDGERY
grouped behaviour
Administer Engagement
engage the client
Manage a team
Collaborate
Be Flexible
Process data
Manage Data
Report
detailed behaviours
Set up client account
Explain decisions
Put teams together
Participate in meetings
Work across team boundaries
Adjust records
Review Specific detail in records
Prepare statements
allocate revenue to profit centres
Build Rapport with clients
Know where people are
Review each other's work
Be flexible
back-oďŹƒce work
annotate documents
Generate Reports
allocate cost to profit centres
Meet with clients face-to-face
Review decisions
Share information
socialise with collegues
Get client's data
Track Adjustments
Customise documentation
Estimate budgets
Advise clients
Assign Work
evaluate opportunities as a team
Chase up payments
Inform the clients about their business
Break tasks up
Make decisions together
Work on site/Visit client
Keep track of progress
represent the client to ATO
18
WORK WITH A TEAM
Check data integrity
This research has been used to generate the diagram below. It attempts to communicate the Mental Model of a team member. The model begins at the bottom; with detailed behaviours that are grouped together into more general behaviour. These are then grouped as mental spaces, or meta groups at the top that encapsulate a goal or way of thinking about an aspect of practice. Using these research findings, together with our knowledge of social software and visualization, we developed scenarios to communicate how the Deloitte Digital online platform might be experienced by clients and Deloitte teams. Two of these scenarios, prototyping client and team experiences, particularly around social knowledge production and management, are diagrammed on the next page, and explained in more detail on the following pages.
TRACK PERFORMANCE
COMPLY & PROTECT
UNDERSTAND THE SITUATION
BECOME BETTER
Track performance
Manage personal workflow
Protect the Firm
Protect myself
Understand the client
Analyse Data
Seek knowledge
Learn on the job
Provide assistance to a team
Track project performance
Manage my time
Meet accounting standards
Check for relationship issues
Understand what the client wants
Identify business opportunities
Get training
Learn through observing
Teach others
Track Deloitte’s performance
Check messages
Meet legislation requirements
Get sign off
Understand the clients organization
Identify potential problems
Establish a network
Learn through doing
Counsel Staff
Track staff performances
Manage lodgement list
Manage the firm's reputation
Know your limits
Understand implications of decisions
Visualize for decision making
Stay informed
Get advice
Store information
Respect confidentiality
Evaluate client capabilities & budget
Compare decisions
Request information
Manage query list
Record client's communications
Maintain business records
Look for information
Get help
Maintain personal records
19
Stephen uses Accounts IQ to get a quick overview of the group's current financial position. He also subscribes to a Deloitte news channel for benchmarking and data relating to his industry sector.
Sales are down. Stephen leaves an annotation for Rob on the current sales chart and adds some colleagues into the workspace.
Rob responds to Stephen's question by creating a new visualization based on new external data.
Stephen Fleming is chief financial officer at Australian Apparel, a clothing manufacturer with 30 stores across Australia.
accou iq
All the data from their individual stores and head office is collected each day by Accounts IQ.
OPEN DATA
XBRL
Using this new information, Stephen creates a report for his board that helps them make a strategic decision.
As more clients add their data to the system it becomes easier to find correlations between circumstances, industry sectors, and operating envoronments
products offered to existing and new clients
Rob uses the conversations to look for business opportunities that Deloitte can pursue with Australian Apparel and other clients.
Advice & Decision Support 20
online accounting
Rob is a senior analyst at Delloite
David and Rob notice similarities between their clients.
unts q
They create a dataWatch query that finds correlations between their client's sales and external factors such as worldwide shipping costs.
As innovations are turned into products, the knowledge produced is fed back into the platform, to be used by everyone
Victoria is also working in this area, and notices what David and Rob are doing. She adds her insight to the situation.
They develop the query in to a potential product offering. Victoria and David are analysts at Delloite
thought leadership via new ideas, systems, and practices
Innovations emerge as a result of internal research and collaboration, and move in two directions
2012
The new product is profiled in the client news channel, demonstrating thought leadership and drumming up new business.
Innovation & Knowledge Platform 21
Online Accounting, 2012 Australian Apparel are a clothing manufacturer and retailer, with 30 stores across Australia. They have been a client of Deloitte's for 5 years and have seen their business grow significantly in that time. Their CFO, Stephen Fleming, has been with the company since its inception and has a good relationship with the companies board and the Deloitte analysts, having previously worked for the firm himself before being recommended by a senior partner at Deloitte for the job at Australian Apparel. Stephen signed up for Accounts IQ two years ago, and has found it has made his job much easier. Each store had an existing POS system that now transmits daily sales data direct to Accounts IQ. This has saved Stephen's department a great deal of time - previously they had to collect, collate and send this data to Deloitte manually, and in a time frame that was more like months, not days. BAS statements and other general accounting tasks also seem trivial now - compared to before, the accounts department is rarely asked by Deloitte to send through additional information. Stephen was initially unsure about how compatible AA's systems would be with IQ, but he was relieved to find they weren't required to make many changes. Their existing systems supported XBRL and Deloitte handled the rest. Each morning Stephen logs into his dashboard and is presented with a number of up-todate graphs and summary data. He is able to check yesterday's sales data, which stores are performing better than others, and he has a number of graphs set up that show him yearto-date sales, inventory levels and the year-to-date performance of the company plotted on a line graph. He loves the flexiblity of his dashboard - almost weekly he makes small changes to it, moving graphs and data around and exploring different combinations. This way, the data is always pertinent to him. He also often exports graphs and tables for use in his own presentations to the board.
Advice and Decision Support He also loves how easy it is to start conversations with people around this data. When he notices something, Stephen is able to leave a question for his anaylst just by clicking on the graph or data cell and typing. He is notified of replies to his questions and is able to invite other colleagues to join the conversation. Last week, he noticed there had been a drop in sales compared to the same time last year, and wanted to get some insight as to why this might be. Within minutes his analyst offered an explanation, and directed Stephen to a graph he had created supporting this. Whilst it's fantastic he's able to get a sense of his own companies position so easily, from a strategic perspective he and the board find it extremely valuable to see real-time comparisons of his company to the industry as a whole. Being able to see generic data from other Australian clothing retailers allows them to easily perform benchmarking and judge the impact of important decisions. As an example, they noticed that they were falling behind their competitors in sales in Victoria and were able to target a marketing campaign to the region. The results of this campaign were then easily measurable as they saw their sales increase in line with the industry for that area.
22
Another valuable aspect of Accounts IQ is its access to external and
into an article, and it received the most hits of any article this
global data sources from places like the Australian Bureau of Sta-
year.
tistics, OECD and other data sources such as ASX announcements, Australian Associated Press (AAP), Bloomberg, Reuters, Dow Jones, ATO guidelines, federal and state legislation. . When Australian Apparel began looking for a location for their new factory, Stephen commissioned his analyst to provide some suggestions. With the support of these sources of information, Stephen's analyst created a number of custom reports that contained geo-political information, estimates of shipping and labour costs and a summary of tax laws in a format that Stephen could easily share with board members. Stephen eventually put forward a case for one particular region - a decision the board easily agreed with.
In the time they used to spend inputing and coding data to the old system, the analysts can now create queries within the system. These queries are able to automatically find correlations between different sets of data in the system. Rob, Joe and David work together to create a query that finds correlations between their client's sales, industry trends and worldwide shipping costs. The analysts set up rules on the query that notifies them automatically when certain conditions are met. During the creation of one of these queries, Sarah, who is an analyst from Tax notices the new query in the system and
Innovation and Knowledge Platform
decides to join the conversation around it. She posts a few
Rob, Joe and David are Senior Analysts in Growth Solutions at
comments with suggestions on how it could be more effec-
Deloitte. They all started together as grads and over the past three
tive, and links these comments to conversations he's had with
years have gained their own clients and increased responsiblity. Be-
clients in the past that provide additional insight. The other
fore Accounts IQ, they spent most of their day doing a lot of the tasks
analysts agree that it solves a problem they were struggling
that the system now automates - things like BAS statements,balance
with, and make the adjustments.
sheet reconciliations, chasing up transaction codes etc.They often wonder if they'd still be stuck doing that type of work now if Accounts IQ hadn't been introduced. They have no doubt that it's certainly moved them up the chain faster, and now they get to do more of the fun stuff. As it happens, all 3 analysts have clients in the fashion retail industry. They often find themselves discussing trends in the industry over Accounts IQ. One of the great things about it is that this conversation can happen anywhere. They are able to start a discussion in a central location they all have access to, but they are also able to see conversations other analysts are having between each other and their clients. When the analysts log in, they see new updates to all the conversations that are relevant to them - not just their own. Similarly, all conversaions are searchable, so when a client asks a tricky question they are able to explore Account IQ for a ready-made solution before investing time in creating one. An area in the system also allows them to make direct comparisons between theirs and their colleague's clients. Evaluating opportunities as a team and sharing this information with others is very easy
A week after they complete the query. one analyst gets a notification, flagging something that is relevant to their client. They run some projections based on the output and package this into a view they can send to the client. Through the sytem, they ask their manager to take a quick look over it, and after getting approval the view is automatically sent to the client. The client makes a decision on the query which leads to a demonstrable increase in sales. Other analysts in Growth Solutions notice this and begin to build variations of the query for their own clients. After a number of successes with the same query across a few different industries, a manager decides to propose it as a new packaged service that can be sold. Selecting one or two of the clients in the system as case studies, she is able to easily demonstrate its value to new clients. Their tasks are automatically logged, which is much better than the other system where they would have to keep track of what they did during the day and spend the last 20 minutes of the afternoon filling out time sheets.
- conversations can be held about successful advice and ideas can be generated for applying this advice to all clients. In fact, a popular subscription service that posts business intelligence about the retail industry often generates content from these internal discussions. Just the other day, a conversation Rob and Joe started was turned
23
about Loupe Loupe is a research project exploring the role of data visualization in enhancing user experiences. The project is a collaboration between Deloitte Digital and ACID, involving the following researchers from RMIT University and The University of Queensland Jeremy Yuille Hugh Macdonald Nifeli Stewart Reuben Stanton Chris Marmo Prof. Mark Burry Dr. Bonna Jones Dr. Laurene Vaughan Dr. Stephen Viller Dr. Yoko Akama Vanessa Cooper Jane Burry
about acid ACID is the Australasian Cooperative Research Centre for Interaction Design. We bring industry together with cutting edge research to find better ways for people to interact with each other using communication technologies. Our expertise lies in helping people participate in the digital world. http://acid.net.au
24