A Framework for Teaching Decision Support Using a Business Simulation Game

A Framework for Teaching Decision Support Using a Business Simulation Game

WARANYA POONNAWAT

Thesis submitted in partial fulfilment of the requirements of the University of the West of Scotland for the award of Doctor of Philosophy in collaboration with Stuttgart Media University

July 2017

Figure 4.2: The system architecture of BIsim.

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11/17/2017

Questionnaire for BI Learning Framework - Pretest

Edit this form

Questionnaire for BI Learning Framework - Pretest This questionnaire is designed for the experiment of Business Intelligence (BI) Learning Framework. The questions are about 21st Century skills and BI skills.

Target group is the students who study Business Intelligence at Faculty of Information and Communication, Stuttgart Media University.

It will take about ten minutes to complete. Thank you for your time.

BIA Research Team About us: www.bi-academy.eu Contact us: research[at]bi-academy.eu * Required

What is your major? * WI - Wirtschaftsinformatik OMM - Online Media Management MBA - HdM

What is your HdM email address? * This will be used con dentially. Do not need to type "[at]hdm-stuttgart.de".

Do you think you can work in a team? * 1

2

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4

5

6

7

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9 10

I am not sure

Yes, of course

Do you think you can communicate your ideas? * 1 I am not sure

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8

9 10 Yes, of course

Do you think you can work e cient with digital devices and computers? * https://docs.google.com/forms/d/e/1FAIpQLSdniYgxLyzOnO5L1ftWuJX4KXfwol0voT8Kq5ON5sHNRNy-eQ/viewform

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Questionnaire for BI Learning Framework - Pretest

1

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9 10

I am not sure

Yes, of course

Do you think you can work in an international team? * 1

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9 10

I am not sure

Yes, of course

Do you think you are creative? * 1

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9 10

I am not sure

Yes, of course

Do you think you can think critically? * 1

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9 10

I am not sure

Yes, of course

Do you think you have enough knowledge to solve a new problem? * 1

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9 10

I am not sure

Yes, of course

Do you think you can produce a good quality work? * 1

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9 10

I am not sure

Yes, of course

Do you think you can learn by yourself? * 1

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9 10

I am not sure

Yes, of course

Do you think you can force yourself to work? * 1 I am not sure

2

3

4

5

6

7

8

9 10 Yes, of course

https://docs.google.com/forms/d/e/1FAIpQLSdniYgxLyzOnO5L1ftWuJX4KXfwol0voT8Kq5ON5sHNRNy-eQ/viewform

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Questionnaire for BI Learning Framework - Pretest

Do you think you are able to plan your work? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Are you able to think exible during team work? * Do you accept other opinions, are you to change your direction of your solution?

1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Do you think you are able to think about alternative solutions? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Do you think you can decide under uncertainty? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Do you think you can handle con icts in your team? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Do you think you can take the initiative? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Do you think you can use OLAP tools for decision support? * 1

2

3

4

5

6

7

8

9 10

I am not sure

Yes, of course

Are you familiar with OLAP-based business planning? * 1

2

3

4

5

6

7

8

9 10

https://docs.google.com/forms/d/e/1FAIpQLSdniYgxLyzOnO5L1ftWuJX4KXfwol0voT8Kq5ON5sHNRNy-eQ/viewform

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Questionnaire for BI Learning Framework - Pretest

I am not sure

Yes, of course

Do you think you can design a multi-dimensional model? * 1

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3

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5

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8

9 10

I am not sure

Yes, of course

Do you think you can design an ETL process? * ETL = extract, transform, load 1

2

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5

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9 10

I am not sure

Yes, of course

Do you think you can apply data mining concepts? * 1

2

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6

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8

9 10

I am not sure

Yes, of course

Do you think you are familiar with BI concepts? * 1

2

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5

6

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9 10

I am not sure

Yes, of course

Do you think you are familiar with Self Service BI tools? * 1

2

3

4

5

6

7

8

I am not sure

9 10 Yes, of course

Submit

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APPENDIX 2: POSTTEST QUESTIONNAIRE Part 1: The issues about BI skills that students evaluate themselves how much they think they have.

1. OLAP & Dashboard 2. Business Planning 3. Multi-Dimensional/Data Mart Modelling 4. ETL Process 5. Data Mining

Part 2: Issues about learning effectiveness and content Please rate how strongly you agree with each of the following points about learning content: 1. I have engaged with my own data.

2. I have engaged with my own decisions.

3. I have experienced using BI tools to support my decision-making.

4. Realistic business data set 5. Real tools 6. Suitable case studies 7. Realistic business scenarios 8. Meaningful experiences 9. Opportunity to refine my decisions 10. Closed-loop scenarios This approach provides several characteristics and features which support my learning outcomes about learning effectively.

1. I can apply the acquired knowledge and skills to different assignments.

2. The results reflected how good I am at making-decisions.

3. The results reflected how good I am at using BI tools.

107

4. With all the assignments together, I have a better understanding of a whole BI project.

5. Better Business knowledge skills 6. Better Communication skills 7. Better Decision-making skills 8. I have learned a lot more with this approach (the closed-loop BI learning framework) than the traditional approach (Lecture + Hands-on).

9. BI Maturity Model

108

The Likert Scales for the Posttest Questionnaire for the Control Group

109

11/17/2017

Questionnaire for BI Learning Framework - Posttest

Edit this form

Questionnaire for BI Learning Framework - Posttest This questionnaire is designed for the experiment of (the closed-loop) Business Intelligence (BI) Learning Framework comparing to the traditional approach (Lecture + Hands-on).

The questions asking about how you have experienced learning with the traditional approach (Lecture + Hands-on).

Target group is the students who study Business Intelligence at Faculty of Information and Communication, Stuttgart Media University.

Thank you for your time.

BIA Research Team About us: www.bi-academy.eu Contact us: research[at]bi-academy.eu * Required

What is your HdM email address? * This will be used conďŹ dentially. Do not need to type "[at]hdm-stuttgart.de".

Term of study * 1 2 3 4 5 6 7 Other:

Please rate how strongly you agree with each of the following statements: * 1 Extremely disagree

2

3

4

5

6

7

8

https://docs.google.com/forms/d/e/1FAIpQLSfyWGlbNk7EuW-ArQQxLm04yX_o4276NQmXDpvVJAz5be2RGQ/viewform

9

10 Extremely agree

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Questionnaire for BI Learning Framework - Posttest

1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

9

10 Extremely agree

I have engaged with my own data.

I have engaged with my own decisions.

I have experienced in using BI tools to support my decision making.

I can apply the acquired knowledge and skills to different assignments.

The results reflected how good I am in making decision.

The results reflected how good I am in using BI Tools.

My BI background from the last semesters was influential my learning in this course.

The improvement of this traditional approach could help learners of my demographic.

With all handson labs together, I have understanding a whole picture of BI project.

I gained more BI skills in: * Please rate how strongly you agree with each of the following items: 1 Extremely disagree

2

3

4

5

6

7

8

https://docs.google.com/forms/d/e/1FAIpQLSfyWGlbNk7EuW-ArQQxLm04yX_o4276NQmXDpvVJAz5be2RGQ/viewform

2/5

11/17/2017

Questionnaire for BI Learning Framework - Posttest

1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

Multidimensional Modelling Developing ETL package Modelling a data cube Using PivotTable (or OLAP) Using Data Mining for aCRM Business knowledge (e.g., business function, ability to explain what is being analysed) Communication Decision making

I have experienced several characteristics and features which support my learning outcomes. * Please rate how strongly you agree with each of the following items: 1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

Realistic business data set Real tools Suitable case study Suitable handson labs Realistic business scenario Meaningful experiences Opportunity to reďŹ ne my decisions Closed-loop scenorios

BI maturity Model https://docs.google.com/forms/d/e/1FAIpQLSfyWGlbNk7EuW-ArQQxLm04yX_o4276NQmXDpvVJAz5be2RGQ/viewform

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Questionnaire for BI Learning Framework - Posttest

My analytical maturity is in this stage (BEFORE taking this course): * http://npengage.com/wp-content/uploads/2012/12/BI-Maturity-Model.png Stage 1 Reporting - What happened?

Stage 2 Analysis - Why did it happen?

Stage 3 Prediction - What will happen?

Stage 4 Operationalize - What is happening?

Stage 5 Activate - Take action!

My analytical maturity is in this stage (AFTER taking this course):): * http://npengage.com/wp-content/uploads/2012/12/BI-Maturity-Model.png Stage 1 Reporting - What happened?

Stage 2 Analysis - Why did it happen?

Stage 3 Prediction - What will happen?

Stage 4 Operationalize - What is happening?

Stage 5 Activate - Take action!

I have learned a lot with the traditional approach (Lecture + Hands-on). * 1

2

3

4

5

6

7

8

9 10

Extremely disagree

Extremely agree

If there will be a new teaching approach with a closed-loop scenario, I would like to participate. * A closed-loop scenario lets you setting up your initial business parameters and redefining your decisions based on the analytical data.

1 Extremely disagree

2

3

4

5

6

7

8

9 10 Extremely agree

What do you like learning with this traditional approach (Lectures + Hands-on)?

https://docs.google.com/forms/d/e/1FAIpQLSfyWGlbNk7EuW-ArQQxLm04yX_o4276NQmXDpvVJAz5be2RGQ/viewform

4/5

11/17/2017

Questionnaire for BI Learning Framework - Posttest

What do you dislike learning with this traditional approach (Lectures + Hands-on)?

Other constructive feedback to improve this learning approach.

Submit

Never submit passwords through Google Forms.

Powered by

100%: You made it.

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5/5

The Likert Scales for the Posttest Questionnaire for the Experimental Group

115

11/17/2017

Questionnaire for BI Learning Framework - Posttest

Edit this form

Questionnaire for BI Learning Framework - Posttest This questionnaire is designed for the experiment of (the closed-loop) Business Intelligence (BI) Learning Framework comparing to the traditional approach (Lecture + Hands-on).

The questions asking about how you have experienced learning with the new approach (the closed-loop BI learning framework).

Target group is the students who study Business Intelligence at Faculty of Information and Communication, Stuttgart Media University.

Thank you for your time.

BIA Research Team About us: www.bi-academy.eu Contact us: research[at]bi-academy.eu * Required

What is your HdM email address? * This will be used con dentially. Do not need to type "[at]hdm-stuttgart.de".

Term of study * 1 2 3 4 5 6 7 Other:

Please rate how strongly you agree with each of the following statements: * 1 Extremely disagree

2

3

4

5

6

7

8

https://docs.google.com/forms/d/e/1FAIpQLSd6J4cqhTphVq-5y5BQD9LvyTqJCXN325XlG2Fy2gh_Wj986Q/viewform

9

10 Extremely agree

1/5

11/17/2017

Questionnaire for BI Learning Framework - Posttest

1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

I have engaged with my own data.

I have engaged with my own decisions.

I have experienced in using BI tools to support my decision making.

I can apply the acquired knowledge and skills to different assignments.

The results reflected how good I am in making decision.

The results reflected how good I am in using BI Tools.

My BI background from the last semesters was influential my learning in this course.

I would like to learn with this approach again instead of the traditional approach.

This learning approach could help learners of my demographic.

With all assignments together, I have more understanding a whole picture of BI project.

I gained more BI skills in: * Please rate how strongly you agree with each of the following items: https://docs.google.com/forms/d/e/1FAIpQLSd6J4cqhTphVq-5y5BQD9LvyTqJCXN325XlG2Fy2gh_Wj986Q/viewform

2/5

11/17/2017

Questionnaire for BI Learning Framework - Posttest

1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

Multidimensional Modelling Developing ETL package Modelling a data cube Using PivotTable Using Data Mining for aCRM Using OLAPbased Business Planning Designing Dashboard Business knowledge (e.g., business function, ability to explain what is being analysed) Communication Decision making

This approach provides several characteristics and features which support my learning outcomes. * Please rate how strongly you agree with each of the following items: 1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

Realistic business data set Real tools Suitable case study Realistic business scenario Meaningful experiences Opportunity to reďŹ ne my decisions Closed-loop scenorios Quiz https://docs.google.com/forms/d/e/1FAIpQLSd6J4cqhTphVq-5y5BQD9LvyTqJCXN325XlG2Fy2gh_Wj986Q/viewform

3/5

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Questionnaire for BI Learning Framework - Posttest

1 Extremely disagree

2

3

4

5

6

7

8

9

10 Extremely agree

Peer/Group assessment Game results

My analytical maturity is in this stage (BEFORE): * Stage 1 Reporting - What happened?

Stage 2 Analysis - Why did it happen?

Stage 3 Prediction - What will happen?

Stage 4 Operationalize - What is happening?

Stage 5 Activate - Take action!

My analytical maturity is in this stage (AFTER): * Stage 1 Reporting - What happened?

Stage 2 Analysis - Why did it happen?

Stage 3 Prediction - What will happen?

Stage 4 Operationalize - What is happening?

Stage 5 Activate - Take action!

I have learned a lot more with this approach (the closed-loop BI learning framework) than the traditional approach (Lecture + Hands-on). * 1 Extremely disagree

2

3

4

5

6

7

8

9 10 Extremely agree

What do you like learning with this approach (the closed-loop BI learning framework) comparing to traditional approach (Lectures + Hands-on)?

What do you dislike learning with this approach (the closed-loop BI learning framework) comparing to traditional approach (Lectures + Hands-on)?

https://docs.google.com/forms/d/e/1FAIpQLSd6J4cqhTphVq-5y5BQD9LvyTqJCXN325XlG2Fy2gh_Wj986Q/viewform

4/5

11/17/2017

Questionnaire for BI Learning Framework - Posttest

Other constructive feedback to improve this learning approach.

Submit

100%: You made it.

Never submit passwords through Google Forms.

Powered by

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Report Abuse - Terms of Service - Additional Terms

https://docs.google.com/forms/d/e/1FAIpQLSd6J4cqhTphVq-5y5BQD9LvyTqJCXN325XlG2Fy2gh_Wj986Q/viewform

5/5

Some responses from the interviews are shown in Tables A10.7.

Table A10.7: Student opinions on BIsim from the interviews.

Student#1

Student#2

BI skills improvement Technological skills

Student#3 I have learned new things about BI related processes.

Work with tools, not only scripts,

ETL: Yes, improved. After a few

Although I had prior experiences with working with

better work and better

lab hours.

databases it was the first time for me to work with them in

understanding with real tools.

Multidimensional Modelling:

a BI context. I have never executed an ETL process and

Yes improved. Before I had no

was surprised how easily this can be done using MS SQL-

Modelling experien

Server. I have also learned that it might be a good idea to

why my skills have improved.

use modelling tools like BI-Modeler to create a Database model instead of creating the schemas manually.

Analytical skills

Work with tools, not only scripts,

Modelling Data Cube: Yes

I have worked with Power Pivot in the past. But it was nice

better work and better

improved, I also had no skills

to use it here again within the game. Because of that there

understanding with real tools.

before the course took place.

was more appeal to get into different analyses as it was

OLAP: Yes improved, also no

theoretical

experience before the course.

knowledge about data mining concepts and the closed

Data Mining: Improve a little,

loop.

focus on that part, and only had a few theoretical lessons.

151

Student#1

Student#2

Student#3

Business knowledge

Business plan: helps for the

Business plan: No real

I did not learn much new about creating a business plan or

& communication

overview and to go into the details.

improvement, already had

working in a team as this was already a part of

several lessons like that.

assignments from other lectures in the past. But it was

Business scenario: No real

nevertheless good to use this knowledge again.

skills

Business scenario: is good to understand combining with Business Plan.

Team/Group: discussion with

improvement.

Team/Group: No real improvement

friends, correct each other, communication Application to a real

Feel more positive that student is

A lot of confidence, but we only

I think that I would be able to do an OLAP Analysis or a

project

able to do another BI project /

used MS tools, still, I think I can

simple ETL task on real data in a business environment.

assignment

apply them to other systems

However, I do not think that I have enough knowledge to do data mining.

same concept (I guess). But I applying Data Mining knowledge and we never really use Data Mining tools.

Lessons learned

Real Tools, CRISP-DM, and ETL

BI can help the business.

Process.

152

More details and practice on using OLAP.

Student#1

Student#2

Student#3

The 4th semester learned only

Theoretical knowledge about data mining, CRISP-DM, and

PivotTable, this semester learned a

closed loop.

lot more about BI concepts.

How to use BI Modeler / When to use a modelling tool for database planning Other issues

PC pool should be provided for

Although I did learn a few things during this lecture I

students

would like it better if there was a bigger theoretical part. It is great to get a hands-on but in the first few lessons we did not really learn anything, we spent it setting up our virtual machines and configuring them. It would have been better to use this time to get some more theoretical background on the topics that were covered. Also I would have liked it more if there was a more guided approach to the hands on. For example it would have been good if we did a project from start to finish together with some more explanation from our teachers instead of just having to go through a click-through tutorial.

153

Teaching Business Intelligence with a Business Simulation Game Waranya Poonnawat1,2, Peter Lehmann1, Thomas Connolly2 1 Faculty of Information and Communication, Stuttgart Media University, Stuttgart, Germany 2 Faculty of Engineering and Computing, University of the West of Scotland, Paisley, UK waranya.poonnawat@gmail.com lehmann@hdm-stuttgart.de thomas.connolly@uws.ac.uk Abstract: The term “Business Intelligence” (BI) has evolved from Management Information Systems to Decision Support Systems since the mid-1960s. Today, modern decision making methodologies and technologies are referred to under the term “Business Intelligence”. The main purpose of this technology is to improve both the efficiency of users’ decision making and the effectiveness of decisions. Decision support technology has been implemented and researched in industry and academia for more than half a century, however, challenges in teaching of this field still remain such as access to suitable data sets, finding interesting business cases, and providing realistic and meaningful experiences. Interestingly, the top rank of CIO global technological priorities is still Business Intelligence and Business Analytics, but the skills gap is significantly wide and negatively impacts on business. Moreover, it is not only the BI skills that are needed but also the 21st Century skills, such as, communication, social skills, creativity, critical thinking, problem solving, productivity, and risk taking – as suggested by the European Community to meet the requirements from the job market. This situation drives BI instructors to improve their teaching strategies or to have considered alternative methods to educate their students.

Business Simulation Games are recognized as an effective educational method to enable students to learn how to make decisions and manage the business process in a modern enterprise, link abstract concepts to real world problems, and improve quantitative skills. Additionally, a game is a future technology trend that will be able to support developing new skills, because game characteristics can contribute and sustain 21 st Century skills. Therefore, the “BI Academy” (BIA) project at the Stuttgart Media University in collaboration with the University of the West of Scotland has developed a business simulation game, called BI game. It is anticipated that using the BI game can help instructors to overcome the limitations and challenges in teaching BI, support students to improve their BI skills and 21st Century skills through the learning process, help students to get a better understanding of how to use BI tools to support decision making, and can leverage students’ BI maturity level.

This paper first presents the status of Business Intelligence in academia, the conceptual framework being used as the basis for game design, the technical framework supporting the game operation, and the organisational format of the BI game which provides a closed-loop learning environment. The paper then describes the preliminary results of students’ self-assessment, which shows that most of the students assessed themselves as having quite good 21st Century skills but quite low BI skills. Finally, the paper will provide directions for future research.

Keywords: Business Intelligence, Decision Support Systems, Business Simulation Games, Decision Making, 21st Century skills, BI Maturity.

1. Introduction The term Business Intelligence (BI) has evolved from Management Information Systems to Decision Support Systems, which emerged during the mid-1960s (Power, 2007). Today, modern decision making methodologies and technologies are referred to under the term “Business Intelligence”. This decision support technology is still an important research topic for both industry and academia (e.g., DSS2.0 Conference 2014). The main purpose of this technology is to improve both the efficiency of users’ decision making as well as the effectiveness of decisions (Shim et al, 2002). Recently, a Gartner survey (Gartner, 2013) reported that Business Intelligence and Business Analytics have been in the top rank of CIO global technological priorities for several years – 2009, 2012, and 2013. However, the skills gap in this field is still significantly wide according to 60% of 2,053 CIOs of 36 industries across 41 countries. This skills gap has a negative impact on business.

Even though decision support technology has been implemented and researched in industry and academia for more than half a century, challenges in teaching this discipline still remain. Based on a survey from the BI Congress in 2009, 2010, and 2012 regarding the status of BI in academia, there were several significant challenges in teaching BI, for instance, access to data sets, finding suitable cases, and providing realistic and meaningful experiences (Wixom et al, 2014). In addition, the labour market needs more new skills and more new ways of learning (Redecker et al, 2011). Thus, it is not only BI skills that are required for the nextgeneration BI workforce (Wixom et al, 2010), but also 21st Century skills, such as, communication, collaboration, social skills, creativity, critical thinking, problem solving, productivity, risk taking, and sense of initiative and entrepreneurship – as suggested by the European Community as soft skills that are required to meet the requirements from the job market. This situation forces BI instructors to improve their teaching strategies or to consider alternative methods to educate students; for example, teaching BI with puzzle-based concept (Presthus and Bygstad, 2012), proposing a pedagogical design and method for a practical technical module for a non-technical BI course (Wang and Wang, 2013), and an experiential learning approach in teaching BI (Podeschi, 2014).

Game technology is one approach that has been used to support the development of new skills, for instance, creativity, initiative, responsibility, team-working, managing, and meta-cognitive skills (Redecker et al, 2011).

Game characteristics such as competition, goals, choice, rules, fantasy, and challenges can contribute and sustain 21st Century skills (Romero, Usart and Ott, 2014). Moreover, business simulation games have been known as one of the most effective education methods for teaching and learning managerial skills (e.g., Faria et al, 2009; Wawer et al, 2013; Williams, 2011).

2. Teaching Business Intelligence in Academia In the field of Information Systems, there are many subjects based on Decision Support Systems and Business Intelligence concepts that have been taught for many years in academia; for example, Analytical Information Systems, Management Information Systems, Business Analytics, Decision Sciences, and Statistics (Power, 2007; Wixom et al, 2014). In a survey from BI Congress 2012 regarding the status business intelligence in academia, responses were obtained from 319 faculties from 43 countries, 614 students from 96 universities, and 446 practitioners (Wixom et al, 2014). The interesting findings were that the number of BI program offerings has dramatically increased as well as the access and usage of BI teaching resources. While the demand for BI students has surpassed the supply, the foundational skills are still the most critical for new BI technology and employers were not satisfied with the graduates’ practical skills. There were several key challenges in teaching BI such as access to data sets, finding suitable cases, providing realistic and meaningful experiences. The universities were requested to prepare students for positions as general business analysts, IT professional who can work with data or analytics, or data-savvy business people. Moreover, it was noted that students should have communication skills and experience in report or dashboard development. The employers also indicated the challenges of hiring students for BI jobs as: lack of experience; insufficient business skills, technical skills, communication skills, critical thinking, and data skills, and inexperience with real tools and real data.

The BI Academy (BIA) (www.bi-academy.eu) is a government-funded project that has developed a web portal for teaching BI, hosted at the Stuttgart Media University, Germany. The study programme – Information Systems and Digital Media at the Faculty of Information and Communication – offers several BI-related courses: Management Information Systems, Analytical Information Systems, Data Warehouse System, and Business Intelligence. To provide realistic and meaningful experiences in learning BI, a business simulation game has been developed as an educational platform and a blend of traditional face-to-face teaching. This is a proven approach as business simulation games have been used in business schools for a half century and there have been several empirical studies indicating that this approach enables students to learn how to make a decision, manage the business process in a modern enterprise, link abstract concepts to real world problems, and improve quantitative skills (e.g., Ben-Zvi, 2010; Wawer et al, 2013; Williams, 2011). However, many commercial business simulation games can be very complex and have been developed based on different learning objectives, for instance, inventory management, strategic management, marketing management, or business management. Therefore, our BI game was developed to be less complex and of a smaller scale and, in particular, it supports only some basic functions of an Enterprise Resource Planning (ERP) system. The “OrderTo-Cash” process was selected as a study area and the BI game focuses more on managerial decision support.

3. The Conceptual and Technical Frameworks The conceptual framework as shown in Figure 1a consists of two parts: the Business Simulation Games and the Management Process & Decision Making. The business simulation game is used as an educational platform to simulate the business processes. Each business activity, which occurs during a business process, requires someone to manage it and make a decision. The strategic goals, the marketing environment, and the enterprise environment should therefore be analysed. The alternative solutions should be evaluated, selected, implemented, and controlled for success (Gluchowski, Gabriel and Dittmar, 2008). The cycle of management process and decision making have to be done as soon as possible after the business event occurred to keep the business value high (Figure 1b) (Hackathorn, 2003), which can be achieved by using BI as an information service in a company. Based on this, the technical framework shown in Figure 2 was developed using the data warehouse approach to support the business scenarios as well as the management processes and decision making from the conceptual framework. A data warehouse “is a subject-oriented, integrated, non-volatile and time-variant collection of data in support of management’s decision making process� (Inmon et al, 2008, p.7).

(a)

(b)

Figure 1: (a) The conceptual framework for the BI Game and (b) the Value-Time curve

Figure 2: A technical framework for the BI game

With this technical framework, the business transactions from the Order-To-Cash process are collected into the game ERP server. The Order-To-Cash process includes several business activities, for instance, order entry, distribution, invoicing, and customer payments. The key figures (e.g., sales amount, revenue, and cost) are generated and stored in the Enterprise Data Warehouse (EDW). All transactional and master data are stored as a relational data warehouse model (Figure 3). This model is also called a multi-fact schema. Next, a MultiDimensional Model (MDM) is designed and an ETL process is developed to populate the analytical data from the enterprise data warehouse to the multi-dimensional model in order to access, analyse, and visualise data with the BI front-end tools. Consequently, the business analytics can be carried out to improve the business performance.

Figure 3: The relational data warehouse model

4. Description of the BI Game The prototype of the BI game was launched in February 2014 and has been tested with students at several European universities. The tasks in the BI game are the development, the implementation, the analysis of the impact and success of a business plan, and the refinement of a business plan. The BI game has been developed based on a closed-loop approach, therefore, the BI game simulates a real business process and students have to take a role of a decision maker, analyse the key figures, calculate the profit as a result from their actions, and react from their own decisions to improve the next decisions (Lee, 2010).

4.1 BI game scenario The game is based around a fictitious company that specialises in selling high quality bicycles and accessories, for instance, mountain bikes, kid bikes, e-Bikes, event bikes, tires, helmets, gloves, and baskets. The company has an expansion strategy to increase the market share by opening more bike stores around the country.

The organisational format of the BI game has four main steps (Figure 4) as follows: (1) Introduction; (2) Business Parameters Setting; (3) Data Collection & Simulation, and (4) Refinement. In the first two steps, students attend an introduction session then develop and implement a business plan by setting the initial business parameters. In the third step, the initial business parameters and the simulated business transactions for the business activities are collected and stored in the ERP server. In the fourth step, the key figures are simulated and stored in the enterprise data warehouse.

Next, the BI assignments are provided to students. The first two BI assignments belong to the ‘Build BI’ module which consists of the “Multi-Dimensional Modelling” and the “ETL Design & Process” phases. The ‘Build BI’ module is designed for students to prepare and deploy the analytical data. The other assignments belong to the ‘Use BI’ module, which consists of the “Sales Analysis with OLAP” and the “OLAP-Based Business Planning” phases. The ‘Use BI’ module is designed using different business scenarios where students perform business analysis and refine their business parameters (Step 4 Refinement). Then, the key figures are calculated and simulated again based on the modified business parameters for the next business analysis.

Figure 4: The organizational format of the BI Game

4.2 Step 1 to Step 3: Introduction, Business Parameters Setting, and Data Collection & Simulation Students are introduced to the BI game and the bike market situation in a specified country. Later, students are randomly assigned to teams of three to four. Each team is responsible for a city and for opening a new bike store in that city. Each selected city including its surrounding areas contains approximately 200,000 – 250,000 inhabitants; for instance: Freiburg im Breisgau, Friedrichshafen, Karlsruhe, and Heidelberg. This is considered an appropriate size for the city in our game.

Each team represents a management team that is responsible for a bike store. Each store has a similar size of about 500 m2. The teams have to collect all relevant market information, for instance, the landscape profile, population, target group (Figure 5a), bike market volume, and competitors in the city (Figure 5b). Then they present their business plan using the following structure: location, target group, competitors, product mix, marketing campaign, personnel plan, and financial plan. An instructor who serves as an executive of the company assigns an initial market share based on the quality of the proposed business plan.

(a)

(b)

Figure 5: Example of a business plan: (a) target groups and (b) competitors After that, the teams register in the BI game application and set their initial business parameters. They select a store for rent, order products from the company stock, consider and choose staff, and select the marketing campaigns (Figure 6). Furthermore, they can add more new stores to the system, request more new products from the company, or create additional marketing campaigns.

Figure 6: A screenshot of the BI game application for the initial settings Based on those inputs, the data generator calculates all business transactions to store in the ERP server. This calculation is based on the data from real bike companies that have a similar size of business and serve as a model or benchmark. The factors that influence the success of the bike business are as follows: location parameters, product mix for the region, qualification of the employees, good marketing campaign, real weather, and real sport events (e.g., Tour de France). The key figures are calculated and stored in the enterprise data warehouse. At this point, the ‘Build BI’ assignments are provided to students to prepare the analytical data. Students are able to access the enterprise data warehouse in order to design a multidimensional model (Figure 7) and design an ETL process (Figure 8) to populate data from the enterprise data warehouse into the multi-dimensional model.

Figure 7: A multi-dimensional model for Sales Performance

Figure 8: An ETL process to populate data from the enterprise data warehouse to a multi-dimensional model

4.3 Step 4: Refinement Before the teams can begin their refinement, they are provided with the ‘Use BI' assignments. The “Sales Analysis with OLAP” assignment helps students to learn how to analyse their sales performance by using an OLAP tool (Figure 9). Students are able to see the results from the implementation of their initial business plan and they have a chance to reflect on their decisions and improve the next decisions by refining their business parameters.

(a) (b) Figure 9: Examples of the sale analysis: (a) an OLAP report and (b) a revenue-gross profit dashboard The next assignment – “OLAP-Based Business Planning” – helps students to learn business planning by using OLAP tools. Students are able to focus on good practice in doing an OLAP-based business planning and can see the results from the previous decisions, have a chance to reflect and improve the next decisions by refining their business parameters. Working with the ‘Use BI’ assignments, students are able to analyse the impact and success of their business plan and refine it. Then, the data generators calculate the values for their key figures.

5. Learning Evaluation It is anticipated that teaching BI with a business simulation game will help students to develop their 21st century skills and BI skills. Today’s BI users need to have at least three groups of skills: (1) analytical skills – e.g., data mining, statistical analysis; (2) IT skills – e.g., data mart model, ETL process, and (3) business knowledge and communication skills – e.g., business functions, ability to explain what is being analysed (Andoh-Baidoo et al, 2014). Additionally, the 21st Century skills are also needed for the next-generation BI workforce. The evaluation for students’ skills from learning with the BI game will be collected from self-assessment, peer assessment, group assessment, game results, quiz, and BI assignments.

5.1 Questionnaires The initial version of the BI game has been evaluated in session 2014-15 with 16 students from the 4th to 6th semester some of whom would have some background knowledge in Business Intelligence and also some related skills. Students were asked to complete a pre-test questionnaire before or during the initial session of the BI game or the traditional BI class. The pre-test questionnaire consisted of three parts: personal information, 21st Century skills, and BI skills (Figure 10). Students’ major and ID were collected to match the results from other assessment methods. Likert-scales with ten items were used to collect students’ selfassessment.

(a) (b) Figure 10: (a) The pre-test questionnaire and (b) some questions about 21st Century skills Questions 1 and 2 were about personal information, questions 3 to 18 were about 21st Century skills, and questions 19 to 25 were about BI skills (see Table 1). The self-assessment results were grouped into three categories: 1 = low, 2 = medium, and 3 = high. Later, students were asked to complete a post-test questionnaire after the last session of the BI game or the traditional BI class. The post-test questionnaire consisted of three main parts: preferences and perceptions, skills, and gaming features. Likert-scales with ten items were used to collect students’ self-assessment.

Table 1: The questions in pre-test questionnaire Question 1 2

Personal Information Major Student ID

Question 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

21st Century Skills Teamwork Communication ICT literacy Social or cultural skills Creativity Critical thinking Problem solving Productivity Learning to learn Self-direction Planning Team flexibility Idea flexibility Risk taking Conflicts management Sense of initiative

Question 19 20 21 22 23 24 25

BI Skills OLAP tools OLAP-based Business Planning Multidimensional Modelling ETL process Data Mining BI concepts BI tools

5.2 The preliminary results The first pre-test questionnaire was completed by an experimental group. It showed that most of students assessed themselves as having relatively high 21st Century skills (Figure 11). About 55-60% of the students assessed themselves for most of questions as having high or medium 21st Century skills. However, the students

who assessed themselves having high skills in creativity (Question 7) and in handling uncertainty (Question 16) were only 19% and 13% respectively.

Figure 11: The preliminary results of the Pre-Test for 21st Century Skills from the experimental group On the other hand, about 80-85% of students assessed themselves as having only medium or low BI skills (Figure 12). Moreover, for questions 22 to 25 – ETL process, data mining concepts, BI concepts, and SSBI tools, respectively – no student assessed him- or herself having high skills. These results suggest that the students have good 21st Century Skills but need to improve their skills in Business Intelligence and they will hopefully benefit from the use of the BI game.

Figure 12: The preliminary results of the Pre-Test for BI Skills from the experimental group The preliminary results concerning BI skills from the post-test of the experimental group (see Figure 13) showed that no student assessed him- or herself as having low BI skills anymore. About 67% of the students assessed themselves as having high BI skills in OLAP tools compared to 19% in the pre-test. About 60% and 80% of students assessed themselves as having high BI skills in OLAP-based Business Planning and Multidimensional Modelling respectively compared to about 13% in the pre-test. About 73% and 60% of students assessed themselves as having high BI skills in ETL process and Data Mining respectively compared to no students having assessed themselves as having high skills in the pre-test.

Figure 13: The preliminary results of the Post-Test for BI Skills from the experimental group

Additionally, about 67% of students assessed themselves as having learned more with BI game. They stated that they liked the practical part together with realistic scenarios and real tools, and gained experiences from learning with the BI game.

Some positive quotes from students were:   

“I liked the practical way of learning with the game.”

“I liked the structure of the course. The combination of practical work with tools and the theoretical background.”

“I liked the practical experience. For me it is easier to learn with a realistic scenario with the opportunity to act on it than to just hear the lecture.”

6. Conclusions With this BI game, instructors are able to put theory into practice with a risk-free and inexpensive environment. Students can be trained in a relatively short period of time with a realistic business scenario and they can experience real data and real tools. Students are provided with a closed-loop environment, which helps students to learn to manage the performance of the business processes and helps them to align business goals and processes consistently (Martin, 2014). Additionally, students are able to understand the impact between each business process because it involves human intervention to improve the way decisions are made (Kerremans and Kitson, 2012).

The advantages of the BI game are: more BI assignments or modules (e.g., data mining, what-if analysis, balanced scorecard dashboard, geographic information system, or big data) can be added to the game; the duration of running the game is quite flexible, and the system’s scalability can handle a large number of players concurrently. We believe that the BI game provides a modern, integrated, and easy-to-use educational platform. Students will improve both their BI skills and 21st Century skills through the learning process and gain a better understanding of how to use decision support technology.

A full version of the BI game is being developed that will include three more BI assignments: Data Mining, What-if Analysis, and Balanced Scorecard Dashboard, which will be able to leverage BI maturity to a higher level. This version will be evaluated using an RCT/quasi-experimental methodology using pre-test/post-test questionnaires to assess the impact on learning.

References Andoh-Baidoo, F., Villa, A., Aguirre, Y. and Kasper, G. (2014) “Business Intelligence & Analytics Education: An Exploratory Study of Business & Non-Business School IS Program Offerings”, The 12th Americas Conference on Information Systems (AMCIS) Proceedings, pp 1-9.

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Gartner. (2013) “Hunting and Harvesting in a Digital World: Insights From the 2013”, [online], Gartner CIO Agenda Report, www.gartner.com/imagesrv/cio/pdf/cio_agenda_insights2013.pdf Faria, A.J., Hutchison, D., Wellington, W.J. and Gold, S. (2009) “Developments in business gaming: a review of the past 40 years”, Simulation & Gaming, Vol 40, No. 4, pp 464-487.

Gluchowski, P., Gabriel, R. and Dittmar, C. (2008) Management Support Systeme und Business Intelligence: Computergestützte Informationssysteme für Fach- und Führungskräfte, Springer-Verlag Berlin Heidelberg.

Hackathorn, R. (2003) “Minimizing Action Distance”, The Data Administration Newsletter, [online], www.tdan.com/print/5132 Inmon, W.H., Strauss, D. and Neushloss, G. (2008). DW 2.0: The Architecture for the Next Generation of Data Warehousing. USA: Morgan Kaufmann.

Karl, C.K. (2013) “Integrative Learning: Exploring Opportunities in Business Simulations”, Developments in Business Simulation and Experiential Learning, Vol 40, pp 48-57.

Kerremans, M. and Kitson, N. (2012) “Aligning Business Process Management and Business Intelligence to Achieve Business Process Excellence”, [online], www.capgemini.com/resource-file-access/resource/pdf/ Aligning_Business_Process_Management_and_Business_Intelligence_to_Achieve_Business_Process_ Excellence.pdf Lee, A. (2010) “Simulation games: Shifting from conceptual learning to experiential learning”, Blended

Learning in Practice, [online], University of Hertfordshire, http://www.herts.ac.uk/about-us/learning-andteaching/learning-teaching-institute/scholarship-research-evaluation/blended_learning_in_practice.

Martin, W. (2014) Performance Management and Analytics: Business Intelligence meets Business Process Management and Big Data. [online], www.wolfgang-martin-team.net/BI-BPM-SOA.php Power, D.J. (2007) “A brief history of decision support systems”, DSSResources.COM, [online], www.groupdecisionroom.nl/artikelen/decision-support-system.pdf Podeschi, R.J. (2014) “Experiential Learning using QlikView Business Intelligence Software”, The Information Systems Educators Conference 2014 Proceedings, Vol 31, No. 3004, pp 1-11.

Presthus, W. and Bygstad, B. (2012) “Business Intelligence in College: A Teaching Case with Real Life Puzzles”, Journal of Information Technology Education, Vol 11, No. 1, pp 121-137.

Romero, M., Usart, M. and Ott, M. (2014) “Can Serious Games Contribute to Developing and Sustaining 21 st Century Skills?”, Games and Culture, pp 1-30.

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A Framework for using Business Intelligence for Learning Decision Making with Business Simulation Games 1Faculty

Waranya Poonnawat1,2 and Peter Lehmann1 of Information and Communication, Stuttgart Media University, Stuttgart, Germany 2School of Computing, University of the West of Scotland, Paisley, U.K.

Keywords:

Business Intelligence, Decision Support Systems, Decision Making, BI Skills, 21St Century Skills, Business Simulation Games, Data Warehouse, Self-Service Business Intelligence, Learning Assessment.

Abstract:

This position paper will give an overview of the Business Intelligence (BI) learning framework which includes: (1) BI game; (2) data warehouse system; (3) self-service BI tools, and (4) learning assessment.

The BI game is used as an educational platform to simulate business scenarios and business processes. The data warehouse system integrates all of the business transactions and results from the BI game and provides a single point of truth for analytical information. During the business processes, self-service BI tools are used to access data marts for business analytics by both students and instructors. The learning assessment component is used to evaluate students’ knowledge and skills in BI and 21st Century skills.

1

INTRODUCTION

The evolution of modern Business Intelligence (BI) is from Decision Support Systems (DSS) which has emerged since the mid-1960s (Power, 2007). This decision support technology is still an important research topic in the realms of both industry and universities (e.g., DSS2.0 Conference 2014).

Recently, Gartner (2013) published a survey result reported that BI has been in the top rank of CIO global technological priorities for several years – 2009, 2012 and 2013. However, the skill gap in the BI field was still significantly up to 60% of reponses from 2,053 CIOs of 36 industries across 41 countries. This skill gap has both a negative and short-term impact on business (Gartner, 2013).

Based on the survey from BI Congress 2012 regarding the status of BI in academia, there were several significant challenges in teaching and learning BI, for instance, access to data sets, finding suitable cases, providing realistic and meaningful experiences (Wixom et al., 2013). Several BI instructors have attempted to improve their BI teaching and learning methods and have considered alternative methods, for instance, proposing course components and learning objectives to teach data warehousing and data mining (Fang et al., 2006), teaching data warehousing and data mining using case projects (Rob et al., 2007), teaching BI using

cloud computing technology (Mrdalj, 2011), teaching BI with puzzle-based concept (Presthus et al., 2012), proposing a pedagogical design and method for a practical technical module for a nontechnically oriented BI course (Wang et al., 2013), concerning an experiential learning concept in teaching BI (Podeschi, 2014).

As well as this, the labour market will need more new skills and more new ways of learning (Redecker et al., 2011). Thus, it is not only BI skills that are needed for the next-generation BI workforce (Wixom et al., 2010), but also the 21st Century skills for European Community.

Game is one aspect of the technology trend that will be able to support the future of learning to build up new skills (Redecker et al., 2011). Game characteristics, for instance, competition and goals, choice, rules, fantasy and challenges, can contribute and sustain 21st Century skills (Romero et al., 2014).

Moreover, business simulation games have been known as one of the most effective education methods for teaching and learning managerial skills (e.g., Faria et al., 2009; Wawer et al., 2013; Williams, 2011).

Therefore, the BI learning framework (see Figure 1) is proposed to contribute learning and teaching BI for the next-generation BI workforce concerning both BI skills and 21st Century skills. The framework consists of four components as follows:

Poonnawat W. and Lehmann P..

A Framework for using Business Intelligence for Learning Decision Making with Business Simulation Games.

DOI: 10.5220/0005474902830288 In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 283-288 ISBN: 978-989-758-108-3 c 2015 SCITEPRESS (Science and Technology Publications, Lda.) Copyright

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(1) BI game – an educational platform to provide simulation realistic business scenarios, data sets, suitable cases with meaningful experiences; (2) Data Warehouse (DW) system – an information service to support business managerial decision making; (3) Self-Service BI (SSBI) tools – a personal business analytical tool to analyse and monitor business performance management, and (4) learning assessment – a set of evaluation methods for students’ learning outcomes.

Figure 1: The four components of the BI Learning Framework.

2

BI GAME

BI game is a kind of computerised business simulation game for teaching and learning BI. Since there have been several empirical studies indicating that business simulation games enable students to learn how to make a decision, manage the business process in a modern enterprise, link between abstract concepts and real world problems and improve quantitative skills (e.g., Ben-Zvi, 2010; Wawer et al., 2013; Williams, 2011). Most of business simulation games were developed based on different learning objectives, for instance, inventory management, strategic management, marketing management, business terms. However, the learning objective of BI game focuses on the BI concept, knowledge and skills for managerial decision support.

BI game has been developed by the research team of BI Academy (BIA) – the learning portal and community for teaching and learning BI (www.biacademy.eu). The prototype was launched since February 2014 and has been tested with students in several European universities.

BI game is based on the conceptual framework (see Figure 2). In each business activity students implement the management process cycle to make a decision (Gluchowski et al., 2008).

The objectives or competence goals of the game focus on students’ learning for both (1) 21st Century skills and (2) BI skills – which are about using OnLine Analytical Processing (OLAP) tools for decision support, creating OLAP-based business

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Figure 2: A conceptual framework for the BI game.

planning, designing a multi-dimensional model, designing an ETL process, appling data mining concepts and running business based on BI concepts by using SSBI tools as a business analytical tool.

The organisational or event format of BI game has six steps (see Figure 3). Firstly, students are assigned into groups randomly and each group represents a city opening a new bike shop. Then, they have been introduced to BI game and the bike marketing situation based on the city. Students have to present their business plan to get an initial market share for starting their business. Secondly, students book the initial settings for a store location, product mix, required employees and marketing campaigns.

After the initial settings (step 2), the data generator with an embedded simulation algorithm generates the revenue based on the input business parameters.

Next, students can access the data access layer of the ERP system, learn how to apply SSBI tools, analyse the data and make a decision to refine their business strategy.

Later, during the game play before going on each step (step 3 to 6), business problems - which are

A Framework for using Business Intelligence for Learning Decision Making with Business Simulation Games

based on the learning objectives - are given to students. The business problems should lead students to use SSBI tools to generate OLAP reports, OLAP-based business planning models, data mining models, what-if scenarios or Balanced Scorecard (BSC) dashboards.

single point of truth for enterprise information (Inmon et al., 2001). Moreover, a data warehouse is a good practice solution for information logistics and is considered as a reference architecture to underpin a successful BI project.

With the data warehouse system component, BI learning framework can handle data from the business simulation game and learning process. Its development is based on the technical framework (see Figure 4).

Figure 3: The organizational format of the BI Game.

The advantages of our BI game are as follows: (1) the implementation with modern technology (e.g., cloud computing); (2) the flexibility in the number of BI teaching modules (e.g., multidimensional modelling, ETL process, OLAP reports, business planning, data mining, what-if analysis, dashboard); (3) the flexibility of the duration for running the game (e.g., three days, one week, one semester); (4) the contribution for international students; (5) the usage of BI vendor university alliance programs (e.g., Microsoft MSDN Academic Alliance, SAP University Alliances); (6) the system scalability which can handle large amount of players at a time, and (7) the learning assessment to analyse learning outcomes.

3

Figure 4: A technical framework for the BI learning framework.

Data sources originate from business game application and the data generator engine simulates the revenues and costs. All are based on the initial settings, initial market share and proposed business plan. The game data is stored in a game server and later will be extracted, transformed and loaded (ETL) into the data warehouse system. All transactional and master data from game are stored as a relational model in Operational Data Store (ODS) layer of the data warehouse system (see Figure 5).

DATA WAREHOUSE SYSTEM

“A data warehouse is a subject-oriented, integrated, non-volatile and time-variant collection of data in support of management’s decision making process” (Inmon et al., 2008, p.7). It integrates heterogenous and distributed data sources. Users, therefore, are able to have access to the same sources of analytical information, gain insights of their business performance and can make better decisions which will help them to balance all levels of their business strategies (Poe, 1996). The major advantange of data warehousing is to support the vertical integration (Oehler, 2006) between different management levels – operational, tactical, and strategic – and provide a

Figure 5: The relational model in ODS layer.

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Later, students are assigned to design an information model for their data mart, design an ETL process and create an ETL package to populate data into data marts. Consequently, students can use SSBI tools to carry out business analytics and improve their business performance in accordance with the rights to access their data only.

4

SELF-SERVICE BUSINESS INTELLIGENCE

Self-Service Business Intelligence or SSBI is a new advanced BI technology. It provides an environment whereby users can easily create their own data models and analyse data by themselves (Imhoff et al., 2011). There are several types of information workers that are involved in using SSBI. These are: information producers, information consumers, information collaborators and Data Warehouse (DW) or BI builders (Imhoff et al., 2011).

SSBI tools cover the front-end applications of BI landscape as follows: (1) presentation tools, for instance, reporting and dashboards; (2) analysis tools, for instance, OLAP analysis and data mining; (3) visualisation tools, for instance, displaying data with maps or various types of charts and graphics; (4) integration tools, for instance, adding external data into the BI data model, and (5) data discovery or exploration tools, for instance, using ad-hac query (Aziz, 2014). OLAP is considered as a core technology of BI for decision makers to view data from a variety of perspectives and visualise summarised information with respect to business performance from various analysis with scorecards and dashboards (Richards et al., 2014). Moreover, modern SSBI tools will be able to access various data sources from different providers and more data mart structures (e.g., relational model, multidimensional model, flat files).

Students will be the next-generation BI workforce for the (business) community. They need to have more analytical skill and make faster and better decisions based on information they have inhand. So, the faster they make a decision, the more they can save the business value (Hackathorn, 2003).

In the BI learning framework environment, students learn in a short period how to use SSBI functionalities in a tool – such as an electronic spreadsheet – to conduct business analytics concerning the business problems and learn to use SSBI tools improving their decisions based on fact.

The usage of BI tools are, for instance, applying data

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mining concept to analyse the prospective customers for a mailing list, creating OLAP-based business planning for the next years procurement, designing a dashboard to monitor the sales performance by using Key Performance Indicators (KPIs), etc.

Instructors also can monitor how students run their business and solve the business problems, for instance, using OLAP-based reports to assess the overview of profit for each group of students (see Figure 6) and a dashboard to see the revenue for each store by price segment (see Figure 7).

Figure 6: Sample of an OLAP-based report.

Figure 7: Sample of a dashboard.

5

LEARNING ASSESSMENT

There are at least three groups of skills that are needed for today’s BI users as follows: (1) analytical skills – e.g., data mining, statistical analysis; (2) IT skills – e.g., data mart model, ETL process, and (3) business knowledge and communication skills – e.g., business functions,

A Framework for using Business Intelligence for Learning Decision Making with Business Simulation Games

ability to explain what is being analysed (AndohBaidoo et al., 2014). However, the diversity of SSBI tools and features are not trivial to use. As most of users focus on consuming the information, while others focus on producing the information.

Consequently, SSBI tools could be difficult to use for some users or with a high risk to be overused by other users (Eckerson, 2012).

The next-generation BI workforce needs also to have 21st Century skills – collaboration or teamwork, communication, ICT literacy, social or cultural skills, creativity, critical thinking, problem solving, productivity, learning to learn, selfdirection, planning, flexibility, risk taking, manage conflicts and sense of initiative (Romero et al., 2014). Additionally, there are two methods that are primarily used for the competency assessment: (1) self-assessment, and (2) evaluation of results from business simulation game by the instructors (Karl, 2013).

Therefore, the learning assessment for the BI learning framework will be categorised into three parts as follows: (1) self-assessment – students are requested to complete the questionnaires before and after playing BI game. They evaluate themselves for BI skills and 21st Century skills.

(2) game results – students should pass the course or get a certificate, as and when they are able to run a business well. The game results also will be used to compare between each group for discussion or debriefing (Crookall, 2010).

(3) SSBI usage – students’ level of BI skills depend on how advance they are able to use SSBI tools for data analysis as shown in the organisational format of BI game.

6

CONCLUSIONS

Our BI learning framework provides a closed-loop model started from the initial settings of business parameters based on business strategy. All business settings are stored in the ERP server, information requirements and data marts are modelled, developed and deployed in the data warehouse server. Business analytics are performed in order to make reasonable decisions and later students are able refine their business model for the next cycle.

This closed-loop approach helps students to learn to manage the performance of the business processes and is able to align business goals and processes consistently (Martin, 2014). Additionally, students are able to understand the impact between each

business process because it involves human intervention to improve the way decisions are made (Kerremans et al., 2012).

We believe that the BI learning framework provides a modern, integrated and easy-to-use platform which will overcome the limitations and challenges in learning and teaching BI. Moreover, students will improve their BI skills and 21st Century skills through their learning process and have better understanding how to use BI to support decision making.

Furthermore, we are working on the integration of more business scenarios into the framework, in order to leverage BI maturity and improve 21st Century skills for students.

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Aziz, M.Y. (2014). Business Intelligence Trends and Challenges. The Fourth International Conference on Business Intelligence and Technology (BUSTECH) 2014 Proceedings, 1-7.

Ben-Zvi, T. (2010). The efficacy of business simulation games in creating decision support systems: an experimental investigation. Decision Support Systems and Electronic Commerce, 49(1), 61-69.

Crookall, D. (2010). Serious Games, Debriefing, and Simulation/Gaming as a Discipline. Simulation & Gaming, 41(6), 898-920.

DSS 2.0 Conference. (2014). “DSS2.0 – supporting decision making with new technologies”, 2-5 June 2014, Universite Pierre et Marie Curie, Paris, France.

Retrieved from http://dss20conference.wordpress.com/ Eckerson, W. (2012). The secrets of Self-Service BI. Blog: Wayne Eckerson – BeyeNETWORK, June 1, 2012.

Retrieved from http://www.b-eye-network.com/blogs/ eckerson/archives/2011/01/the_secrets_of.php.

Fang, R. and Tuladhar, S. (2006). Teaching Data Warehousing and Data Mining in a Graduate Program of Information Technology. Journal of Computing Sciences in Colleges, 21(5), 137-144.

Faria, A.J., Hutchison, D., Wellington, W.J. and Gold, S. (2009). Developments in business gaming: a review of the past 40 years. Simulation & Gaming, 40(4), August 2009, 464-487.

Gartner. (2013). Hunting and Harvesting in a Digital World: Insights From the 2013 Gartner CIO Agenda Report. Retrieved from. http://www.gartner.com/imagesrv/cio/pdf/cio_agenda_insi ghts2013.pdf.

Gluchowski, P., Gabriel, R. and Dittmar, C. (2008).

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Intelligence: Computergestützte Informationssysteme für Fach- und Führungskräfte. Springer-Verlag Berlin Heidelberg.

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Imhoff, C. and White, C. (2011). Self-Service Business Intelligence: Empowering Users to Generate Insights.

TDWI Best Practices Report, 3rd Quarter 2011.

Inmon, W.H., Strauss, D. and Neushloss, G. (2008). DW 2.0: The Architecture for the Next Generation of Data Warehousing. USA: Morgan Kaufmann.

Inmon, W.H., Imhoff, C. and Sousa, R. (2001). Corporate Information Factory. 2nd edition. The United States of America: John Wiley & Sons, Inc. Karl, C.K. (2013). Integrative Learning: Exploring Opportunities in Business Simulations. Developments in Business Simulation and Experiential Learning, 40, 48-57.

Kerremans, M. and Kitson, N. (2012). Aligning Business Process Management and Business Intelligence to Achieve Business Process Excellence. Retrieved from http://www.capgemini.com/resource-fileaccess/resource/pdf/Aligning_Business_Process_Man agement_and_Business_Intelligence_to_Achieve_Bus iness_Process_Excellence.pdf.

Martin, W. (2014). Performance Management and Analytics: Business Intelligence meets Business Process Management and Big Data. Retrieved from http://www.wolfgang-martin-team.net/BI-BPMSOA.php.

Mrdalj, S. (2011). Would Cloud Computing Revolutionize.

Teaching Business Intelligence Courses? Issues in Informing Science and Information Technology, 8, 209-217.

Oehler, K. (2006). Corporate Performance Management mit Business Intelligence Werkzeugen. Wien: Hanser.

Poe, V. (1996). Building a Data Warehouse for Decision Support. USA: Prentice Hall PTR. Podeschi, R.J. (2014). Experiential Learning using QlikView Business Intelligence Software. The Information Systems Educators Conference 2014 Proceedings, 31(3004), 1-11.

Power, D.J. (2007). A brief history of decision support systems. DSSResources.COM. version 4.0, March 10, 2007. Retrieved from http://www.groupdecisionroom.nl/artikelen/decisionsupport-system.pdf.

Presthus, W. and Bygstad, B. (2012). Business Intelligence in College: A Teaching Case with Real Life Puzzles. Journal of Information Technology Education, 11(1), 121-137.

Redecker, C., Leis, M., Leendertse, M., Punie, Y., Gijsbers, G., Kirschner, P., Stoyanov, S. and Hoogveld, B. (2011). The Future of Learning: Preparing for Change. JRC Scientific and Technical Reports.

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Romero, M., Usart, M. and Ott, M. (2014). Can Serious Games Contribute to Developing and Sustaining 21st Century Skills? Games and Culture, 1-30.

Wawer, M., Miloz, M., Muryjas, P. and Rzemieniak, M. (2013). Business simulation games in forming of students’ enterpreneurship. International Journal of Economics and Management Sciences (IJEMS), 3(1).

Wang, S. and Wang, H. (2013). Design and Delivery of Technical Module for the Business Intelligence Course. Journal of Information Tehnology Education: Innovation in Practice, 12, 169-184.

Williams, D. (2011). Impact of business simulation games in enterprise education. The 2010 University of Huddersfield Annual Learning and Teaching Conference. University of Huddersfield, Huddersfield.

Wixom, B.H., Ariyachandra, T. and Mooney, J. (2013).

State of Business Intelligence in Academia, BI Congress 3, 15-16 December 2012, Orlando, FL, USA. Wixom, B.H., Watson, H.J., Marjanovic, O. and Ariyachandra, T. (2010). Educating the NextGeneration BI Workforce. Business Intelligence Journal, 15(3), 3rd Quarter 2010, 26-31.

A Framework of using DSS in Business Simulation Games Study Object: Business Intelligence and Corporate Performance Management a

Waranya POONNAWATa,b,1 and Peter LEHMANN a Faculty of Information and Communication, Stuttgart Media University, b School of Computing, University of the West of Scotland,

Abstract. Decision Support Systems (DSS) have been used as a managerial decision supporting information system since the mid-1960s and the evolution of DSSs remains important for academia and industries. A new generation of DSSs technology, Self-Service Business Intelligence (SSBI), gives more powerful for information workers to make better decisions based on facts with less dependency to IT department. However, there are several challenges in teaching and learning how to use SSBI tools, since they are diverse and support broader ways to implement BI ad-hoc solutions. Business Simulation Games (BSGs) is considered as an effective educational platform that mostly be used in business schools and its potential enables students to develop management skills which need for the real life problems. The aim of this research is to propose a framework for teaching and learning decision making by using SSBI with Business Simulation Games (BSGs).

Additionally, learning activities will be collected for further learning outcomes evaluation.

Keywords. Self-Service Business Intelligence, Business Simulation Games, Decision Support Systems, Business Intelligence, Management Process, Decision Making.

Introduction The information systems for supporting management decision making, which are wellknown as “Decision Support Systems” (DSS), have been evolving since the mid-1960s [1]. Many DSS applications, tools and technologies are widely revealed on the market after the intensive research and development by Information Technology (IT) companies and universities. Additionally, the evolution of DSS concepts remains an important research topic in both industries and universities.

Over the last two decades, DSS were utilized with some limitations and difficulties, such as heterogeneous data source extraction, multi-dimensional modelling, business analytics, information workers’ collaboration, multi-channel user interfaces and massive data visualisation. Meanwhile, the high demand for managing the corporate information factory [2] brought the modern and powerful DSS concepts and methods 1

Corresponding Author: poonnawat@hdm-stuttgart.de

onto the DSS stage, which are compromised under the term “Business Intelligence” (BI).

Today, the emergence of a new generation of decision support technologies – SelfService Business Intelligence (SSBI) – enables DSS to function for a wider group of end users. DSS applications have moved from the management-focused decision support to the easy-to-use decision support for information workers at all levels of a company – strategic, tactical and operational. The decision making in the companies is necessary for operating and managing the highly optimised business processes.

Nevertheless, the DSS-related subjects are typical in the field of Information System (IS) and have been taught for many years [1,3]. They are still very popular in the academic world. Subjects such as Information Analytics, Management Information System, Business Intelligence and Business Analytics, are based on the DSS concepts.

Moreover, Wixom’s survey about the BI status in academia [4], had a base of 319 professors from 257 universities in 43 countries around the world. There were many BI-related subjects that have been taught in various academic disciplines.

In the survey’s top message, the question about teaching and learning BI was listed as the most challenging issue. Challenges mentioned included: access to data sets, finding a suitable textbook, finding suitable cases and providing realistic experiences [4].

Consequently, this advent of decision support technologies leads to the following questions: (1) “How can students be taught not only the basic concepts about BI and the handling of a BI tool, but also to select the “right” BI tool for making good decisions in the decision making process?”

(2) “What kind of educational platform can be used to teach the effective and efficient usage of BI tools?”

1. Literature Review and Literature Search The review question derived from the questions that mentioned in the introduction section.  Is there empirical evidence of using DSS/BI associated with business simulation games currently exist in the literature?

In order to answer the review question, the secondary research methodology was used with this search terms: (“serious games” OR “business games” OR “games-based learning”) AND (“decision support system” OR “management information system”) Furthermore, the background theories related to this research including management decision making process, decision-making support technology and business simulation games have been reviewed.

1.1. Management Decision Making Process DSS and BI technologies are used increasingly to support the management processes, which can be seen as a systematic series of different phases. As an example for management process the following schema will be used to explain the typical management tasks in four phases: Business Analysis, Decision Taking, Organisation & Steering and Success Controlling [5].

(Adapted from [5]) Figure 1. Phase diagram for management process.

The business value, which can be gained from management process, depends on the decision making latency or action distance – the distance between the starting point that the business event occurs and the action is taken. The action distance consists of three factors as follows: (1) data latency – the time starting from the point that a business event occurs, relevant data are captured, prepared and stored; (2) analysis latency – the time for data analysis, information generation and delivery to the proper persons, and (3) decision latency – the time to consider and understand all relevant information, make decisions to take the course of action and respond with an intelligent manner [6].

The Value-Time Curve shows the relationship between the (business) value and time to take the action – which represents as a decay function. The business value decreases rapidly after the business event happens, therefore, the faster to take action, the higher to save business value.

(Adapted from [6]) Figure 2. The Value-Time Curve.

1.2. Decision-Making Support Technology Shim et al. [7] stated that computer technology solutions have been used to support complex decision making and problem solving since the late 1950s in terms of DSS and become more significant since the early 1970s. The research areas of DSS technology typically focus on how to improve the “efficiency” of users’ decision making and the “effectiveness” of decisions.

DSS applications can be used to describe any analytical applications that help managers in planning and optimising business goals and objectives, such as production planning, investment portfolio optimisation, Executive Information System, expert system and Online Analytical Processing (OLAP) [8]. DSS remain popular in corporate and academic research publications due to the contribution of the four powerful DSS technologies: data warehouse, OLAP, data mining and World Wide Web (WWW) [7].

Since the early 1990s, Gartner coined the term BI to describe “a set of concepts and methods to improve business decision making by using fact-based support systems” [1], and the term BI also has been used to describe the analytical and decision support applications. Wixom et al. [8] defined BI as “a broad category of technology, applications and processes for gathering, storing, accessing and analysing data to help its users make better decisions”. The authors also stated that BI plays a critical role, impacts to organisational success, is required to compete in the marketplace and changes from being used by a few specialists to many workers.

In today’s economic environment, BI solutions become more important and essential for managing the company intelligently. However, many decisions still are not based on BI because of the limitations to access information and to use suitable BI tools for business analytics. A new development of BI technology called Self-Service BI (SSBI) offers an environment to support and empower end users to create their own BI solutions and making decision faster. The development of SSBI technology is highly growing and the new SSBI functionalities will be launched more into the marketplace [9,10].

Today, the development of SSBI emerged as a new advanced BI technology in the marketplace in order to fulfil this objective. The paradigm shift of SSBI is about the changing in semantic modelling concept which is beyond the traditional BI concept.

Some significant drivers for SSBI requirement are as follows: the businesses need to

change constantly and rapidly, the IT departments are unable to satisfy the business users’ requirements in timely manner, the slow access to information provided by the IT departments, the business users need to do more analytics and the limitation of IT budget (e.g., [11-13]).

SSBI has evolved from BI technology and is defined as “the facilities within the BI environment that enable BI users to become more self-reliant and less dependent on the IT organisation” [12].

Since SSBI tools are diverse, this makes SSBI tools difficult to use for some information workers or with a high risk to be overused by others [11]. An appropriate self-service environment can be provided by knowing the types of information workers, the skill levels of different information workers and the tools of SSBI they need [12], moreover, business users’ skills and the lack of business users’ training are two of the top five inhibitors for SSBI. 1.3. Business Simulation Games Business simulation game is a subset of simulation games which focuses on business content, whereas, the broader definition of simulation game underlying of two concepts: simulation and game. The term “simulation” generally refers to “a representation of a real system, an abstract system, an environment or a process that is electronically generated” [14]. The term “game” is defined as “an artificially constructed, competitive activity with a specific goal, a set of rules and constraints that is located in a specific context” [15]. Cruickshank stated that the term simulation game is used as “one in which participants are provided with simulated environment in which to play” [16].

Faria et al. [17] stated that business simulation games have been developed and used as the vehicles for teaching the business concepts for more than 40 years in universities and companies. The major reasons of using business simulation games were as follows: gained experience, strategy aspects, decision-making, learning outcomes and teamwork experience. The advancement of IT provided more opportunities to improve the learning experience and the way to use business simulation games and also to develop a more complex environment. In addition, business simulation games have moved from being a supplemental tool to a central tool and have become a major form of pedagogy for business education.

Several studies stated that business simulation games enable students to learn how to make decisions, manage the business process in a modern enterprise, link between abstract concepts and real world problems and improve quantitative skills (e.g., [3,18,19]). Furthermore, the new concept of business simulation games, which combines with case-based approaches and experience-based learning theories, results in business simulation games being one of the popular and effective way of education methods [18]. Moreover, games will be one of ICT trends for the future of learning [20].

1.4. Literature Search Result The literature search has been performed and carried out using several online databases: Google Scholar, ScienceDirect, EBSCO, Wiley Online Library, ACM, Springer, IEEE, and Emerald. The initial literature search returned 1,362 articles. There were ten articles meet the criteria and other two articles were added from the references.

The empirical studies showed that some business simulation games provided decision support tools inside the games and some others used the external decision support tools. The reporting in the business simulation games was often based on predefined queries with little flexibilities in using ad-hoc queries. Analytical modules for prediction were restricted to the database of the games. The flexibility for tactical queries and automated decisions were not foreseen. Moreover, business simulation games in the studies were not designed with regard to teaching and learning BI concepts and a new generation of DSS/BI technology. However, the strength was on teaching business scenario.

1.5. Research Gap There is no framework using business simulation games as a BI learning platform that focused in teaching and learning the BI concept and skills, how to use and apply the right SSBI tools, and concerning the decision making latency in the management process.

2. Research Question and Research Methodology 2.1. Research Question 

Would the proposed BI-learning framework be possible to improve students’ BI knowledge and skills, using and applying the right SSBI tools, and decision making skill?

2.2. Research Methodology In order to answer this research question, the survey research and the experimental research methodology will be used.

The prototype of BI-learning framework will be developed. Expert interview will be taken. The search of experts will be considered within Germany and will select at least four experts from both academia and companies. The aim of the interview is to collect the experts’ ideas and recommendation to revise the prototype and framework before the experiment.

The true experimental research will be undertaken and utilizing a between-subjects approach with a pre- and posttest control group design. The procedures are as follows:  Students will be randomly assigned to a control group and an intervention/experimental group.  The control group and experimental group will include at least 30 students each.  Students in the control group will participate only in the pre-testing and posttesting phases of the study.  Students in the control group will play business simulation games and making decision without BI-learning framework or without using DSS/SSBI tools.  Students in the BI-learning framework experimental group will play business simulation games and making decision with BI-learning framework or with using DSS/SSBI tools.

 Both group will be pretested and post-tested on the BI questions and exercises.

The participants will be students from Stuttgart Media University or other universities which have basic knowledge about BI.

3. Plan for Completion Work

2014 Q2

Q3

Q4

2015 Q1

Literature review Select & prepare a business simulation game and learning evaluation engine Develop a prototype Revise the framework & prototype Evaluate & experiment Write thesis

4. Proposed Solution and Expected Contribution The main aim of this thesis is to propose the BI-learning framework, which consists of the conceptual framework (Figure 3) and technical framework (Figure 4), of using DSS on top of business simulation games to teach and learn how to make decisions in the management decision making process. The BI-learning framework will be tested empirically and will be generalizable to any business simulation games, SSBI tools, and learning analytic engines.

4.1. Conceptual Framework For a conceptual framework (Figure 3), business simulation games will be used as an educational platform to simulate the business scenario and business processes. During the cycle of business process, the management decision-making process will be taken in each business activity to support better decision making and decrease the decision making latency.

Q2

Figure 3. A conceptual framework of BI-learning framework.

4.2. Technical Framework A technical framework (Figure 4) will be used to support the conceptual framework. In this framework, data sources from a business simulation game will be extracted, transformed and loaded into data warehouse. Semantic models will model information requirements based on business processes from business simulation game. Semantic models will compose of unified model and physical model such as dimensional fact model (DFM), tabular model or other modelling tools. Data mart and data cube will be built and able to access, analyse, and visualise with SSBI front-end tools. Moreover, learning data and learning activities will be collected and used to measure the learning outcomes.

Figure 4. A technical framework of BI-learning framework.

4.3. Expected Contribution This research will make the following contributions to the body of knowledge as follows:  Providing a generalizable BI-learning framework for any business simulation games, SSBI tools, and learning analytics engines  Providing a prototype of using DSS based on SSBI on top of business simulation games to teach and learn decision making  Providing a semantic layer to support information modelling based on modeldriven approach

5. Conclusions For many years, DSS have been used to improve the quality of managerial decisions.

DSS applications have changed over the last decades, moving from Enterprise Reporting System to Management Information System and nowadays to Business Intelligence Solutions. The issue of teaching and learning DSS is still a big challenge in the academic world, since the DSS-related subjects are still difficult, complex and challenging. Moreover, the demand for well-educated students in the field of DSS is still growing.

This research is working on the frameworks to overcome the restrictions and limitations of the existing DSS teaching solutions. A SSBI solution will be embedded into a business simulation game in order to learn and teach DSS in a modern, integrated and fun-to-use environment to increase the learning outcomes. A platform will be provided to measure and manage students’ learning skills in the field of DSS/BI. Later, the platform will also be used for experiments to measure learning behaviour, with a strong focus on the 21st century skills defined by the European Community [20].

References [1] D.J. Power, A brief history of decision support systems, DSSResources.com (2007).

[2] W.H. Inmon, C. Imhoff, R. Sousa, Corporate Information Factory, John Wiley & Sons, Inc., The United States of America, 2001.

[3] T. Ben-Zvi, The efficacy of business simulation games in creating decision support systems: an experimental investigation, Decision Support Systems and Electronic Commerce 49 (2010), 61-69.

[4] B.H. Wixom, T. Ariyachandra, J. Mooney, State of business intelligence in academia, BI Congress 3, 1516 December 2012, Orlando, FL, USA. [5] P. Gluchowski, R. Gabriel, C.Dittmar, Management Support Systeme und Business Intelligence: Computergestützte Informationssysteme für Fach- und Führungskräfte. Springer-Verlag Berlin Heidelberg, 2008.

[6] R.Hackathorn, Minimizing action distance. The Data Administration Newsletter (2003) http://www.tdan.com/print/5132 [accessed 14.05.2014].

[7] J.P. Shim, M. Warkentin, J.F. Courtney, D.J. Power, R. Sharda, C. Carlsson, Past, present, and future of decision support technology. Decision Support System 33 (2002), 111-126.

[8] B.H. Wixom, H.J. Watson, The BI-based organization. International Journal of Business Intelligence Research 1 (2010), 13-28.

[9] B. Evelson, The Forrester WaveTM: Self-Service Business Intelligence Platforms, Q2 2012. Forrester www.forrester.com [accessed 14.05.2014].

[10] C. Howson, 7 top business intelligence trends for 2013 http://www.informationweek.com/software/information-management/7-top-business-intelligence-trendsfor-2013/d/d-id/1108351? [accessed 14.05.2014].

[11] W. Eckerson, The secrets of Self-Service BI. Blog: Wayne Eckerson – BeyeNETWORK (2012) http://www.b-eye-network.com/blogs/eckerson/archives/2011/01/ the_secrets_of.php [accessed 14.05.2014].

[12] C. Imhoff, C. White, Self-Service Business Intelligence: empowering users to generate Insights, TDWI Best Practices Report (2011).

[13] N. Kulkarni, Embrace the future of BI: Self Service. Information Management (2012) http://www.information-management.com/newsletters/self-service-business-intelligence-bi-tdwikulkarni-10022855-1.html [accessed 14.05.2014].

[14] T. Hainey, Using games-based learning to teach requirements collection and analysis at tertiary education level, PhD Thesis, University of the West of Scotland, 2010.

[15] K.A. Wilson, W.L. Bedwell, E.H. Lazzara, E. Salas, C.S. Burke, J.L. Estock, K.L. Orvis, C. Conkey, Relationships between attributes and learning outcomes: review and research proposals. Simulation & Gaming 40 (2009), 217-266.

[16] T. Connolly, M. Stansfield, Using games-based eLearning technologies in overcoming difficulties in teaching information systems. Journal of Information Technology Education 5 (2006), 459- 476.

[17] A.J. Faria, D. Hutchison, W.J. Wellington, S. Gold, Developments in business gaming: a review of the past 40 years. Simulation & Gamin 40 (2009), 464-487.

[18] M. Wawer, M. Miloz, P. Muryjas, M. Rzemieniak, Business simulation games in forming of students’ entrepreneurship. International Journal of Economics and Management Sciences (IJEMS) 3 (2013).

[19] D. Williams, Impact of business simulation games in enterprise education, The 2010 University of Huddersfield Annual Learning and Teaching Conference, University of Huddersfield, Huddersfield.

[20] C. Redecker, M. Leis, M. Leendertse, Y. Punie, G. Gijsbers, P. Kirschner, S. Stoyanov, B. Hoogveld, The Future of Learning: Preparing for Change. JRC Scientific and Technical Reports,(2011) http://www.researchgate.net/publication/256461836_The_Future_of_Learning_Preparing_for_Change/ file/72e7e522dcb7c5bbec.pdf [accessed 22.05.2014].

Using Self-Service Business Intelligence for Learning Decision Making with Business Simulation Games

Waranya Poonnawat1,2 and Peter Lehmann1 1

Faculty of Information and Communication, Stuttgart Media University, Stuttgart, Germany 2 School of Computing, University of the West of Scotland, Paisley, United Kingdom {poonnawat, lehmann}@hdm-stuttgart.de, waranya.poonnawat@gmail.com

Keywords:

Self-Service Business Intelligence, Business Simulation Games, Decision Support Systems, Business Intelligence, Management Process, Decision Making.

Abstract:

This position paper presents, firstly, the evolution of decision support systems (DSS) and the challenges in teaching in the field of DSS. Secondly, the concepts of management process, decision support technology, self-service business intelligence (SSBI), business simulation games and literature search results on business games associated with DSS are presented. Lastly, we suggest a conceptual framework of using DSS/SSBI on top of business simulation games to support better decision making.

1

INTRODUCTION

Information systems for supporting management decision making, known as Decision Support Systems, or DSS, have been evolving since the mid1960s (Power, 2007). The evolution of DSS concepts remains an important research topic in both industries and universities (e.g., DSS 2.0 Conference, 2014).

Over the last decades, DSS were utilized with some limitations and difficulties, such as heterogeneous data source extraction, multidimensional modelling, business analytics, information workers’ collaboration, multi-channel user interfaces and massive data visualisation.

Meanwhile, the high demand for managing the corporate’s information factory (Inmon, Imhoff and Sousa, 2001) brought the modern and powerful DSS concepts and methods onto the DSS stage, which are compromised under the term “Business Intelligence”, or BI. Starting in the late 90s, Gartner coined the term BI to describe “a set of concepts and methods to improve business decision making by using fact-based support systems” (Power, 2007, p.

11-12). This enables BI applications to function for a wider group of end users and move from the management-focused decision support to the easyto-use decision support to users at all levels of a

company – strategic, tactical and operational.

Decision making in companies is necessary for operating and managing highly optimised business processes. Using BI applications with less support from the information technology (IT) departments is called “Self-Service Business Intelligence”, or SSBI (Imhoff and White, 2011, p. 5). SSBI is a new BI generation beyond traditional BI technology which needs more IT contribution. Using SSBI tools, users have a variety of personal decision support features and functions, for instance creating, searching, exploring, modelling, analysing, visualising, sharing and collaborating to develop their own ad-hoc BI solutions. The complexity of BI functionalities, therefore, is far more powerful than ever and users are able to use SSBI technology within their desktops or spreadsheet applications with a higher degree of independency from the IT departments.

Nevertheless, the BI-related subjects are typical in the field of Information System (IS) and have been taught for many years (Power, 2007). They are still very popular in the academic world. Subjects such as Information Analytics, Management Information System, Business Intelligence and Business Analytics, are based on DSS/BI concepts.

Moreover, Wixom’s survey about the BI status in academia (Wixom, Ariyachandra and Mooney, 2013), had a base of 319 professors from 257

universities in 43 countries around the world. There were many BI-related subjects that have been taught in various academic disciplines, for instance Information System, Decision Science, Statistics, Computer Science, Management Information System, Business Analytics, Operations Research, Supply Chain Management, Economics, Marketing and Accounting.

In the survey’s top message, the question about teaching and learning BI was listed as the most challenging issue. Challenges mentioned included: access to data sets, finding a suitable textbook, finding suitable cases and providing realistic experiences (Wixom et al., 2013).

Consequently, the questions about how to teach and learn BI have arised as follows: (1) “How can students be taught not only the basic concepts about BI and the handling of a BI tool, but also to select the “right” BI tool for making good decisions in the decision making process?”

Since BI/SSBI tools are diverse and often overlap each other, as a consequence they are difficult to use for some users or with a high risk to be overused by other users (Eckerson, 2012).

(2) “What kind of educational platform can be used to teach the effective and efficient usage of BI tools?” Business simulation games are popular and known as one of the most effective education methods, which are widely used for teaching and learning managerial skills, such as making decisions, using management techniques, integrating ideas, applying theory to practice and giving the experiential learning to students (e.g., Ben-Zvi, 2010; Faria, Hutchison, Wellington and Gold, 2009; Lin and Tu, 2012; Wawer, Miloz, Muryjas and Rzemieniak, 2013; Williams, 2011).

In this position paper we suggest a framework to teach and learn BI concepts as a decision supporting method on top of business simulation games. The framework focuses on using SSBI which is a new and powerful BI technology generation for a wide range of decision making by users or information workers. We will focus on SSBI because it gives the opportunities to all kind of users to design DSS/BI models with less IT-technical background needed.

2

MANAGEMENT PROCESS AND DECISION MAKING

DSS/BI technologies are used increasingly to support the management processes, which can be seen as a systematic series of different phases. As

an example for management process the following schema will be used to explain the typical management tasks in four phases: Business Analysis, Decision Taking, Organisation & Steering and Success Controlling (Gluchowski, Gabriel and Dittmar, 2008) (see Figure 1).

Business Analysis Strategic Goals

Marketing Environment

Decision Taking Solution Alternatives Alternatives Evaluation

Decision

Enterprise Environment

Success Controlling

Organisation & Steering

(Adapted from Gluchowski et al., 2008, p. 21)

Figure 1: Phase diagram for management process.

Phase 1 Business Analysis: the managerial decisions always occur along the business processes, which depend on the context of business objectives, internal and external environment. This initial phase focuses on the (permanent) analysis of situations based on three pillars: (1) Strategic Goals – all objectives from all levels of company should be harmonised, not be in conflict and used as a strategic framework to influence and balance with the other two pillars; (2) Marketing Environment – such as competition, economic growth and stability and technological advancements, and (3) Enterprise Environment – such as availability of resources, organisational culture and structure. All activities, that have an impact or influence on the stability of this system have to be observed, analysed and validated.

Phase 2 Decision Taking: this phase emphasizes the planning for taking decisions and consists of three steps: (1) Solution Alternatives – any possible, realistic and relevant alternatives are formulated and collected under a given assumption for any expected future actions; (2) Alternatives Evaluation – all collected alternatives have to be evaluated and compared based on the possible risks, feasibility and implications of each alternative, and (3) Decision – an alternative is selected out from others which has an acceptable risk and is suitable for a specific business situation for further implementations.

Phase 3 Organisation & Steering: the selected alternative has to be implemented and a course of actions has to be undertaken. Therefore, the organisational structure and project management have to be designed and developed in order to transfer any accountabilities, responsibilities and communication through all hierarchical management levels during the implementation period.

Phase 4 Success Controlling: the selected alternative is used as the baseline and the actual results are used to measure and compare with the baseline. The variances have to be analysed, which leads to any new actions and restarts the next cycle of the management process.

The business value, which can be gained from management process, depends on the decision making latency or action distance – the distance between the starting point that the business event occurs and the action is taken. The action distance consists of three factors as follows: (1) data latency – the time starting from the point that a business event occurs, relevant data are captured, prepared and stored; (2) analysis latency – the time for data analysis, information generation and delivery to the proper persons, and (3) decision latency – the time to consider and understand all relevant information, make decisions to take the course of action and respond with an intelligent manner (Hackathorn, 2003).

Figure 2 shows the Value-Time Curve – the relationship between the (business) value and time to take the action – which represents as a decay function. The business value decreases rapidly after the business event happens, therefore, the faster to take action, the higher to save business value. business event

Value

dat

data stored

a la

information delivered

te n cy

value loss

ana

lys

is l

a te

action taken

nc y decisio

n late

nc y

action distance

Time

(Adapted from Hackathorn, 2003)

Figure 2: The Value-Time Curve.

3

DECISION SUPPORT TECHNOLOGY

Shim, Warkentin, Courtney, Power, Sharda and Carlsson (2002) stated that computer technology solutions have been used to support complex decision making and problem solving since the late 1950s in terms of DSS and become more significant since the early 1970s. Classical DSS tools have been designed with three main components: (1) the capabilities to access internal and external data, information and knowledge; (2) the functions for modelling and analysing, and (3) the simplified user interfaces to enable interactive queries, reporting and graphing functions. In addition, the research areas of DSS technology typically focus on how to improve the “efficiency” of users’ decision making and the “effectiveness” of decisions.

DSS applications can be used to describe any analytical applications that help managers in planning and optimising business goals and objectives, such as production planning, investment portfolio optimisation, Executive Information System, expert system and Online Analytical Processing (OLAP) (Wixom and Watson, 2010). In addition, data warehouse technology has emerged to handle massive data, operate OLAP and implement dashboard or scorecard applications for DSS (Power, 2007). DSS remain popular in corporate and academic research publications due to the contribution of the four powerful DSS technologies: data warehouse, OLAP, data mining and World Wide Web (WWW) (Shim et al., 2008).

Since the early 1990s, Gartner coined the term BI and the term BI also has been used to describe the analytical and decision support applications. Wixom et al. (2010, p. 14) also defined BI as “a broad category of technology, applications and processes for gathering, storing, accessing and analysing data to help its users make better decisions”. The authors also stated that BI plays a critical role, impacts to organisational success, is required to compete in the marketplace and changes from being used by a few specialists to many workers.

In today’s economic environment, BI solutions become more important and essential for managing the company intelligently. However, many decisions still are not based on BI because of the limitations to access information and to use suitable BI tools for business analytics. A new development of BI technology called Self-Service BI, or SSBI, offers an environment to support and empower users to create their own ad-hoc BI solutions and making decision faster. The development of SSBI technology is highly growing and the new SSBI functionalities

will be launched more into the marketplace (Evelson, 2012; Howson, 2013).

4

SELF-SERVICE BUSINESS INTELLIGENCE

The concept of personal decision support systems is the oldest form of DSS (Arnott, 2008), and the concept of SSBI has been attempted to integrate in BI systems for many years (Mundy, 2013).

Originally, the objective of both concepts is supporting personal decision making. In recent years, however, the development of SSBI emerged as a new advanced BI technology in the marketplace in order to fulfil this objective. Some significant drivers for SSBI requirement are as follows: the business needs change constantly and rapidly, the IT departments are unable to satisfy the business users’ requirements in timely manner, the slow access to information provided by the IT departments, the business users need to do more analytics and the limitation of IT budget (e.g., Eckerson, 2012; Imhoff and White, 2011; Kulkarni, 2012).

SSBI is defined as “the facilities within the BI environment that enable BI users to become more self-reliant and less dependent on the IT organisation” (Imhoff and White, 2011, p. 5). These facilities focus on four main objectives: (1) to make BI results easy to consume and enhance; (2) to make BI tools easy to use; (3) to make data warehouse solutions fast to deploy and easy to manage, and (4) to make data sources easy to access (Imhoff and White, 2011).

Since SSBI tools are diverse, an appropriate selfservice environment can be provided by knowing the types of information workers, the skill levels of different information workers and the tools or fuctions of SSBI they need (Imhoff and White, 2011). Moreover, Imhoff and White (2011) found that business users’ skills and the lack of business users’ training are two of the top five inhibitors for SSBI.

5

BUSINESS SIMULATION GAMES

Business simulation game is a subset of simulation games which focuses on business content, whereas, the broader definition of simulation game underlying of two concepts: simulation and game. The term “simulation” generally refers to “a representation of a real system, an abstract system, an environment or

a process that is electronically generated” (Hainey, 2010, p. 44). The term “game”, is defined by Hay as “an artificially constructed, competitive activity with a specific goal, a set of rules and constraints that is located in a specific context” (as cited in Wilson, Bedwell, Lazzara, Salas, Burke, Estock, Orvis and Conkey, 2009, p. 2). Cruickshank stated that the term simulation game is used as “one in which participants are provided with simulated environment in which to play” (as cited in Connolly and Stansfield, 2006, p. 466).

Faria et al. (2009) stated that business simulation games have been developed and used as the vehicles for teaching the business concepts for more than 40 years in universities and companies. The major reasons of using business simulation games were as follows: gained experience, strategy aspects, decision-making, learning outcomes and teamwork experience. The advancement of IT provided more opportunities to improve the learning experience and the way to use business simulation games and also to develop a more complex environment. In addition, business simulation games have moved from being a supplemental tool to a central tool and have become a major form of pedagogy for business education.

Several studies stated that business simulation games enable students to learn how to make decisions, manage the business process in a modern enterprise, link between abstract concepts and real world problems and improve quantitative skills (e.g., Ben-Zvi, 2010; Lin and Tu, 2012; Wawer, 2013; Williams, 2011). Furthermore, the new concept of business simulation games, which combines with case-based approaches and experience-based learning theories, results in business simulation games being one of the popular and effective way of education methods (Wawer, 2013).

6

LITERATURE SEARCH

The literature search has been performed to find the empirical studies about business games associated with DSS. The literature search has been done using several online databases – Google Scholar, ScienceDirect, EBSCO, IEEE, Springer, Wiley Online, ACM and Emerald. The terms used for searching from abstracts, titles and keywords, as follows: (“serious games” OR “business games” OR “games-based learning”) AND (“decision support system” OR “management information system”)

The initial search returned 1,362 results, of which ten articles met the criteria - business games associated with using DSS for making decision – and two added articles were found in the references.

The studies showed that some business simulation games provided decision support tools inside the games and some others used the external decision support tools. The reporting in the business simulation games was often based on pre-defined queries with little flexibilities in using ad-hoc queries. Analytical modules for prediction were restricted to the database of the games. The flexibility for tactical queries and automated decisions were not foreseen. Moreover, business simulation games in the studies were not designed with regard to teaching and learning BI concepts.

However, the strength was on teaching business scenario.

7

PROPOSED SOLUTION

This position paper suggests a conceptual framework of using DSS on top of business simulation games to teach and learn decision making (see Figure 4).

Management Process & Decision Making

In the research project, business simulation games will be used as an educational platform to simulate the business scenario. During the business processes, the DSS tools – SSBI – will be applied for each business activity to support the decision making process.

We will select business simulation games which support the representative business processes, evaluate a software platform for DSS provided SSBI functionalities and then attach with the business simulation games, lastly, create criteria for measuring the learning outcomes.

The framework will be used for experiments to prove that whether it is able to support learning and teaching BI, provide a better understanding of using SSBI tools and applications and SSBI technology will help end users to make better (valuable/actionable) decisions.

Furthermore, this framework will integrate an instrument for students and teachers to measure the learning outcomes based on the concept of “learning analytics” (Siemens, Gasevic, Haythornthwaite, Dawson, Shum, Ferguson, Duval, Verbert and Baker, 2011).

8

CONCLUSIONS

Business Analysis Strategic Goals

Marketing Environment

Enterprise Environment

Success Controlling

Decision Taking Solution Alternatives Alternatives Evaluation

Organisation & Steering

Decision

Business Analysis

Business Activity 1

Decision Taking

Success Controlling Organisation & Steering

Business Activity 2

Business Simulation Games Business Activity n Business Analysis

Decision Taking

Success Controlling Organisation & Steering

...

Business Analysis

Decision Taking

Business Activity 3

Success Controlling Organisation & Steering

Business Analysis

Business Activity 4

Decision Taking

Success Controlling Organisation & Steering

Figure 4: A conceptual framework of using DSS on top of business simulation games.

For many years, DSS have been used to improve the quality of managerial decisions. DSS applications have changed over the last decades, moving from Enterprise Reporting System to Management Information System and nowadays to Business Intelligence Solutions. The issue of teaching and learning DSS is still a big challenge in the academic world, since the DSS- or BI-related subjects are still difficult, complex and challenging. Moreover, the demand for well-educated students in the field of DSS is still growing.

We are working on a framework using business simulation games to overcome the restrictions and limitations of the existing DSS teaching solutions.

We will embed a SSBI solution into business simulation games in order to learn and teach DSS/BI in a modern, integrated and fun-to-use environment.

We also believe that the integration of SSBI into business simulation games will increase the learning outcomes. We will provide a platform to measure and manage students’ learning in the field of DSS/BI. Our platform will also be used for experiments to measure learning behaviour, with a strong focus on the 21st century skills defined by the European Community (Redecker, Leis, Leendertse,

Punie, Gijsbers, Kirschner, Stoyanov and Hoogveld, 2011).

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