Description
The main aim of Business Intelligence (BI) is to support decision making in the industry.
Never giving up: Challenges and solutions
when teaching Business Intelligence
Wanda Presthus
The Norwegian School of IT
[email protected]
Abstract The main aim of Business Intelligence (BI) is to support decision making in the
industry. Today, BI is evolving as a subject in business studies in colleges around the world,
which calls for a growing number of lecturers. This paper investigates the perceived
challenges in BI teaching and the solutions from the BI lecturer’s point of view. Ten lecturers
were interviewed at an SAP University Alliance workshop in Cambridge, UK. The data was
analysed using the “BI Dynamics” model by Eckerson. The following findings are offered:
Undergraduate students are eager to learn SAP applications, but less about the process and
effect, while the postgraduates are less eager to learn tools, and have more focus on the
business value. Consequently, the lecturers are embedding all parts of BI (process, analytics,
tools, and applications) simultaneously in the lectures by using real life examples. The study
concludes that although BI is perceived to be radically different and more challenging than
other related subjects such as enterprise resource planning, the lecturers equally find BI more
fun and interesting to teach. These findings should be of interest to any lecturer of BI, as well
as the industry in current need of more BI consultants.
Key words: Business Intelligence, SAP University Alliance, “BI Dynamics”
1 Introduction
Modern Business Intelligence (BI) is “a broad category of technologies, applications, and
processes for gathering, storing, accessing, and analyzing data to help its users make better
decisions” (Wixom and Ariyachandra 2011), p. 1. The term appeared for the very first time in
an IBM article called “A Business Intelligence System” by Luhn in 1958 (Luhn 1958), and
the aim was to analyse textual data automatically for a company. In the following years,
frameworks were developed for decision making (Gorry and Scott Morton 1971) with
corresponding Decisions Support Systems (Turban et al. 2011) for management in an
organisation. Business Intelligence is en evolvement of such Decisions Support Systems
(Power 2007).
Davenport (2010) claims that companies possess enough data and that the challenge is to
exploit these data. In order to make utmost use, the decision maker needs access to various
views of the data. One way of providing such sophisticated views is through reports generated
from multidimensional cubes in a data warehouse. A data warehouse is a physical place in the
centre of the BI environment, and presents a single version of the truth (McDonald et al.
2006).
The industry requires that the student master all the objectives mentioned in the previous
paragraph. Moreover, not only does the student need to learn the technology, but also the
business value of such reporting (Watson 2006). The students are tomorrow’s decision makers
in companies. Ten years ago, Davenport et al reported how companies struggle to turn their
data into business value (Davenport et al. 2001). In an article from 2010, he claims that the
companies are still struggling (Davenport 2010). Some interesting questions arise from the
articles by Watson and Davenport: How can the lecturer of BI provide the best lessons in
order to educate (tomorrow’s) decision makers for the industry? Is teaching BI any different
from teaching more traditional subjects such as for example project management and
databases? Which kind of challenges can a BI lecturer expect to encounter?
Alas, research is limited on teaching BI with both a technical and business focus. For
example, the analysis by Shollo and Kautz reveal that most of the publications on BI were
mainly from a technical perspective, meaning how to turn data into information, and less
about decision making and business value (Shollo and Kautz 2010). The research question of
this paper is therefore: What are the perceived teaching challenges in BI and what are the BI
lecturers’ solutions?
The main aim of this paper is to produce some empirical evidence on teaching BI. The rest of
this paper has the following structure: It starts with a brief literature review of BI in the
industry and in academia. The chosen method is described in some detail, along with the data
analysis. The qualitative findings are presented in a data display and discussed using the “BI
Dynamics” model by Eckerson (2008). Limitations and suggested further research are briefly
addressed before the conclusion.
2 Literature Review of BI
This section briefly introduces BI in the industry and in academia, and illustrates a selection
of the common concepts of SAP Business Warehouse. This literature review results in three
propositions which in turn lays ground for the questions in the interview.
2.1 BI in the industry
The definitions on BI are multiple (Wixom and Watson 2010), however, modern BI is about
decision making (Davenport 2010). Davenport explains that the previous attention was about
automating core business processes through Decision Support Systems (DSS), and his paper
describes how organisations try to improve their decision making by use of information. He
claims that systems and tools can only contribute so far, and that the primary obstacle is still
“traditional understandings of management responsibility”.
Today, BI is employed in virtually every industry, such as retail, transportation, medicine,
bank, and insurance (Turban et al. 2011). The success stories of BI are abundant. Well known
examples are Wachovia Bank (deciding which branch is non-profitable) and Harrah’s
Entertainment (increasing loyalty of existing hotel customers) (Davenport et al. 2001) as well
as Continental Airlines (using a data warehouse to manage today’s flight operations, and
reward frequent flyers) (Wixom et al. 2008). However, in order to achieve such benefits,
companies need to possess employees who are able to handle the technologies, applications
and processes related to BI.
There are several vendors of BI applications (Sallam et al. 2011) and the leading companies
are Microsoft, Oracle, MicroStrategy, IBM, and SAP, as shown in the top, right square in
figure 1.
Figure 1: The Magic Quadrant for Business Intelligence Platforms (Sallam et al. 2011)
This paper focuses on SAP technology, as the case was a SAP University Alliance workshop.
SAP is a large company offering applications for multiple disciplines, including BI, which is
called SAP Netweaver. Netweaver comprises a wide range of large applications such as the
SAP Business Warehouse, and various end-user BI tools for reporting and analysis.
Figure 2: Print screen of creating an InfoCube in SAP Business Warehouse, Version 7.10
(researcher’s own product)
Figure 2 shows an InfoCube for a fictive fruit company. According to McDonald et al (2006)
an InfoCube is a multidimensional data container. Setting up the InfoCube is only a fraction
of the BI process. Having made the cube correctly, appropriate reports must be created
(“Which type of fruit sells most in which month by which store”) or other end-user products
such as a dashboard (“A speedometer should go red if less than five boxes of apples in
stock”).
2.2 BI in academia
According to Power, DSS was initially lectured at business schools in the late 1970s (Power
2007). BI is now being taught at universities around the world (Wixom and Ariyachandra
2011). A search on Google with the words “master in business intelligence” will return about
two million hits, and shows that schools in all corners of the world offer dedicated master
courses in BI. Although BI is becoming a popular subject in several Bachelor and Master’s
studies, research on how to teach BI is only slowly evolving. A literature review study by
Jourdan et al found that the main categories of publications within BI were Artificial
Intelligence, benefits, decision making, implementation, and strategies (Jourdan et al. 2008).
The index of the Business Intelligence Journal
(http://www.highbeam.com/publications/business-intelligence-journal-p436548) reveals little
research on BI and teaching. Watson explains about BI in higher education: “When I…tell
people that I teach BI and data warehousing courses at the University of Georgia, they are
normally interested and sometimes surprised. I get comments…such as: I didn't know those
courses are taught in universities” (Watson 2006), p. 4.
Building on Watson, Fang and Tuladhar (2006) report on a data warehouse course taught at
their university. The learning outcomes included multidimensional modelling and quality of
data. Based on feedback from students, the course was successful; however, they wanted to
learn tools from vendors (illustrated in figure 1 above), and working with real world case
studies. Extending the research of Fang and Tuladhar, Mrdalj states that the main challenge in
teaching BI is the overlap with statistics, databases and various business disciplines (Mrdalj
2007). The author applied SAP Business Warehouse in a Master course, and the students gave
positive feedback. They found the use of SAP Business Warehouse was “extremely useful for
their careers”. The mix of theory and practices, as well as the use of real-world examples, are
enhanced. Regarding challenges, Mrdalj points to providing a large variety of data samples,
and that preparing for such a course is time consuming. In a new publication, Mrdalj
experiments with cloud technology, suggesting that this may contribute to ease of teaching BI
to business students. He concludes that cloud technology allows them to focus on the business
value of BI instead of struggling with the technology (Mrdalj 2011).
Wixom and Ariyachandra recently conducted a survey on the current state of BI in academia
(Wixom and Ariyachandra, 2011). Their survey is of particular interest because they
investigate not only the student, but also the lecturer, as well as the employers and recruiters
in the industry. As this paper focuses mainly on the student and lecturer, the top five
challenges are repeated in table 1 below, respectively.
Rank Challenge according to student Challenge according to lecturer
1 more/better real-world software access to data sets
2 more/better real-world data sets finding suitable cases
3 an in-class BI project finding a suitable textbook
4 clearer link to jobs providing realistic experiences
5 more/better speakers technical support and training
Table 1: Challenges related to the subject of BI in academia (Wixom and Ariyachandra 2011)
As this literature review shows, teaching a BI subject presents certain challenges for the BI
lecturer. Three propositions (Yin, 1994) emerge:
• The BI process is abstract for the students: what is BI, and what are the business
values?
• It is difficult for the business students to learn and master a BI application such as SAP
Business Warehouse.
• BI differs from other subjects and is more difficult to teach for the lecturer.
2.3 BI Dynamics
A framework called “BI Dynamics” (figure 3), based on the comprehensive Systems Theory
by Senge (Eckerson 2008), is used in the discussion. Despite the success stories mentioned
above, there are also several BI projects that fail, and Eckerson has developed a model for
trying to explain why and how to prevent this from happening.
Figure 3: “BI Dynamics” (Eckerson 2008, based on Senge 1990)
According to Eckerson, this framework is useful in Business Intelligence in order to explain
why some BI projects fail or succeed in organisations. The right circle in figure 3 shows a BI
project caught in a negative cycle, due to poor usability of the BI application, which leads to
bad reputation, which again results in use of spreadmarts (renegade BI systems) instead of
the new BI application. The left cycle has the opposite effect. If the BI project can
demonstrate business outcomes such as reduced cost or increased revenue, the sponsors will
provide more funding to support the ongoing project, resulting in more business value and so
on.
This paper suggests that this situation is similar to the failure or success of teaching a BI
subject at a college. If the BI application is too difficult, the lecturer may have to turn to Excel
sheets or diminish the amount of BI tools in the curriculum. This may in turn lead to fewer
students in the next semester. On the other hand, if the lecturer can demonstrate that the
learning outcomes of the BI module is obtained by the use of BI tools, more students will
apply for the course, the school will for example provide funding for new releases of
applications, and teacher’s assistants, for use of BI tools in coming semesters.
The next section describes how data was collected from ten lecturers of SAP Business
Warehouse, and how data was analysed.
3 Methodology
While Wixom and Ariyachandra conducted a comprehensive survey with 173 lectures from
several universities, this research goes more in depth with ten face to face interviews. The aim
is to explore and describe how Business Intelligence as a discipline is being perceived from
the lecturer’s point of view.
Thus, this is a qualitative study using semi-structured interviews and course material from the
SAP University Alliances workshop. Qualitative research, in short, is designed to help us
understand people and the context in which they live (Myers, living version). Myers also
draws attention to the fact that humans can talk about their experiences, as opposed to the
physical objects studied in natural science, where quantitative research has its origin.
“Case studies are a common way to do qualitative inquiry” (Stake 2005) p. 443. In line with
Yin this qualitative research resembles a case study because it is an in-depth study of a real
life phenomenon where the boundaries may be unclear (Yin 1994). Examples of boundaries
are a company’s economy, or a country’s culture and law enforcements. In this study, British
culture may influence the students, as briefly mentioned in the limitations.
3.1 Data collection
Data was collected from ten respondents teaching either at Master or Bachelor level at
universities in UK, as well as instructors of the SAP University Alliances Programme in UK.
This annual workshop aims to provide lecturers “...with access to SAP to enable them to
explore a large enterprise system and to devise new and interesting ways to teach...”
(http://www.sap.com/uk/services/uap/index.epx). The researcher also attended the course and
took part in all of the exercises, one example is figure 2.
All informants were participating in a SAP Business Intelligence workshop at Cambridge
University, UK, in June 2011. The participants were either teaching BI to apprentices at SAP
in Europe, or undergraduates and/or postgraduates at colleges in the UK. Further details are
found in table 2.
Number Title of participant Institution and nationality
1 Director SAP University Alliances UK, Ireland,
BeNeLux
2 Senior Support Engineer SAP Dublin, Ireland. University Alliance,
Dublin
3 Business Intelligence Architect SAP Maidenhead, UK and Europe
4 Senior Support Consultant SAP Ireland
5 Support Consultant SAP University Alliance, Dublin and Cambridge
6 Senior Lecturer in Business
Computing
Sheffield Hallam State University, UK
7 Primary Support Manager SAP UK
8 Senior Lecturer in systems and
operations
University of Central Lancashire, Lancashire
Business School, Preston, UK (UCLAN)
9 Lecturer. Director of Management
Development Programme
University of Strathclyde (Business School),
Glasgow, Scotland
10 Lecturer in systems analysis and
design
Plymouth University, UK
Table 2: The participants with title, affiliated institution, and nationality
The interviews were conducted face to face with semi-structured questions. An assistant typed
the answers consecutively, which allowed the researcher to concentrate on the dialog with
exploratory follow-up questions. The transcripts from each interview were e-mailed to the
appurtenant informant for additional comments and approval. All participants made some
minor corrections or additions, and gave their approval by e-mail. The questions are found in
appendix 1.
3.2 Data analysis
Miles and Huberman (1994) provide a framework for qualitative data analysis which includes
data reduction, data display and conclusions. Building on principles from Miles & Huberman,
the textual data from the interviews were analysed by data reduction and data display, as well
as examining the course material and tools provided by the SAP University Alliance. Then
these findings are discussed against the three propositions from the end of the literature
section, using Eckerson’s framework “BI Dynamics”. Thus, the analysis is conducted in three
steps, as shown in table 3:
Step Description Output (as found in Findings)
1 Data reduction and display Key findings of the interviews: table 3 (and appendix 2)
2 Identify themes by
clustering
6 themes identified: curriculum, student’s
challenge/solution, lecturer’s challenge/solution, how BI
differs from other subjects
3 Possible explanations using
“BI Dynamics”
Findings and conclusion: various support of the propositions
Table 3: Steps in data analysis with outputs
The next section presents the findings from the workshop, along with discussion.
4 Findings and Discussion
The data display shown in table 4 below has the following structure: The themes of the
questions are found in the horizontal columns, and the participants’ numbers correspond to
the ones from table 2 above. The participants have been teaching BI from 1 to 17 years; the
average amounts to 6,5 years. Note that participant 8 had not yet begun to teach BI, but had
taught related subjects including project management, enterprise resource planning and supply
chain management for 13 years. For a more comprehensive table, see appendix 2.
Curriculum Student’s
challenge
Student’s
solution
Lecturer’s
challenge
Lecturer’s
solution
BI versus other
subjects
1 Concepts,
tools
process,
modelling
Data
warehouse,
visualization
Seeing the
value
Get real life
case studies
SAP has
large data
sets
ERP, CRM.
They are easier
2 Tools How
everything
connects
Following
exercises
Not having
own material
Mock-
presentations
SAP ERP.
Harder to teach
SAP BEx
3 Strategy, tools BI technologies Seeing the
value
Students
lack
education
Learning the
product
(Only teach
SAP and BI)
4 Concepts,
process,
modelling
Lack
experience
Determination
to learn
Not having
own material
Make my
own material
Marks &
Spencer, their
system easier
than SAP
5 Metadata.
Practical
issues
Lack
knowledge of
the product
Motivation Keep up to
date with
new releases
Network in
SAP
SAP Finance.
BW more fun
6 Enterprise
Systems/
Architectures,
Web-based IS
Only want to
focus on SAP
Motivation
for SAP
Time to
learn
technology
Delegate
work to
students and
assistant
Microsoft. SAP
more brittle
7 The
integrations of
SAP systems
Learning curve
is slow at first
Struggle more
with process
than
technology
Time Network in
SAP
Anti-virus,
Geo-clustering,
Data
replication.
SAP more
difficult
8 Future BI
module: ERP,
BI theory &
practice
(SAP)
ERP: concept,
complexity,
abbreviations
ERP: By
motivation.
Setting theory
into action
Tool
changes
often
Develop own
material, link
tool to theory
Nothing of what
I teach is
similar to SAP
BI
9 Concepts,
tools,
modelling
Undergraduates
good at
technology,
postgraduates
good at
business
Motivation
and curiosity
Young
students lack
business
experience
Real life
example:
girlfriend
choice
IS management.
BI harder
10 Process, tools,
modelling
Data
modelling, BI
process
Motivation Time to
learn
technology
Work
overtime,
read books
Databases, IS
management.
They differ a lot
Table 4: Data display with key finding of the interviews
Each theme is discussed in accordance to previous literature.
Curriculum: When Watson taught BI and Data Warehousing in 2006, his learning objectives
included introduction to the disciplines, applications of data warehouse and BI tools,
modelling, and case studies. Fang and Tuladhar’s learning objectives were modeling, data
warehousing and BI products such as queries. Mrdailj’s course covered the same objectives,
but also used SAP Business Warehouse as hands-on application for BI, as well has having a
strong focus on the business process and value. In this study, the curriculum of the ten
participants covers all of these objectives; “concepts, BI process, modelling, tools, strategy,
integration, analysis”. The participants working for SAP have a slightly higher focus on the
technical components of the system, such as InfoCubes (explained above) and modelling, but
they also include a holistic understanding of how the system is integrated with business
processes and value. Only one participant commented that “BI is not the same as I thought it
would be when I started 5 years ago. Now the tech is disappearing and it is more focus on
business perspective, such as KPI, sales, service, retail, research.” Consequently, the
curriculum of BI is the same after half a decade, but has grown to include business value.
Challenges and solutions for students: This paper is not only about college students, but
also apprentices in companies. Are there any differences between college students and
company apprentices when it comes to challenges? The recent study of Wixom and
Ariyachandra (2011) provides a ranked list of challenges. Recall from table 1 that the
student’s top five major challenges were more real-world software and data sets, in-class BI
project, clearer link to jobs, and better speakers.
The results of the ten semi-structured interviews support only one of these challenges. Eight
of the participants list “learning the application” as one of the students main challenges. Thus,
the problem in this study is not having access to real-world software, but to master it. Other
features found in this study are difficulties in understanding BI concepts and abbreviations,
and the whole process of BI, which is defined by Turban et al. (2011) as turning data into
actionable information which again can provide business value.
There are several reasons for this divergence. First, the answers were given from how the
lecturer perceives the student struggle. If the student was asked personally, the answers could
be different. Moreover, six of the ten respondents in this study teach SAP trainees, and not
college students, and obviously these trainees do not need any in-class BI projects or a clear
link to jobs, as they find themselves in the very middle of it.
The course material provided by SAP is very thorough, with step-by-step explanations with
illustrative print screens. Each theme and exercise was carefully walked through by the SAP
staff. However, no matter how detailed the material, the participants at the workshop needed
follow-up, and made additional notes in the course book.
Challenges and solutions for lecturer: According to Wixom and Ariyachandra (2011) the
lecturer’s challenges were access to data sets, finding suitable cases and textbook, providing
realistic experiences, and technical support and training. Interestingly, all of these obstacles
are supported by the data from this study. Data sets and cases are difficult to provide, as
participant 1 explains: “It is difficult to get hold of real life case studies because people and
companies are secretive about their analysis and data”. Participant 2 and 4 clarify that even
though they have ready-made presentations and material at SAP, they prefer to create their
own: “You should use your own Powerpoints rather than material from others”.
Regarding the technical support and training, one might expect that this issue should be less
challenging for the participants of this study, since six of them are in fact SAP employees.
Actually, the majority of the lecturers still declare that teaching the technology is challenging.
However, as one might expect, the challenges are even larger for the lecturers employed at the
university than at SAP. Participant 6 draws a rather bleak picture: “Undergraduate students
have no awareness of SAP. Unless you get it right, they end up hating SAP. So we have to be
careful how we present it.”
The participants, even the ones working for SAP, report that finding large data sets are
challenging. The reason for this seems to be that customers of SAP regard their data as “gold”
and providing competitive advantage. The workshop in SAP Business Intelligence did
provide selected data sets from a fictive bike company. Although fictive, the sets were
containing metadata and gave a realistic view of real-life reporting. Apart from the pre-
prepared datasets, this case study does not reveal any other ways of addressing this challenge.
Possible difference BI and related subjects taught by the lecturer: Is teaching BI any
different than teaching for example ERP-systems, or IT project management? Participant 8
has no doubt: “Nothing of what I teach is similar to BI”. This participant teaches multiple
subjects: supply chain management, project management and enterprise resource planning,
which should provide a basis for comparison. Actually, nine of the ten participants quickly
labelled BI as different from other subjects they teach, with typically reads enterprise resource
planning, customer relationship management, finance, and data bases. The last participant,
number 3, has exclusively taught BI and SAP, so this participant cannot contribute in this
category.
Having established that BI is distinctively different from related subject, does this mean that it
is more difficult? Recall participant 6 warning that the lecturer only gets one shot at “getting
it right”, at least when it comes to learning SAP, and according to two participants 6 and 9,
learning SAP is what the students want to do, more than learning about processes and
modelling: “When we introduce them to research approaches, they only want to focus on the
SAP part”, explains participant 6.
Despite such challenges, four participants finds teaching BI and SAP Business Warehouse
easier than for example finance. Several participants declare the reason is that the students are
motivated, and participant 5 adds that BI is fun and interesting to teach. Other ways of coping
with the challenges are suggested by participant 9: “It helps to have four hour sessions
instead of two”. The students need to “warm up” with the tools/thinking/reflecting about the
problems” and participant 4 shares: “I enjoy training. Nice to have training with interaction –
meaning that it is nice to have a non-traditional classroom, as it makes people interact
more.”
By using a semi-structure interview, a fascination finding emerged from this study:
motivation and fun. Motivation is highly present, coming from both the student and lecturer.
From the students’ point of view, it seems that they accept some pain for gain. Participant 9
argues that “the students want to obtain a diploma with SAP on it”. The lecturer is equally
motivated, quoting participant 4: “Students never get BI straightaway. However, I go through
3-4 times to make sure everybody gets it, using different approaches. I never give up!”
Implications for the BI lecturer
All of the participants inform that teaching BI has certain challenges, and that the tools are
difficult. Poor usability is critical for ending up in the negative cycle (Eckerson, 2008). Given
the rather bleak statements from the participants “lecturer must get it right, or the students
will hate SAP”, this should make a BI course vulnerable of getting caught in a negative cycle
in the “BI Dynamics” model. Figure 2 hardly seems intuitive, and along with the SAP
concepts such as “InfoCube” one might expect that this would result in poor usability.
Conversely, none of the participants report of such a negative cycle. The lecturers do not even
appear to have a “plan B” if SAP Business Warehouse should fail. Eckerson declares that if
an organisation is caught in a negative cycle, it will stay there until the BI project is cancelled
or pulled out by the BI team somehow – the latter often by delivering a “quick-hit” product
with is easy to use. A “plan B” for a lecturer could be making a dashboard or using Excel for
demonstrating reporting and analysis. However, all respondents were determined to follow
their initial plan with modelling, making multidimensional cubes, and creating products such
as reports in SAP.
So why are there no sign of any negative loops in this case? Eckerson (2008) writes that he
has never seen a successful Bl projects without a top-down management, meaning strong
sponsorship. In this paper, one argues that this transfers to a dedicated lecturer. The lecturer
needs to possess the technical skills for the BI tool as well as enthusiasm for the discipline.
This may seem obvious for any subject at any school. However, according to Eckerson, once
you have passed this “magical tipping point”, “it will seem like there is nothing you can’t
accomplish” (p. 11). Attending workshops such as the ones SAP gives for Business
Intelligence should therefore be a safe investment of time and effort a BI lecturer. However,
there is one element which emerged from the interviews, and which is lacking from
Eckerson’s model: fun. What makes BI fun? BI is a hot topic in these days, constantly
evolving with visualisation, “big data”, predictive analysis, social media analysis, and more.
Summing up, each proposition is repeated with various degrees of support:
Proposition 1: The BI process is abstract for the students: what is BI, and what are the
business values? Indeed, this is a perceived challenge from the lecturer’s point of view. The
undergraduate students are eager to start learning the tools more than they are of modelling
and reflecting on the value of the technology, and the postgraduates are more focused on the
business value and more reluctant to learn SAP. This implies that this proposition is half-way
supported: The BI process is abstract for the undergraduates, but less for the postgraduates.
Proposition 2: It is difficult for the students to learn and master a BI application such as
SAP Business Warehouse. Even for the SAP staff, SAP Business Warehouse with reporting
tools are perceived as complex and “huge”, and the students rarely have prior experience with
any SAP technology, as they may have with for example Microsoft. One participant claims
that the lecturer only gets one shot at presenting SAP, meaning that it has to be taught
correctly or the students will “end up hating SAP”. Despite this high threshold, the majority
of the students are eager to learn the application, as they believe that knowledge of SAP
technology is valued and on demand in working life.
Proposition 3: BI differs from other subjects and is more challenging to teach.
Surprisingly, the two parts of this proposition do not correlate. The first part of this
proposition, “Bi differs from other subjects”, may be the most unambiguous finding. Nine out
of the ten participants classifies BI as distinctly different from related subjects such as
relational databases, knowledge management, enterprise resource planning, and finance. The
reason for this difference is explained as follows: Relational databases require mostly
technology skills, while finance demands mainly business understanding. BI is broader, like
an umbrella: In addition to handling a complex tool with multiple functions and visualisation,
BI is also focused around business processes which require analytical and structured skills
from the student. This study also finds that undergraduates are more interested in learning the
technology than the business value, and vice versa for the post graduates.
However, regarding the last part of the proposition, only four of the ten informants find BI
more difficult to teach. The reason is that the students see the value and they are very
motivated for learning BI, despite the high threshold of mastering SAP Business Warehouse.
Perhaps this is what makes all of the lectures equally motivated for teaching BI. Even though
teaching BI is perceived as more difficult than teaching other subjects, the lecturers never give
up.
Limitations and suggested further research
There are limitations to this paper. First, one has to acknowledge that ten participants make a
small study. Second, the findings were based on participants at a SAP University Alliance
workshop, and this case will influence the answers given. Further research can reveal whether
applications from other BI vendors are perceived the same way. Third, the UK school system
and students may be different from other countries; for example, this paper does not address
whether the UK schools are more or less authoritarian then other nations. For example, one
participant commented that “British students need to be kept engaged. They have a low
boredom threshold”. Finally, as the majority of the participants stated that BI differs from
related subjects, additional research can explore these differences in more depth.
5 Conclusion
This paper has investigated: What are the perceived teaching challenges in BI and what are
the BI lecturers’ solutions? Drawing on the three propositions as background for the semi-
structured interviews, the following conclusions are offered: Proposition 1: teaching the BI
process and potential business value is challenging, but the undergraduates will struggle more
than the postgraduates to learn. Proposition 2: The opposite is true for teaching the technology
such as SAP Business Warehouse, where the lectures report that the master students struggle
more than the bachelor students. Proposition 3, however, is divided: BI does differs from
other subjects, but it is not harder to teach. The lecturers’ solutions to the above are to provide
real-life examples and making sure they master the tools themselves before teaching the
students.
Hopefully, this paper can motivate teachers of BI and provide ideas on how to accomplish
even more successful and interesting BI courses, thus remaining in the positive reinforcing
cycle from the “BI Dynamics”.
Acknowledgment
The author would like to thank all ten participants for providing comprehensive data for this
paper, and Knut Urbye for transcripts and comments. Additional gratitude goes to SAP
University Alliances in the UK.
References
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of Business Intelligence Research, 1(1), pp. 1-12.
Davenport, T. H., Harris, J. G., De Long, D. W., and Jacobsen, A. L. (2001). “Data to Knowledge to
Results: Building an Analytic Capability.” California Management Review, 43(2), pp.117-
138.
Eckerson, W. W. (2008). “Pervasive Business Intelligence – Techniques and Technologies to Deploy
BI on an Enterprise Scale” TDWI Best Practice Report Available at:
http://www.tdwi.org/research/reportseries/reports.aspx.
Fang, R., and Tuladhar, S. (2006). “Teaching data warehousing and data mining in a graduate program
of information technology.” Consortium for Computing Sciences in Colleges, Journal of
Computing Sciences in Colleges, 5, pp. 137-144.
Gorry, G. A., and Scott Morton, M. S. (1989). “A Framework for Management Information Systems.”
Sloan Management Review Classic Reprint, Spring 1989, pp. 49-61.
Jourdan, Z., Rainer, R. K., and Marshall, T. E. (2008). “Business Intelligence: An Analysis of the
Literature.” Information Systems Management, 25(2), pp. 121-131.
Luhn, H. P. (1958). “A Business Intelligence System.” IBM Journal of Research and Development,
2(4), pp. 314-319
McDonald, K., Wilmsmeier, A., Dixon, D. C., and Inmon, W. H. (2006). Mastering the SAP Business
Information Warehouse, Second Edition: Wiley.
Miles, M. B., and Huberman, A. M. (1994). Qualitative Data Analysis: Thousand Oaks: Sage
Publications.
Mrdalj, S. (2007). “Teaching an applied Business Intelligence course.” Issues in Information Systems,
VIII(1), pp. 134-138.
Mrdalj, S. (2011). “Would Cloud Computing Revolutionize Teaching Business Intelligence Courses?”
Issues in Informing Science and Information Technology, 8, pp. 209-217.
Myers, M. D. (living version). “Qualitative Research in Information Systems.” The University of
Auckland, New Zealand. Originally published in MISQ Discovery, June 1997.
Power, D. J. (2007). A Brief History of Decision Support Systems, version 4.0, March 10, 2007.
Available at: http://DSSResources.COM/history/dsshistory.html
Sallam, R. L., Richardson, J., Hagerty, J., and Hostmann, B. (2011). “Magic Quadrant for Business
Intelligence Platforms.” Gartner Group, 27. January 2011, pp. 1-31.
Shollo, A., and Kautz, K. (2010). “Towards an Understanding of Business Intelligence” ACIS 2010
Proceedings. Paper 86.
Stake, R. E. (2005). “Qualitative Case Studies”, in N. L. Denzin, Y., (ed.), The Sage Handbook of
Qualitative Research. Sage Publications, pp. 443-466.
Turban, E., Sharda, R., and Delen, D. (2011). Decision Support and Business Intelligence Systems:
Prentice Hall.
Watson, H. J. (2006). “BI and Data Warehousing in Universities.” Business Intelligence Journal,
11(3), pp. 4-6.
Wixom, B. H., and Ariyachandra, T. (2011) “State of Business Intelligece in Academia 2010.”
Presented at BI Congress II.
Wixom, B. H., and Watson, H. J. (2010). “The BI-Based Organization.” International Journal of
Business Intelligence Research, 1(1), pp. 13-28.
Wixom, B. H., Watson, H. J., Reynolds, A. M., and Hoffer, J. A. (2008). “Continental Airlines
Continues to Soar with Business Intelligence.” Information Systems Management, 25, pp. 102-
112.
Yin, R. K. (1994). Case study research. Design and Methods 2nd ed.: Sage Publications, Inc.
Appendix: semi-structured interview
1. What is your title?
2. Where do you teach (name of University and nationality)?
3. How long have you taught BI?
4. What are the 3 major parts of your curriculum? (Some examples: Conceptions and
abbreviations/BI process (turning data into actionable information)/Effects of BI
(faster/more accurate reporting, saving money, more satisfied customers)/Data
modelling/Use of tools)
5. What are the students’ major challenges related to BI?
6. How do the students cope with the challenges related to BI? (Some examples: By
motivation/ By being analytical/By drawing on skills and prerequisites)
7. What are your major challenges as a BI lecturer? Examples?
8. How do you solve the challenges as a BI lecturer? Examples?
9. Do you teach other subjects?
10. If yes: which ones, and do they differ from BI as a subject when it comes to
challenges?
11. Age group of students?
12. Any comments?
Appendix: data display of key findings of the interviews
Q = Question
P = Participant
Q: P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P 10
How
long
taught
BI?
3 years 3 years 17 years About 1
year
About 2,5
years
15 years 11 years N/A Over 10
years
About 5
years
What are
major
parts of
curriculu
m?
Concepts, BI
process, data
modelling,
tools
Tools:
Business
Warehouse,
BEx,
Broadcastin
g
Strategy to
meet
business
requiremen
ts. The
right tool in
the right
way
Terminolog
y. BI
process.
Data
modelling.
Metadata.
Practical
issues that
help
colleagues
do their
jobs
Enterprise
Systems/Ar
chitectures,
Web-based
IS
The
unique
integratio
ns of SAP
systems
Future BI
module:
ERP, BI
theory &
practice
(SAP)
Concepts
and
abbreviati
ons,
modelling,
tools
The BI
process,
modelling,
ETL,
OLAP,
Dashboar
ds
What are
students’
major
challenge
s?
Data
warehousing,
visualization
How
everything
connects
together
BI differs
from other
technologie
s
Students
never get
BI
straightawa
y. Lack
experience
Lack of
knowledge
on the
product.
Never seen
lack of
motivation
They only
want to
focus on the
SAP part
Learning
curve is
slow at
first
ERP: The
system’s
complexit
y,
concept,
and
abbreviati
ons
Undergrad
uates good
at
technolog
y ,
postgradu
ates good
at
business
Data
modelling,
BI process
How do
the
students
cope?
See the value.
I teach
concepts
early. They
draw on skills
Reviewing
material,
following
exercises
See the
value
Determined
to learn
By
motivation
Students
are
motivated
for SAP
Struggle
less with
technolog
y, but
more on
process
ERP: By
motivatio
n. Setting
theory
into action
By
motivatio
n and
profession
al
curiosity
By
motivatio
n. They
like hands
on
experienc
e.
What are
your
major
challenge
s as a
lecturer?
What BI is all
about. Get
hold of real
life case
studies
Making the
material
my own
Customer’s
challenge
tends to be
lack of
education
Improve
communica
tion. Not
having own
Powerpoint
s
Keep up to
date with
new
releases
Time to
learn
technology
Time and
structure
of
informatio
n
ERP more
strenuous
to teach. It
changes
often.
Young
students
lack
business
experience
, but easy
to
motivate
Time to
learn new
technolog
y
How do
you solve
the
challenge
s?
SAP has large
data sets
Review
materials.
Mock-
presentatio
ns
Proper
education
of the
product and
adoption of
best
practice
Ramp up
people over
a year.
Make my
own
material
Trough
internal
network. I
cope with
technical
issues
myself
Delegate
work to
students
and
assistant
Network
in SAP,
but
difficult to
find the
right
person
Develop
own
teaching
material,
and link it
to theory
Real life
example:
girlfriend
choice,
business
case
studies
Work
overtime,
read
books
Do you
teach
other
subjects?
– if yes,
do they
differ
from BI?
ERP, CRM,
ByD – all are
easier/more
“old school”,
less
visualization
ERP, SAP
Enterprise
Portal.
Harder to
teach SAP
BEx
N/A
(Only SAP
and BI)
Classroom
training at
Marks &
Spencer,
their
system
easier than
SAP
SAP
Finance=
boring, BW
= more fun
Students
are used to
Microsoft,
not SAP.
SAP more
brittle
Anti-
virus,
Geo-
clustering,
Data
replication
. SAP
more
difficult
Nothing
of what I
teach is
similar to
SAP BI.
IS
manageme
nt.
BI harder:
need
prerequisit
es in
understan
ding
Relational
database,
Software
managem
ent. They
differ a lot
Age of
students?
19-20 and 25-
30
25-35 30-50 20-30 25 to 35 18-21, 20-
30
25-28,
early 40
21-23, 25-
30
18-20, 25-
30, 40-60
20+,
Some in
50’s.
Any
comment
s?
British
students need
to be kept
engaged
Exercises
make the
student
learn twice
as much
No I enjoy
training, in
a non-
traditional
classroom
No No 1500+
people
work in
SAP UK.
I support
products
Students
more
interested
in SAP
than
theory
Students
want to
obtain a
SAP
diploma
The tech
is
disappeari
ng in BI,
focus on
business
doc_985713105.pdf
The main aim of Business Intelligence (BI) is to support decision making in the industry.
Never giving up: Challenges and solutions
when teaching Business Intelligence
Wanda Presthus
The Norwegian School of IT
[email protected]
Abstract The main aim of Business Intelligence (BI) is to support decision making in the
industry. Today, BI is evolving as a subject in business studies in colleges around the world,
which calls for a growing number of lecturers. This paper investigates the perceived
challenges in BI teaching and the solutions from the BI lecturer’s point of view. Ten lecturers
were interviewed at an SAP University Alliance workshop in Cambridge, UK. The data was
analysed using the “BI Dynamics” model by Eckerson. The following findings are offered:
Undergraduate students are eager to learn SAP applications, but less about the process and
effect, while the postgraduates are less eager to learn tools, and have more focus on the
business value. Consequently, the lecturers are embedding all parts of BI (process, analytics,
tools, and applications) simultaneously in the lectures by using real life examples. The study
concludes that although BI is perceived to be radically different and more challenging than
other related subjects such as enterprise resource planning, the lecturers equally find BI more
fun and interesting to teach. These findings should be of interest to any lecturer of BI, as well
as the industry in current need of more BI consultants.
Key words: Business Intelligence, SAP University Alliance, “BI Dynamics”
1 Introduction
Modern Business Intelligence (BI) is “a broad category of technologies, applications, and
processes for gathering, storing, accessing, and analyzing data to help its users make better
decisions” (Wixom and Ariyachandra 2011), p. 1. The term appeared for the very first time in
an IBM article called “A Business Intelligence System” by Luhn in 1958 (Luhn 1958), and
the aim was to analyse textual data automatically for a company. In the following years,
frameworks were developed for decision making (Gorry and Scott Morton 1971) with
corresponding Decisions Support Systems (Turban et al. 2011) for management in an
organisation. Business Intelligence is en evolvement of such Decisions Support Systems
(Power 2007).
Davenport (2010) claims that companies possess enough data and that the challenge is to
exploit these data. In order to make utmost use, the decision maker needs access to various
views of the data. One way of providing such sophisticated views is through reports generated
from multidimensional cubes in a data warehouse. A data warehouse is a physical place in the
centre of the BI environment, and presents a single version of the truth (McDonald et al.
2006).
The industry requires that the student master all the objectives mentioned in the previous
paragraph. Moreover, not only does the student need to learn the technology, but also the
business value of such reporting (Watson 2006). The students are tomorrow’s decision makers
in companies. Ten years ago, Davenport et al reported how companies struggle to turn their
data into business value (Davenport et al. 2001). In an article from 2010, he claims that the
companies are still struggling (Davenport 2010). Some interesting questions arise from the
articles by Watson and Davenport: How can the lecturer of BI provide the best lessons in
order to educate (tomorrow’s) decision makers for the industry? Is teaching BI any different
from teaching more traditional subjects such as for example project management and
databases? Which kind of challenges can a BI lecturer expect to encounter?
Alas, research is limited on teaching BI with both a technical and business focus. For
example, the analysis by Shollo and Kautz reveal that most of the publications on BI were
mainly from a technical perspective, meaning how to turn data into information, and less
about decision making and business value (Shollo and Kautz 2010). The research question of
this paper is therefore: What are the perceived teaching challenges in BI and what are the BI
lecturers’ solutions?
The main aim of this paper is to produce some empirical evidence on teaching BI. The rest of
this paper has the following structure: It starts with a brief literature review of BI in the
industry and in academia. The chosen method is described in some detail, along with the data
analysis. The qualitative findings are presented in a data display and discussed using the “BI
Dynamics” model by Eckerson (2008). Limitations and suggested further research are briefly
addressed before the conclusion.
2 Literature Review of BI
This section briefly introduces BI in the industry and in academia, and illustrates a selection
of the common concepts of SAP Business Warehouse. This literature review results in three
propositions which in turn lays ground for the questions in the interview.
2.1 BI in the industry
The definitions on BI are multiple (Wixom and Watson 2010), however, modern BI is about
decision making (Davenport 2010). Davenport explains that the previous attention was about
automating core business processes through Decision Support Systems (DSS), and his paper
describes how organisations try to improve their decision making by use of information. He
claims that systems and tools can only contribute so far, and that the primary obstacle is still
“traditional understandings of management responsibility”.
Today, BI is employed in virtually every industry, such as retail, transportation, medicine,
bank, and insurance (Turban et al. 2011). The success stories of BI are abundant. Well known
examples are Wachovia Bank (deciding which branch is non-profitable) and Harrah’s
Entertainment (increasing loyalty of existing hotel customers) (Davenport et al. 2001) as well
as Continental Airlines (using a data warehouse to manage today’s flight operations, and
reward frequent flyers) (Wixom et al. 2008). However, in order to achieve such benefits,
companies need to possess employees who are able to handle the technologies, applications
and processes related to BI.
There are several vendors of BI applications (Sallam et al. 2011) and the leading companies
are Microsoft, Oracle, MicroStrategy, IBM, and SAP, as shown in the top, right square in
figure 1.
Figure 1: The Magic Quadrant for Business Intelligence Platforms (Sallam et al. 2011)
This paper focuses on SAP technology, as the case was a SAP University Alliance workshop.
SAP is a large company offering applications for multiple disciplines, including BI, which is
called SAP Netweaver. Netweaver comprises a wide range of large applications such as the
SAP Business Warehouse, and various end-user BI tools for reporting and analysis.
Figure 2: Print screen of creating an InfoCube in SAP Business Warehouse, Version 7.10
(researcher’s own product)
Figure 2 shows an InfoCube for a fictive fruit company. According to McDonald et al (2006)
an InfoCube is a multidimensional data container. Setting up the InfoCube is only a fraction
of the BI process. Having made the cube correctly, appropriate reports must be created
(“Which type of fruit sells most in which month by which store”) or other end-user products
such as a dashboard (“A speedometer should go red if less than five boxes of apples in
stock”).
2.2 BI in academia
According to Power, DSS was initially lectured at business schools in the late 1970s (Power
2007). BI is now being taught at universities around the world (Wixom and Ariyachandra
2011). A search on Google with the words “master in business intelligence” will return about
two million hits, and shows that schools in all corners of the world offer dedicated master
courses in BI. Although BI is becoming a popular subject in several Bachelor and Master’s
studies, research on how to teach BI is only slowly evolving. A literature review study by
Jourdan et al found that the main categories of publications within BI were Artificial
Intelligence, benefits, decision making, implementation, and strategies (Jourdan et al. 2008).
The index of the Business Intelligence Journal
(http://www.highbeam.com/publications/business-intelligence-journal-p436548) reveals little
research on BI and teaching. Watson explains about BI in higher education: “When I…tell
people that I teach BI and data warehousing courses at the University of Georgia, they are
normally interested and sometimes surprised. I get comments…such as: I didn't know those
courses are taught in universities” (Watson 2006), p. 4.
Building on Watson, Fang and Tuladhar (2006) report on a data warehouse course taught at
their university. The learning outcomes included multidimensional modelling and quality of
data. Based on feedback from students, the course was successful; however, they wanted to
learn tools from vendors (illustrated in figure 1 above), and working with real world case
studies. Extending the research of Fang and Tuladhar, Mrdalj states that the main challenge in
teaching BI is the overlap with statistics, databases and various business disciplines (Mrdalj
2007). The author applied SAP Business Warehouse in a Master course, and the students gave
positive feedback. They found the use of SAP Business Warehouse was “extremely useful for
their careers”. The mix of theory and practices, as well as the use of real-world examples, are
enhanced. Regarding challenges, Mrdalj points to providing a large variety of data samples,
and that preparing for such a course is time consuming. In a new publication, Mrdalj
experiments with cloud technology, suggesting that this may contribute to ease of teaching BI
to business students. He concludes that cloud technology allows them to focus on the business
value of BI instead of struggling with the technology (Mrdalj 2011).
Wixom and Ariyachandra recently conducted a survey on the current state of BI in academia
(Wixom and Ariyachandra, 2011). Their survey is of particular interest because they
investigate not only the student, but also the lecturer, as well as the employers and recruiters
in the industry. As this paper focuses mainly on the student and lecturer, the top five
challenges are repeated in table 1 below, respectively.
Rank Challenge according to student Challenge according to lecturer
1 more/better real-world software access to data sets
2 more/better real-world data sets finding suitable cases
3 an in-class BI project finding a suitable textbook
4 clearer link to jobs providing realistic experiences
5 more/better speakers technical support and training
Table 1: Challenges related to the subject of BI in academia (Wixom and Ariyachandra 2011)
As this literature review shows, teaching a BI subject presents certain challenges for the BI
lecturer. Three propositions (Yin, 1994) emerge:
• The BI process is abstract for the students: what is BI, and what are the business
values?
• It is difficult for the business students to learn and master a BI application such as SAP
Business Warehouse.
• BI differs from other subjects and is more difficult to teach for the lecturer.
2.3 BI Dynamics
A framework called “BI Dynamics” (figure 3), based on the comprehensive Systems Theory
by Senge (Eckerson 2008), is used in the discussion. Despite the success stories mentioned
above, there are also several BI projects that fail, and Eckerson has developed a model for
trying to explain why and how to prevent this from happening.
Figure 3: “BI Dynamics” (Eckerson 2008, based on Senge 1990)
According to Eckerson, this framework is useful in Business Intelligence in order to explain
why some BI projects fail or succeed in organisations. The right circle in figure 3 shows a BI
project caught in a negative cycle, due to poor usability of the BI application, which leads to
bad reputation, which again results in use of spreadmarts (renegade BI systems) instead of
the new BI application. The left cycle has the opposite effect. If the BI project can
demonstrate business outcomes such as reduced cost or increased revenue, the sponsors will
provide more funding to support the ongoing project, resulting in more business value and so
on.
This paper suggests that this situation is similar to the failure or success of teaching a BI
subject at a college. If the BI application is too difficult, the lecturer may have to turn to Excel
sheets or diminish the amount of BI tools in the curriculum. This may in turn lead to fewer
students in the next semester. On the other hand, if the lecturer can demonstrate that the
learning outcomes of the BI module is obtained by the use of BI tools, more students will
apply for the course, the school will for example provide funding for new releases of
applications, and teacher’s assistants, for use of BI tools in coming semesters.
The next section describes how data was collected from ten lecturers of SAP Business
Warehouse, and how data was analysed.
3 Methodology
While Wixom and Ariyachandra conducted a comprehensive survey with 173 lectures from
several universities, this research goes more in depth with ten face to face interviews. The aim
is to explore and describe how Business Intelligence as a discipline is being perceived from
the lecturer’s point of view.
Thus, this is a qualitative study using semi-structured interviews and course material from the
SAP University Alliances workshop. Qualitative research, in short, is designed to help us
understand people and the context in which they live (Myers, living version). Myers also
draws attention to the fact that humans can talk about their experiences, as opposed to the
physical objects studied in natural science, where quantitative research has its origin.
“Case studies are a common way to do qualitative inquiry” (Stake 2005) p. 443. In line with
Yin this qualitative research resembles a case study because it is an in-depth study of a real
life phenomenon where the boundaries may be unclear (Yin 1994). Examples of boundaries
are a company’s economy, or a country’s culture and law enforcements. In this study, British
culture may influence the students, as briefly mentioned in the limitations.
3.1 Data collection
Data was collected from ten respondents teaching either at Master or Bachelor level at
universities in UK, as well as instructors of the SAP University Alliances Programme in UK.
This annual workshop aims to provide lecturers “...with access to SAP to enable them to
explore a large enterprise system and to devise new and interesting ways to teach...”
(http://www.sap.com/uk/services/uap/index.epx). The researcher also attended the course and
took part in all of the exercises, one example is figure 2.
All informants were participating in a SAP Business Intelligence workshop at Cambridge
University, UK, in June 2011. The participants were either teaching BI to apprentices at SAP
in Europe, or undergraduates and/or postgraduates at colleges in the UK. Further details are
found in table 2.
Number Title of participant Institution and nationality
1 Director SAP University Alliances UK, Ireland,
BeNeLux
2 Senior Support Engineer SAP Dublin, Ireland. University Alliance,
Dublin
3 Business Intelligence Architect SAP Maidenhead, UK and Europe
4 Senior Support Consultant SAP Ireland
5 Support Consultant SAP University Alliance, Dublin and Cambridge
6 Senior Lecturer in Business
Computing
Sheffield Hallam State University, UK
7 Primary Support Manager SAP UK
8 Senior Lecturer in systems and
operations
University of Central Lancashire, Lancashire
Business School, Preston, UK (UCLAN)
9 Lecturer. Director of Management
Development Programme
University of Strathclyde (Business School),
Glasgow, Scotland
10 Lecturer in systems analysis and
design
Plymouth University, UK
Table 2: The participants with title, affiliated institution, and nationality
The interviews were conducted face to face with semi-structured questions. An assistant typed
the answers consecutively, which allowed the researcher to concentrate on the dialog with
exploratory follow-up questions. The transcripts from each interview were e-mailed to the
appurtenant informant for additional comments and approval. All participants made some
minor corrections or additions, and gave their approval by e-mail. The questions are found in
appendix 1.
3.2 Data analysis
Miles and Huberman (1994) provide a framework for qualitative data analysis which includes
data reduction, data display and conclusions. Building on principles from Miles & Huberman,
the textual data from the interviews were analysed by data reduction and data display, as well
as examining the course material and tools provided by the SAP University Alliance. Then
these findings are discussed against the three propositions from the end of the literature
section, using Eckerson’s framework “BI Dynamics”. Thus, the analysis is conducted in three
steps, as shown in table 3:
Step Description Output (as found in Findings)
1 Data reduction and display Key findings of the interviews: table 3 (and appendix 2)
2 Identify themes by
clustering
6 themes identified: curriculum, student’s
challenge/solution, lecturer’s challenge/solution, how BI
differs from other subjects
3 Possible explanations using
“BI Dynamics”
Findings and conclusion: various support of the propositions
Table 3: Steps in data analysis with outputs
The next section presents the findings from the workshop, along with discussion.
4 Findings and Discussion
The data display shown in table 4 below has the following structure: The themes of the
questions are found in the horizontal columns, and the participants’ numbers correspond to
the ones from table 2 above. The participants have been teaching BI from 1 to 17 years; the
average amounts to 6,5 years. Note that participant 8 had not yet begun to teach BI, but had
taught related subjects including project management, enterprise resource planning and supply
chain management for 13 years. For a more comprehensive table, see appendix 2.
Curriculum Student’s
challenge
Student’s
solution
Lecturer’s
challenge
Lecturer’s
solution
BI versus other
subjects
1 Concepts,
tools
process,
modelling
Data
warehouse,
visualization
Seeing the
value
Get real life
case studies
SAP has
large data
sets
ERP, CRM.
They are easier
2 Tools How
everything
connects
Following
exercises
Not having
own material
Mock-
presentations
SAP ERP.
Harder to teach
SAP BEx
3 Strategy, tools BI technologies Seeing the
value
Students
lack
education
Learning the
product
(Only teach
SAP and BI)
4 Concepts,
process,
modelling
Lack
experience
Determination
to learn
Not having
own material
Make my
own material
Marks &
Spencer, their
system easier
than SAP
5 Metadata.
Practical
issues
Lack
knowledge of
the product
Motivation Keep up to
date with
new releases
Network in
SAP
SAP Finance.
BW more fun
6 Enterprise
Systems/
Architectures,
Web-based IS
Only want to
focus on SAP
Motivation
for SAP
Time to
learn
technology
Delegate
work to
students and
assistant
Microsoft. SAP
more brittle
7 The
integrations of
SAP systems
Learning curve
is slow at first
Struggle more
with process
than
technology
Time Network in
SAP
Anti-virus,
Geo-clustering,
Data
replication.
SAP more
difficult
8 Future BI
module: ERP,
BI theory &
practice
(SAP)
ERP: concept,
complexity,
abbreviations
ERP: By
motivation.
Setting theory
into action
Tool
changes
often
Develop own
material, link
tool to theory
Nothing of what
I teach is
similar to SAP
BI
9 Concepts,
tools,
modelling
Undergraduates
good at
technology,
postgraduates
good at
business
Motivation
and curiosity
Young
students lack
business
experience
Real life
example:
girlfriend
choice
IS management.
BI harder
10 Process, tools,
modelling
Data
modelling, BI
process
Motivation Time to
learn
technology
Work
overtime,
read books
Databases, IS
management.
They differ a lot
Table 4: Data display with key finding of the interviews
Each theme is discussed in accordance to previous literature.
Curriculum: When Watson taught BI and Data Warehousing in 2006, his learning objectives
included introduction to the disciplines, applications of data warehouse and BI tools,
modelling, and case studies. Fang and Tuladhar’s learning objectives were modeling, data
warehousing and BI products such as queries. Mrdailj’s course covered the same objectives,
but also used SAP Business Warehouse as hands-on application for BI, as well has having a
strong focus on the business process and value. In this study, the curriculum of the ten
participants covers all of these objectives; “concepts, BI process, modelling, tools, strategy,
integration, analysis”. The participants working for SAP have a slightly higher focus on the
technical components of the system, such as InfoCubes (explained above) and modelling, but
they also include a holistic understanding of how the system is integrated with business
processes and value. Only one participant commented that “BI is not the same as I thought it
would be when I started 5 years ago. Now the tech is disappearing and it is more focus on
business perspective, such as KPI, sales, service, retail, research.” Consequently, the
curriculum of BI is the same after half a decade, but has grown to include business value.
Challenges and solutions for students: This paper is not only about college students, but
also apprentices in companies. Are there any differences between college students and
company apprentices when it comes to challenges? The recent study of Wixom and
Ariyachandra (2011) provides a ranked list of challenges. Recall from table 1 that the
student’s top five major challenges were more real-world software and data sets, in-class BI
project, clearer link to jobs, and better speakers.
The results of the ten semi-structured interviews support only one of these challenges. Eight
of the participants list “learning the application” as one of the students main challenges. Thus,
the problem in this study is not having access to real-world software, but to master it. Other
features found in this study are difficulties in understanding BI concepts and abbreviations,
and the whole process of BI, which is defined by Turban et al. (2011) as turning data into
actionable information which again can provide business value.
There are several reasons for this divergence. First, the answers were given from how the
lecturer perceives the student struggle. If the student was asked personally, the answers could
be different. Moreover, six of the ten respondents in this study teach SAP trainees, and not
college students, and obviously these trainees do not need any in-class BI projects or a clear
link to jobs, as they find themselves in the very middle of it.
The course material provided by SAP is very thorough, with step-by-step explanations with
illustrative print screens. Each theme and exercise was carefully walked through by the SAP
staff. However, no matter how detailed the material, the participants at the workshop needed
follow-up, and made additional notes in the course book.
Challenges and solutions for lecturer: According to Wixom and Ariyachandra (2011) the
lecturer’s challenges were access to data sets, finding suitable cases and textbook, providing
realistic experiences, and technical support and training. Interestingly, all of these obstacles
are supported by the data from this study. Data sets and cases are difficult to provide, as
participant 1 explains: “It is difficult to get hold of real life case studies because people and
companies are secretive about their analysis and data”. Participant 2 and 4 clarify that even
though they have ready-made presentations and material at SAP, they prefer to create their
own: “You should use your own Powerpoints rather than material from others”.
Regarding the technical support and training, one might expect that this issue should be less
challenging for the participants of this study, since six of them are in fact SAP employees.
Actually, the majority of the lecturers still declare that teaching the technology is challenging.
However, as one might expect, the challenges are even larger for the lecturers employed at the
university than at SAP. Participant 6 draws a rather bleak picture: “Undergraduate students
have no awareness of SAP. Unless you get it right, they end up hating SAP. So we have to be
careful how we present it.”
The participants, even the ones working for SAP, report that finding large data sets are
challenging. The reason for this seems to be that customers of SAP regard their data as “gold”
and providing competitive advantage. The workshop in SAP Business Intelligence did
provide selected data sets from a fictive bike company. Although fictive, the sets were
containing metadata and gave a realistic view of real-life reporting. Apart from the pre-
prepared datasets, this case study does not reveal any other ways of addressing this challenge.
Possible difference BI and related subjects taught by the lecturer: Is teaching BI any
different than teaching for example ERP-systems, or IT project management? Participant 8
has no doubt: “Nothing of what I teach is similar to BI”. This participant teaches multiple
subjects: supply chain management, project management and enterprise resource planning,
which should provide a basis for comparison. Actually, nine of the ten participants quickly
labelled BI as different from other subjects they teach, with typically reads enterprise resource
planning, customer relationship management, finance, and data bases. The last participant,
number 3, has exclusively taught BI and SAP, so this participant cannot contribute in this
category.
Having established that BI is distinctively different from related subject, does this mean that it
is more difficult? Recall participant 6 warning that the lecturer only gets one shot at “getting
it right”, at least when it comes to learning SAP, and according to two participants 6 and 9,
learning SAP is what the students want to do, more than learning about processes and
modelling: “When we introduce them to research approaches, they only want to focus on the
SAP part”, explains participant 6.
Despite such challenges, four participants finds teaching BI and SAP Business Warehouse
easier than for example finance. Several participants declare the reason is that the students are
motivated, and participant 5 adds that BI is fun and interesting to teach. Other ways of coping
with the challenges are suggested by participant 9: “It helps to have four hour sessions
instead of two”. The students need to “warm up” with the tools/thinking/reflecting about the
problems” and participant 4 shares: “I enjoy training. Nice to have training with interaction –
meaning that it is nice to have a non-traditional classroom, as it makes people interact
more.”
By using a semi-structure interview, a fascination finding emerged from this study:
motivation and fun. Motivation is highly present, coming from both the student and lecturer.
From the students’ point of view, it seems that they accept some pain for gain. Participant 9
argues that “the students want to obtain a diploma with SAP on it”. The lecturer is equally
motivated, quoting participant 4: “Students never get BI straightaway. However, I go through
3-4 times to make sure everybody gets it, using different approaches. I never give up!”
Implications for the BI lecturer
All of the participants inform that teaching BI has certain challenges, and that the tools are
difficult. Poor usability is critical for ending up in the negative cycle (Eckerson, 2008). Given
the rather bleak statements from the participants “lecturer must get it right, or the students
will hate SAP”, this should make a BI course vulnerable of getting caught in a negative cycle
in the “BI Dynamics” model. Figure 2 hardly seems intuitive, and along with the SAP
concepts such as “InfoCube” one might expect that this would result in poor usability.
Conversely, none of the participants report of such a negative cycle. The lecturers do not even
appear to have a “plan B” if SAP Business Warehouse should fail. Eckerson declares that if
an organisation is caught in a negative cycle, it will stay there until the BI project is cancelled
or pulled out by the BI team somehow – the latter often by delivering a “quick-hit” product
with is easy to use. A “plan B” for a lecturer could be making a dashboard or using Excel for
demonstrating reporting and analysis. However, all respondents were determined to follow
their initial plan with modelling, making multidimensional cubes, and creating products such
as reports in SAP.
So why are there no sign of any negative loops in this case? Eckerson (2008) writes that he
has never seen a successful Bl projects without a top-down management, meaning strong
sponsorship. In this paper, one argues that this transfers to a dedicated lecturer. The lecturer
needs to possess the technical skills for the BI tool as well as enthusiasm for the discipline.
This may seem obvious for any subject at any school. However, according to Eckerson, once
you have passed this “magical tipping point”, “it will seem like there is nothing you can’t
accomplish” (p. 11). Attending workshops such as the ones SAP gives for Business
Intelligence should therefore be a safe investment of time and effort a BI lecturer. However,
there is one element which emerged from the interviews, and which is lacking from
Eckerson’s model: fun. What makes BI fun? BI is a hot topic in these days, constantly
evolving with visualisation, “big data”, predictive analysis, social media analysis, and more.
Summing up, each proposition is repeated with various degrees of support:
Proposition 1: The BI process is abstract for the students: what is BI, and what are the
business values? Indeed, this is a perceived challenge from the lecturer’s point of view. The
undergraduate students are eager to start learning the tools more than they are of modelling
and reflecting on the value of the technology, and the postgraduates are more focused on the
business value and more reluctant to learn SAP. This implies that this proposition is half-way
supported: The BI process is abstract for the undergraduates, but less for the postgraduates.
Proposition 2: It is difficult for the students to learn and master a BI application such as
SAP Business Warehouse. Even for the SAP staff, SAP Business Warehouse with reporting
tools are perceived as complex and “huge”, and the students rarely have prior experience with
any SAP technology, as they may have with for example Microsoft. One participant claims
that the lecturer only gets one shot at presenting SAP, meaning that it has to be taught
correctly or the students will “end up hating SAP”. Despite this high threshold, the majority
of the students are eager to learn the application, as they believe that knowledge of SAP
technology is valued and on demand in working life.
Proposition 3: BI differs from other subjects and is more challenging to teach.
Surprisingly, the two parts of this proposition do not correlate. The first part of this
proposition, “Bi differs from other subjects”, may be the most unambiguous finding. Nine out
of the ten participants classifies BI as distinctly different from related subjects such as
relational databases, knowledge management, enterprise resource planning, and finance. The
reason for this difference is explained as follows: Relational databases require mostly
technology skills, while finance demands mainly business understanding. BI is broader, like
an umbrella: In addition to handling a complex tool with multiple functions and visualisation,
BI is also focused around business processes which require analytical and structured skills
from the student. This study also finds that undergraduates are more interested in learning the
technology than the business value, and vice versa for the post graduates.
However, regarding the last part of the proposition, only four of the ten informants find BI
more difficult to teach. The reason is that the students see the value and they are very
motivated for learning BI, despite the high threshold of mastering SAP Business Warehouse.
Perhaps this is what makes all of the lectures equally motivated for teaching BI. Even though
teaching BI is perceived as more difficult than teaching other subjects, the lecturers never give
up.
Limitations and suggested further research
There are limitations to this paper. First, one has to acknowledge that ten participants make a
small study. Second, the findings were based on participants at a SAP University Alliance
workshop, and this case will influence the answers given. Further research can reveal whether
applications from other BI vendors are perceived the same way. Third, the UK school system
and students may be different from other countries; for example, this paper does not address
whether the UK schools are more or less authoritarian then other nations. For example, one
participant commented that “British students need to be kept engaged. They have a low
boredom threshold”. Finally, as the majority of the participants stated that BI differs from
related subjects, additional research can explore these differences in more depth.
5 Conclusion
This paper has investigated: What are the perceived teaching challenges in BI and what are
the BI lecturers’ solutions? Drawing on the three propositions as background for the semi-
structured interviews, the following conclusions are offered: Proposition 1: teaching the BI
process and potential business value is challenging, but the undergraduates will struggle more
than the postgraduates to learn. Proposition 2: The opposite is true for teaching the technology
such as SAP Business Warehouse, where the lectures report that the master students struggle
more than the bachelor students. Proposition 3, however, is divided: BI does differs from
other subjects, but it is not harder to teach. The lecturers’ solutions to the above are to provide
real-life examples and making sure they master the tools themselves before teaching the
students.
Hopefully, this paper can motivate teachers of BI and provide ideas on how to accomplish
even more successful and interesting BI courses, thus remaining in the positive reinforcing
cycle from the “BI Dynamics”.
Acknowledgment
The author would like to thank all ten participants for providing comprehensive data for this
paper, and Knut Urbye for transcripts and comments. Additional gratitude goes to SAP
University Alliances in the UK.
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Appendix: semi-structured interview
1. What is your title?
2. Where do you teach (name of University and nationality)?
3. How long have you taught BI?
4. What are the 3 major parts of your curriculum? (Some examples: Conceptions and
abbreviations/BI process (turning data into actionable information)/Effects of BI
(faster/more accurate reporting, saving money, more satisfied customers)/Data
modelling/Use of tools)
5. What are the students’ major challenges related to BI?
6. How do the students cope with the challenges related to BI? (Some examples: By
motivation/ By being analytical/By drawing on skills and prerequisites)
7. What are your major challenges as a BI lecturer? Examples?
8. How do you solve the challenges as a BI lecturer? Examples?
9. Do you teach other subjects?
10. If yes: which ones, and do they differ from BI as a subject when it comes to
challenges?
11. Age group of students?
12. Any comments?
Appendix: data display of key findings of the interviews
Q = Question
P = Participant
Q: P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P 10
How
long
taught
BI?
3 years 3 years 17 years About 1
year
About 2,5
years
15 years 11 years N/A Over 10
years
About 5
years
What are
major
parts of
curriculu
m?
Concepts, BI
process, data
modelling,
tools
Tools:
Business
Warehouse,
BEx,
Broadcastin
g
Strategy to
meet
business
requiremen
ts. The
right tool in
the right
way
Terminolog
y. BI
process.
Data
modelling.
Metadata.
Practical
issues that
help
colleagues
do their
jobs
Enterprise
Systems/Ar
chitectures,
Web-based
IS
The
unique
integratio
ns of SAP
systems
Future BI
module:
ERP, BI
theory &
practice
(SAP)
Concepts
and
abbreviati
ons,
modelling,
tools
The BI
process,
modelling,
ETL,
OLAP,
Dashboar
ds
What are
students’
major
challenge
s?
Data
warehousing,
visualization
How
everything
connects
together
BI differs
from other
technologie
s
Students
never get
BI
straightawa
y. Lack
experience
Lack of
knowledge
on the
product.
Never seen
lack of
motivation
They only
want to
focus on the
SAP part
Learning
curve is
slow at
first
ERP: The
system’s
complexit
y,
concept,
and
abbreviati
ons
Undergrad
uates good
at
technolog
y ,
postgradu
ates good
at
business
Data
modelling,
BI process
How do
the
students
cope?
See the value.
I teach
concepts
early. They
draw on skills
Reviewing
material,
following
exercises
See the
value
Determined
to learn
By
motivation
Students
are
motivated
for SAP
Struggle
less with
technolog
y, but
more on
process
ERP: By
motivatio
n. Setting
theory
into action
By
motivatio
n and
profession
al
curiosity
By
motivatio
n. They
like hands
on
experienc
e.
What are
your
major
challenge
s as a
lecturer?
What BI is all
about. Get
hold of real
life case
studies
Making the
material
my own
Customer’s
challenge
tends to be
lack of
education
Improve
communica
tion. Not
having own
Powerpoint
s
Keep up to
date with
new
releases
Time to
learn
technology
Time and
structure
of
informatio
n
ERP more
strenuous
to teach. It
changes
often.
Young
students
lack
business
experience
, but easy
to
motivate
Time to
learn new
technolog
y
How do
you solve
the
challenge
s?
SAP has large
data sets
Review
materials.
Mock-
presentatio
ns
Proper
education
of the
product and
adoption of
best
practice
Ramp up
people over
a year.
Make my
own
material
Trough
internal
network. I
cope with
technical
issues
myself
Delegate
work to
students
and
assistant
Network
in SAP,
but
difficult to
find the
right
person
Develop
own
teaching
material,
and link it
to theory
Real life
example:
girlfriend
choice,
business
case
studies
Work
overtime,
read
books
Do you
teach
other
subjects?
– if yes,
do they
differ
from BI?
ERP, CRM,
ByD – all are
easier/more
“old school”,
less
visualization
ERP, SAP
Enterprise
Portal.
Harder to
teach SAP
BEx
N/A
(Only SAP
and BI)
Classroom
training at
Marks &
Spencer,
their
system
easier than
SAP
SAP
Finance=
boring, BW
= more fun
Students
are used to
Microsoft,
not SAP.
SAP more
brittle
Anti-
virus,
Geo-
clustering,
Data
replication
. SAP
more
difficult
Nothing
of what I
teach is
similar to
SAP BI.
IS
manageme
nt.
BI harder:
need
prerequisit
es in
understan
ding
Relational
database,
Software
managem
ent. They
differ a lot
Age of
students?
19-20 and 25-
30
25-35 30-50 20-30 25 to 35 18-21, 20-
30
25-28,
early 40
21-23, 25-
30
18-20, 25-
30, 40-60
20+,
Some in
50’s.
Any
comment
s?
British
students need
to be kept
engaged
Exercises
make the
student
learn twice
as much
No I enjoy
training, in
a non-
traditional
classroom
No No 1500+
people
work in
SAP UK.
I support
products
Students
more
interested
in SAP
than
theory
Students
want to
obtain a
SAP
diploma
The tech
is
disappeari
ng in BI,
focus on
business
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