Description
Business Intelligence The impact on decision support and decision making processes
J ÖNKÖP I NG I NT E RNAT I ONAL BUS I NE S S SCHOOL
JÖNKÖPING UNIVERSITY
Busi ness I ntel l i gence
The impact on decision support and decision making processes
Bachelor’s thesis in Informatics
Author: Daniel Andersson
Hannes Fries
Per Johansson
Tutor: Jörgen Lindh
Jönköping January 2008
Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics
Title: Title: Title: Title: B BB Business Intelligence usiness Intelligence usiness Intelligence usiness Intelligence – –– – Impact on decision support and decision making Impact on decision support and decision making Impact on decision support and decision making Impact on decision support and decision making
Author: Author: Author: Author: Daniel Andersson Daniel Andersson Daniel Andersson Daniel Andersson
Hannes Fries Hannes Fries Hannes Fries Hannes Fries
Per Johansson Per Johansson Per Johansson Per Johansson
Tutor: Tutor: Tutor: Tutor: Jörgen Lindh Jörgen Lindh Jörgen Lindh Jörgen Lindh
Date Date Date Date: 2008 2008 2008 2008- -- -01 01 01 01- -- -17 17 17 17
Subject terms: Subject terms: Subject terms: Subject terms: Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, In nn ntuition tuition tuition tuition
Abstract
Introduction
Historically, decision support systems have been used in organizations to facilitate better
decisions. Business Intelligence has become important in recent years because the business
environment is more complex and changes faster than ever before. Organizations have
started to realize the value of existing information in operational, managerial, and strategic
decision making. By using analytical methods and data warehousing, decision support can
now be used in a flexible way and assist decision makers in decision making processes.
Problem
Increasing investments in Business Intelligence indicate that it can bring value to organiza-
tions. Benefits such as the ability to access relevant and timely decision support when it is
needed can be of tremendous value when the use of existing information has become more
a question of survival or bankruptcy for an organization, than profit or loss. Thus, it would
be interesting to see how decision support and decision making have changed in organiza-
tions after implementing a Business Intelligence system.
Purpose
The purpose of this thesis is to investigate if and how Business Intelligence has changed
decision support and decision making processes.
Method
A deductive approach using a qualitative method has been used with semi-structured elite
interviews. The thesis aims to investigate the manufacturing industry located in the
Jönköping region in Sweden. The interviewed organizations are Husqvarna AB,
Fläkt Woods AB, Myresjöhus AB, and Kinnarps AB.
Conclusions
Our analysis shows positive effects of Business Intelligence in organizations with im-
provements of decision support due to timeliness, accessibility, quality, and better control
of organizational information. As improvements in decision support has occurred, decision
making has become better. Complicated problems are now easier to interpret by decision
makers. Our research also concludes that intuition still has a major impact in decision mak-
ing processes.
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Table of Contents
1 Introduction............................................................................... 1
1.1 Background ............................................................................................1
1.2 Problem Discussion................................................................................2
1.3 Purpose..................................................................................................2
1.4 Problem Delimitation ..............................................................................2
1.5 Interested Parties ...................................................................................2
1.6 Positioning..............................................................................................3
1.7 Definitions...............................................................................................3
2 Method....................................................................................... 4
2.1 Categorization of Knowledge..................................................................4
2.2 Method Approach...................................................................................5
2.2.1 Qualitative and Quantitative Research ...................................................5
2.3 Data Collection.......................................................................................6
2.3.1 Literature ................................................................................................6
2.3.2 Personal Interviews ................................................................................7
2.3.2.1 Outlining of Interview Questions.....................................................................................8
2.3.3 Interpretation of the Empirical Findings ..................................................8
2.4 Selection of Respondents.......................................................................9
2.5 Method Validation...................................................................................9
3 Frame of Reference ................................................................ 11
3.1 Business Intelligence............................................................................12
3.1.1 Data Warehousing Environment...........................................................13
3.1.2 Analytical Environment .........................................................................14
3.1.2.1 Decision Support...........................................................................................................14
3.2 Decision Making Processes .................................................................15
3.2.1 Managerial Activity ...............................................................................16
3.2.2 Management Information Systems.......................................................16
3.2.3 Intuition.................................................................................................18
4 Empirical Findings.................................................................. 20
4.1 Husqvarna AB ......................................................................................20
4.2 Fläkt Woods AB....................................................................................21
4.3 Myresjöhus AB .....................................................................................22
4.4 Kinnarps AB .........................................................................................23
4.5 Summary of Empirical Findings............................................................25
5 Analysis................................................................................... 26
5.1 Decision Support ..................................................................................26
5.2 Decision Making Processes .................................................................28
6 Conclusions ............................................................................ 30
7 Final Discussion ..................................................................... 31
7.1 Reflections............................................................................................31
7.2 Suggestions for Further Studies...........................................................31
7.3 Acknowledgements ..............................................................................32
References ................................................................................... 33
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Figures
Figure 2.1 - The inductive versus the deductive approach..................................5
Figure 3.1 - The connection between theories in the frame of reference ..........11
Figure 3.2 - The BI environment (Eckerson, 2003) ...........................................13
Figure 3.3 – The decision making model (Simon, 1960) ...................................16
Figure 3.4 - A Framework for Management Information Systems (Gorry & Scott
Morton, 1989) .......................................................................................18
Figure 5.1 - Analysis model...............................................................................26
Appendices
Appendix 1 – Interview Questions, English.......................................................35
Appendix 2 – Interview Questions, Swedish .....................................................36
1
1 Introduction
In this chapter we introduce the reader to the phenomena of decisions and parts of the historical evolution of
computerized support for decision making. We describe and problematize the phenomena of Business Intel-
ligence and thereafter state our research questions and present the purpose of this study. We also delimit the
problem, determine major stakeholders, briefly describe some other studies concerning Business Intelligence
and some fundamental concepts.
The field of Business Intelligence (BI) has become a popular area in recent years as a new
approach to gather and analyze data for business use. We believe there is a lack of research
conducted within this field. Since we have an interest in the area of BI and the fact that the
phenomena is fairly new, we believe we have a good opportunity to contribute with new
knowledge within this topic. Werner (2007) argues that BI is one of the most popular areas
for IT management to invest in, which makes BI an even more interesting topic.
1.1 Background
Humans have always been forced to make various decisions in different situations. Deci-
sions can be very simple, without major impact and consequences, or be of a very complex
nature, with a huge impact on millions of people. In organizations, managers have always
been making decisions concerning operational, managerial, and strategic matters and these
decisions can have an impact on the organizations’ stakeholders, ranging from the employ-
ees to the government. Managers often try to predict and understand the outcome of dif-
ferent decisions they make. This could be extremely difficult because speculating in a future
context is always a daunting task. However, a successful prediction can be very helpful in
order to select the right decision to achieve the desired outcome or best alternative.
Already in the 1950s, organizations realized the potential of computers and they began to
play an important role. Between the mid 1950s and the early 1970s, the use of computers
and information systems (IS) in organizations grew tremendously but few of these systems
had an impact on mangers’ decision making. In the 1970s, new systems were developed to
support managers in accessing relevant business information needed for decision making
(Gorry & Scott Morton, 1989). These systems provided business managers with static, two
dimensional reports without any analytical capabilities (Turban, Aronson, Liang & Sharda,
2007b). In the 1980s, the systems evolved and started moving away from static reports. In-
stead, focus was shifted to monitoring the organizations’ progress and performance to-
wards critical goals (Burkan, 1991). Organizations did not only need information systems to
support their ongoing operations, they needed systems that could assist managers with val-
ue added information in their decision making processes (Fernandez, Labib, Walmsley &
Petty, 2003). McNurlin and Sprague (2004) describe decision support systems (DSS) as a
management information system (MIS) in conjunction with analytics. To be able to cope
with the huge information flow, DSS have moved towards an attempt to create an intelli-
gent DSS. This attempt provides a great promise to improve both individual performance
and organizational performance and is designed to support, not replace human decision
makers (Dalal & Yadav, 1992). The Gartner Group introduced the term BI in the mid-
1990s but the concept has evolved from MIS, used to support reporting in the 1970s (Tur-
ban et al., 2007b). Today, the term BI is widely used when discussing support in organiza-
tional decision making, instead of traditional terms like MIS and DSS.
2
1.2 Problem Discussion
Today, the organizations’ business environment is becoming more complex and changes
faster than ever before. This generates pressures on the organizations and forces them to
respond quickly. To be able to respond to these pressures in an appropriate manner, or-
ganizations have to make quick, operational, managerial, and strategic decisions, which can
be of a very complex nature and may require timely and relevant information, data, and
knowledge (Turban, Sharda, Aronson & King, 2007a). To be able to improve processes
and create additional business value, organizations have started to realize the value of exist-
ing information. As a result of the increasing demand, the number of BI vendors and tools
has increased substantially in the past years. Using analytical tools and data warehousing, BI
is extracting and analyzing relevant information and making it accessible to the right mem-
ber as a support in decision making processes. In this process, data is gathered from differ-
ent systems, which leads to large amounts of organizational data. To support decision mak-
ers in their decision making process to make more informed decisions this data needs to be
analyzed, distributed, and accessed by the right person, at the right time (Turban et al.,
2007a). The number of implementations and organizations using BI has increased during
the last years (Miller & Reinke, 2007). This might imply they have become more aware of
the usefulness of business information. Turban et al. (2007a) argues that providing updated
and accurate business information, which we address as decision support throughout this
study, is more a question of survival or bankruptcy for an organization than profit or loss.
BI’s major benefit is the ability to access relevant decision support when it is needed.
Based on this, it would be interesting to see how decision support and decision making
have changed in organizations after implementing a BI system.
• How has decision support changed after a BI implementation?
• How has BI changed the way organizations make decisions?
1.3 Purpose
We will investigate if and how BI has changed decision support and decision making proc-
esses.
1.4 Problem Delimitation
To be able to generalize our study in one business sector, we will focus on one industry;
manufacturing. According to Werner (2007) manufacturing organizations are currently one of
the most frequent investors in BI. The region of Jönköping has a relatively high number of
manufacturing organizations (SCB, 2005), therefore this region is appropriate for this
study. This study will only focus on two aspects, decision support and how it is considered
to have changed after a BI implementation. The second aspect is how decision making
processes have changed after implementing BI. We do not intend to include anything re-
garding technical or human-computer interaction (HCI) aspects of BI.
1.5 Interested Parties
Organizations with an interest in implementing a BI system could gain understanding of
how decision support and decision making have changed after BI implementations. Or-
ganizations that create and market BI systems may have an interest in seeing what the user
experiences are and how the systems have affected decision support and decision making.
3
In the academic world, researchers and students with an interest in BI might find this thesis
interesting when searching for new areas of research. Organizational managers could see
how decision support has changed and therefore develop an understanding of BI and how
it could affect their organization in the daily work and long-term planning.
1.6 Positioning
BI is currently a discussed topic in the Swedish business world (Werner, 2007). Exido
(2007), a Swedish research analyst company, predicts a 14 percent increase in BI invest-
ments in Sweden for 2007. The global BI market is increasing at a similar rate. Gartner
(2007) predicts the BI market in Europe, Middle East, and Africa (EMEA) to increase with
10 percent in 2007. Despite its popularity, Swedish research in this field is lacking. Our
study will therefore be based mostly on American literature. We do not believe there are
any major differences between BI systems in the US and Sweden and we therefore consider
our choice of literature appropriate. However, we have found a few studies conducted in
Sweden. Karlberg and Karlsson (2006) discuss BI from two perspectives, the concept and
the phenomenon. Similar, Biberic and Hodell (2007) evaluate BI from a rational decision
making model to understand how companies use BI. Olsson (2007) discusses the history
behind BI and describes current and future BI trends from a business perspective. Dagman
and Wigsten (2007) identify causes that constrain organizations to efficiently utilize a BI
process and they discuss how to create higher efficiency in a BI process.
1.7 Definitions
Business Intelligence – Using data warehousing and analytical tools, BI is extracting and
analyzing relevant information and making it accessible to the right member as a support in
decision making processes.
Data Warehouse – Central storage of data to support decision making in organizations.
Decision Support – BI generated information supporting decision makers in decision
making processes.
Decision Support Systems – Systems used to directly support specific decision making
processes.
ETL – Extract, Transform and Load is the process of extracting data from different
sources, converting it into an appropriate format and loading the data into a data ware-
house.
Key Performance Indicator – A financial or non-financial measure of how well an or-
ganization is performing.
Management Information Systems – Information systems used to analyze and solve
business problems.
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2 Method
The data necessary to perform this study will be gathered through qualitative methods. The empirical find-
ings will be collected from primary sources through in-depth interviews with selected organizations. Data
from secondary sources will be collected from literature. The choice of methodology for this study will be pre-
sented and argued for below.
Goldkuhl (1998) argues a researcher who wants to develop knowledge must plan and de-
sign the process of acquiring knowledge. This process can be divided into two phases. The
first phase is planning and defining and the second phase is realization of the planned activities.
Development of knowledge starts when a researcher is interested in a phenomenon and
wants to know more about this specific area. Based on this interest, preliminary research
questions are formulated. The researcher thereafter tries to articulate pre-knowledge about
the phenomena. This pre-knowledge in conjunction with other knowledge in the area helps
the researcher to reformulate and determine the research questions. Thereafter, the re-
searcher determines the demanded knowledge - what knowledge to develop. The de-
manded knowledge is the determinant of what strategy and what method to use to conduct
the study (Goldkuhl, 1998). The major objectives in this research are to understand how
decision support and decision making processes have changed after implementing a BI so-
lution in an organization. Therefore, the research group needs to understand current litera-
ture about human and computer in conjunction in decision making processes. Our research
questions are based on discussions in order to fulfill the purpose and give appropriate guid-
ance of the thesis. The research questions have been affected, determined, and adjusted
based on literature the group has studied.
2.1 Categorization of Knowledge
Determining what type of knowledge to develop is referred to as categorization of knowledge.
Goldkuhl (1998) argues a researcher needs to determine what kind of knowledge to de-
velop. This is a process to establish the value of the developed knowledge and to determine
the appropriate strategy for the study. Goldkuhl (1998) presents different types of knowl-
edge which are appropriate in different research situations. Deciding on a strategy is neces-
sary to determine what type of knowledge to create and the chosen strategy should follow
the categorization of knowledge (Goldkuhl, 1998). In this study explanatory knowledge will
be created using an explorative strategy.
• Explanatory knowledge is an approach where the researcher explains why a phenome-
non is in a certain manner and mention causes, foundations, reasons or prerequisites
for the resulting relations. Often, explanatory knowledge means to test hypothesis
but there are other cases as well. One approach within explanatory knowledge is to
study effects which originate from a certain event (Goldkuhl, 1998).
• Explorative strategy is a research strategy where the researcher does not test hypothesis
but is an attempt to get better knowledge within a specific area. This approach is like-
ly to generate hypothesis (Goldkuhl, 1998).
To conduct this study in a suitable manner, the most appropriate type of knowledge is ex-
planatory knowledge. One of the approaches within this type of knowledge is to study effects
which originate from a certain event (Goldkuhl, 1998), in this case a BI implementation.
The explorative strategy aims at generating knowledge within a specific area and since existing
literature does not cover the intended research objectives we have to use the explorative
strategy.
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2.2 Method Approach
Holme and Solvang (1997) argue that explaining a phenomenon or a situation using theo-
ries is not always easy. Even though it is a complex task, it is necessary in order to create
understanding and develop new theories. There are two approaches researchers use; induc-
tive and deductive (Holme & Solvang, 1997). The two approaches are illustrated in figure 2.1.
Using an inductive approach, the researcher collects empirical material. The empirical data
is analyzed and generalized and new theories are generated from the generalizations. The
deductive approach is more formalized. Unlike the inductive approach, it starts in theory
where the researcher derives a testable hypothesis or a theoretical proposition. Through
analysis of the collected empirical data, the hypothesis is accepted or rejected (Bailey, 1996).
Figure 2.1 - The inductive versus the deductive approach
The thesis work will start with a comprehensive literature study, to familiarize ourselves
with the concept of BI, systems for decision support, and areas related to decision making
processes. From the findings in the literature study, we will develop our research questions
and define the purpose of this thesis. The next step in our research process will be to gath-
er empirical material through interviews with manufacturing organizations using questions
related to existing theories. An analysis of the findings using these theories will follow and
the results aim to provide answers to our research questions. Based on this description of
planned activities, we argue that we follow the deductive approach.
2.2.1 Qualitative and Quantitative Research
The reality of today’s society is of complex nature and it is impossible to seize the reality of
this environment using only one method. There are two different categories of methods,
qualitative and quantitative methods. Both methods have their strengths and weaknesses and
the selection of which method to use should have its starting point in the purpose. The
primary focus of qualitative studies is explanatory and implies less standardization. Creating
a deep understanding of the study objects is one important feature of this method. The
other important aspect is to describe the whole of the context. One characteristic of this
method is to be close to the studied objects. Quantitative methods are characterized by a
higher degree of standardization and they imply higher control for the researcher but also
pre-determine possible answers. The planning and selection in quantitative methods is cha-
racterized by distance to the research objects (Holme & Solvang, 1997).
The qualitative approach generates an overall picture of social processes and contexts. Be-
cause of time restrictions and to be able to get an overview of the information, a limited
6
number of research objects are often selected. The qualitative approach also generates an
understanding of the total situation. The strengths of quantitative methods are their ability
to result in generalizable outcomes. The outcomes can lead to opportunities for the re-
searcher to make statistical statements. There are some characteristics associated with these
methods as well. Qualitative methods are flexible and the questions can be revised during
the meeting with an object and can be changed during the study. This is both a strength
and a weakness, since it can be difficult to compare the outcomes of different interviews.
Quantitative methods on the other hand are fixed and cannot be changed during the study,
it must be standardized. This gives the researcher control and the ability to generalize
(Holme & Solvang, 1997).
To gain deeper understanding in how decision support and decision making have changed
after an implementation of BI, a close interaction with the study objects is needed. This in-
teraction will take place during personal interviews with a low degree of standardization.
The objective is to get an overall picture of BI usage in manufacturing organizations, both
how it is utilized and what that impacts are on decision making. Based on this, the qualita-
tive approach was found to be the most appropriate for this study since we will focus on
qualitative changes which have occurred after a BI implementation.
2.3 Data Collection
There are two different types of data, primary and secondary data. Primary data is informa-
tion gather by the researcher using a certain method. Holme and Solvang (1997) argue that
primary data is gathered when the researcher is close to the study objects and the inter-
viewed object has experienced the situation itself. Secondary data is information gathered
by other researchers in earlier studies (Lundahl & Skärvad, 1999; Holme & Solvang, 1997).
To gather information using qualitative methods four approaches are typically used. First,
the researcher can participate in a setting. Secondly, a researcher can conduct a direct ob-
servation. The third and fourth approaches to gather information are personal interviews
and analyzing documents and material culture (Marshall & Rossman, 1999). In this study
we are going to use literature studies and conduct personal interviews.
2.3.1 Literature
When a researcher describes the context and history of a phenomenon, an important part
of research work consists of reviewing documents. This approach can be seen as supple-
mentary to other qualitative approaches to gather information. Research journals concern-
ing the topic are one example if they are relevant for the study. One obvious drawback us-
ing literature in a study is the need for interpretation by the researcher (Marshall & Ross-
man, 1999). The sources used in this study have all been processed in a way suggested by
Holme and Solvang (1997). They describe four steps to analyze a source; observation,
source, interpretation and usefulness. In the first step, observation, the researcher tries to
find sources which enlightens the questions determined in the problem discussion and also
tries to get an overview of relevant existing literature. In the second phase, source, is im-
portant to determine who the author is to establish the source’s trustworthiness. The third
step, interpretation, deals with the problems of interpreting what the source describes and
try to analyze the information. In the last step, usefulness, the researcher determines how
useful the source is to fulfill the purpose (Holme & Solvang, 1997).
Since we will use a deductive approach in this study and have the starting point in literature
it is of great importance to asses the sources used. The observation step was conducted
7
early in the study and the reliability of sources used are high since most of the literature
used within the report are written by well-known writers or published in well-known re-
search journals. The third and fourth steps are more problematic since we need to interpret
what the author meant and determine how useful the information is. Hence, we believe to
have understood the context and meaning of the literature and that it is applicable to the
study. To fulfill our purpose we intend to use literature regarding BI, decision support, and
decision making.
2.3.2 Personal Interviews
Interviews have, like most methods, strengths and weaknesses. Interviews generate large
amounts of data quickly and the ability to follow up the result immediately. An interviewer
needs participation and cooperation from the interview object, and a problem may be the
interview object’s unwillingness of answering all questions. The interviewer can also have
problems when it comes to understanding a certain behavior because of differences in cul-
tures, languages or the interviewer’s lack of skill. The interview object can also have reasons
for not being truthful (Marshall & Rossman, 1999).
One common approach to distinguish between interviews is the degree of standardization.
If the questions have a high degree of standardization the questions and the order of the
questions is the same for all interviews in the study. If the interview is unstandardized, the
outlining of the questions is different from interview to interview but as long as the need
for information is covered, this type of interview can be a good alternative. Many inter-
views are not either of these types, which can be seen as extremes, but somewhere in be-
tween these interviews types. Lundahl and Skärvad (1999) argue these interviews can be
addressed as semi-standardized interviews. In semi-standardized interviews, the interviewer
asks certain questions to all respondents and then asks attendant questions to get a deeper
understanding. Unstandardized interviews are appropriate when the researcher conduct qu-
alitative studies where the researcher wants to develop an understanding of a respondent’s
situation or its motives and ideas about a certain phenomena. Standardized interviews are
appropriate when the researcher needs more quantitative data (Lundahl & Skärvad, 1999).
We will use semi-standardized interviews since we want to cover certain question areas.
However, we do not want to be tied to asking questions in a certain order and we want to
be able to ask attendant questions to get a deeper understanding of BI usage.
In this report we will use what Marshall and Rossman (1999) addresses as elite interviewing.
This type of interviewing aims at interviewing individuals with a certain amount of influ-
ence in an organization. The interview objects are selected based upon their knowledge or
expertise in a certain area. Elite interviewing gives the researcher opportunity to gain valu-
able information because of the interview objects’ position in the organization. They can
also give an overview of internal and external relations as well as legal and financial aspects.
There are problems with this type of approach. Elites can be hard to interview since they
have limited time and the interviewer may be forced to adapt the interview based on the el-
ites’ wishes. To conduct a successful elite interview puts pressure on the interviewer to de-
velop competence in related topics. However, if the interviewer is well-prepared, this type
of interview generates quality information (Marshall & Rossman, 1999).
Using a tape recorder is desired to minimize the amount of notes and direct full attention
to the respondent (Ritchie & Lewis, 2003). We intend to use a tape recorder during the in-
terview since we do not want to miss important information. Bailey (1996) argues the re-
searcher needs to explicitly clarify the use of tape recorder for the interview object and ask
for its permission. One of the reasons to conduct a face-to-face interview is the possibility
8
to observe the body language of the respondent. Bailey (1996) claims this is very important.
To be able to cope with the difficulties with elite interviewing we have studied a large
amount of literature related to BI to be prepared for the interview situation. Time is an im-
portant issue and we intend to contact our interview objects early in the research process.
2.3.2.1 Outlining of Interview Questions
Lundahl and Skärvad (1999) argue that personal interviews build on confidence between
the respondent and the researcher. Further, interviews should start with questions of less
controversial nature. Holme and Solvang (1997) also presents this view on interviews but
they also argue that the interview should end with unproblematic questions because this
gives the respondent and the researcher time to neutralize tensions which may have oc-
curred during the more controversial part of the interview (Holme & Solvang, 1997).
The outlining of the questions has been influenced by the authors mentioned above. The
starting questions concern the respondent’s background within the organization and what
position they have as well as questions regarding the organization. These questions are gen-
eral and easy to answer. This will be done to build a confidence between respondent and
interviewer and the fact that position can influence the answers. After these opening ques-
tions, the interviewer will move on to BI and the questions asked will be based on the
theoretical framework. The final questions will be of less controversial nature and we have
decided to let the respondent speculate in the future of BI. When the interviews take place,
the interviewers will always ask the questions like how and why. This approach was chosen
to get a deeper understanding of each organization’s specific view of their own BI system
and to get a clearer picture of the respondents’ thoughts about their BI system. We also be-
lieve when how and why questions are asked, respondents will give more comprehensive and
detailed answers and richer descriptions of each question.
Before the interviews are conducted, we will send the questions to the respondents. This
will be done so that the they can familiarize themselves with our research and prepare an-
swers to our questions. There are drawbacks using this approach, such as the fact that re-
spondents may prepare the answers they think we want to receive and we might not get
spontaneous reactions to our questions (Thomas, 2004). We believe the answers will be
more valid and more complete if the respondents get the opportunity to prepare.
2.3.3 Interpretation of the Empirical Findings
According to Fischer (2007), researchers often discover two contradicting problems when
analyzing research material. The first problem is the law of the missing middle. Researchers
who start writing their results right after collecting empirical material often miss out the in-
termediate stages of sorting and sifting and miss the line of argument. The second problem
is the dilemma of drafting. Researchers never really understand their empirical material until
they write it up. It is the process of writing that forces researchers to really understand the
material. This implies an iterative process of sorting and sifting followed by writing (Fisch-
er, 2007; Starrin & Svensson, 1994). These interpretations and understandings are based on
knowledge and experience but also imagination and creativity. According to Starrin and
Svensson (1994) this is legitimate when a researcher tries to interpret interview material.
To be able to cope with the huge amount of data we will gather during the interviews we
have decided to only present and analyze the material which directly relates to the research
questions and the theories presented below. After each interview we will listen to the re-
corded material once and then listen a second time to get a deeper understanding of the
9
context and the respondents’ answers. During the second listening notes will be taken. The
recording will be stopped and rewind several times to assure nothing important is left out.
This to assure that the presented empirical material is correct and presented in the right
context. To be able to compile the empirical findings in a sufficient text, where no impor-
tant information is left out, we will take notes and divide the answers between our research
questions. We have also chosen to present important quotes from the conducted inter-
views. We believe this approach will be helpful since we do not want to have too long texts
with information not related to our research questions. The information presented in the
empirical findings will not follow a chronological order, since we think that the interview
questions will probably not always follow the intended outline. This is due to the fact that
the respondents may answer questions before they are asked or the respondents might an-
swer questions similar to other questions and therefore it will not be necessary to present
these answers twice.
2.4 Selection of Respondents
Statistical generalizations are not central purposes within qualitative studies but nonethe-
less, the selection of respondents is an important process in the study. Since the purpose of
qualitative studies is to create a deeper understanding of a phenomenon, the selection
process is not random. The selected respondents must fulfill certain criteria which are theo-
retically or strategically determined (Holme & Solvang, 1997). We will base our selection of
respondents on convenience sampling. There are drawbacks with this type of sampling. For
example, Holme and Solvang (1997) claim conclusions from research based on conven-
ience sample may be misleading. We argue we have good reasons for using convenience
sampling since both the distances to the possible respondents and the time constraints will
not allow us to conduct personal interviews to the same extent if we randomly select or-
ganizations from the entire population. The benefits of using convenience sampling exceed
the drawbacks and we therefore argue that this approach is appropriate in this study.
To select respondents, the Affärsdata database will be used to search for manufacturing or-
ganizations. To limit the number of hits and only show manufacturing organizations, our
searches will be filtered using Svensk Näringsgrensindelning (SNI) codes, which follow the
General Name for Economic Activities in the European Union (NACE) standard. The appropriate
SNI codes will be found using the search criteria “Manufacturing”. We are only interested
in manufacturing organizations in the region of Jönköping and therefore the results will be
filtered by using Jönköping’s län and Västra Götaland’s län. Furthermore, we are only in-
terested in organizations with more than 300 employees since we believe these organiza-
tions have been working with BI for a longer time. We also intend to only interview or-
ganizations with more than one year experience of using BI. We believe those organiza-
tions have matured in their usage and therefore contribute with more interesting aspects of
how the organization has changed after the implementation. To be able to generate suffi-
cient empirical findings we will interview between four and seven organizations.
2.5 Method Validation
A study should, to be trustworthy, have a high degree reliability and validity. Those two as-
pects have a close connection. Reliability is determined by how accurate the researcher has
processed the information. Is the information the researcher presents reliable? Often there
is no problem to determine in qualitative studies since the respondent can participate and
give its opinion on how the researcher has interpreted the things the respondent described
during the interview. Validity is dependent on what we will research and measure and if
10
that is clear in the purpose of the report. To gain valid information is easier in qualitative
studies than in quantitative studies because the researcher is closer to the respondents and
the respondent gets the opportunity to control its participation. One problem to get valid
information is when the researcher does not understand what the respondent describes. It
can also be hard for the researcher to determine whether to be active or passive during the
interview. In different interviews it can differ in how valid information the researcher gains
depending on being active or passive. Another problem can be the relationship between re-
searcher and respondent. The respondent believes it should act in a certain way (Holme &
Solvang, 1997). To be able to generalize a study is very important since the study should
represent the reality and the result should be applicable to the entire population (Lundahl
& Skärvad, 1999).
To deal with the problems regarding reliability we will assure we are well-prepared before
conducting the interviews. Both the concept of BI and interview techniques will be studied.
We believe when we know the concept of BI well, and the respondents realize this, they
will give better and more advanced answers to our questions. After the interviews, we will
compile the interview material and send it via email to the respondents to assure that the
empirical material is correct and interpreted in the right context. We also believe this will
solve any translation mistakes that might have occurred, since the interviews will be con-
ducted in Swedish. Validity is harder to determine but since the interview questions clearly
are adapted to the research questions we believe we have high validity. During the process
of developing the interview questions we will have the difficulties concerning validity in
mind and try hard to adapt the interview questions to fit the purpose of this study. Also,
the selection of respondents will be carefully conducted. We will only interview respon-
dents with extensive knowledge and insight in how BI works to increase validity. To be
able to generalize our study we intend to conduct interviews until we have reached the point
of sufficient knowledge which means very few new statements and thoughts regarding BI
are discovered during the interviews. We will not be able to generalize our study for all in-
dustries in Sweden, but we believe generalization of the results from this study will be pos-
sible in one business sector; the Swedish manufacturing industry. Working with reliability
and validity during the entire research process we believe will increase generalization of this
thesis.
11
3 Frame of Reference
In this section we present some relevant theories for our study concerning Business Intelligence. We will also
focus on describing what decision support is and different levels and aspects of decision making.
The connections between the theories in the frame of reference are shown in figure 3.1 and
aims to clarify different parts in this chapter. It also illustrates different elements and the re-
lationship between them. Gorry and Scott Morton’s (1989) well-known framework for
management information systems (MIS) , which is widely present in the literature, serves as
the core base. Elements that are included to support this framework are; Anthony’s (1965)
description of managerial activities and Simon’s (1960) decision making model and deci-
sions.These parts are explained more in depth for a better understanding of the framework.
Eckerson’s (2003) BI environment is used to give an understanding of BI and its compo-
nents. It is divided into two parts, one technical side and one analytical side. The data wa-
rehousing environment contains data compiling from different source systems (also re-
ferred to as ETL), information quality, and a data warehouse (DW). On the other side, the
analytical environment describes decision support which is the outcome of BI systems
(Eckerson, 2003). Intuition has also been identified because it has a great impact in deci-
sion making processes (Hayashi, 2001). The final decision, made by a decision maker, is
shown on the right side of the model.
BI is applied on top of Gorry and Scott Morton’s (1989) framework. By doing this, we will
analyze if BI has contributed to changes in decision support. We will also examine if BI has
had any impact on organizational decision making from an operational, managerial, and
strategic point of view.
Figure 3.1 - The connection between theories in the frame of reference
12
3.1 Business Intelligence
Today, BI is used as an umbrella term for describing computerized decision support sys-
tems. However, BI evolved from DSS, a concept researchers started working on in the
1960s as computerized systems to assist in decision making and planning. As the develop-
ment of new types of DSS continued, the scope of the concept expanded and branched in-
to several different categories (Power, 2007). Power (2007) states that data-driven DSS in-
volve access and manipulation of organizational, sometimes external and real-time, data. It
could be simple files on a local machine or more advanced systems with additional func-
tionality, such as a DW. These systems generally enable analytical functionality and analysis
of historical data for support in decision making processes. “In general, business intelligence sys-
tems are data-driven DSS” (Power, 2007). The Gartner Group introduced the term BI in the
mid-1990s (Turban et al., 2007b). However, Watson (2005) states that BI is the result of a
continuous evolution. “Just because it has a new name doesn’t mean it is necessary new” (Watson,
2005, p. 4). Davenport and Harris (2007) conclude the entire field of systems for decision
support is referred to as BI.
However, Turban et al. (2007b) argue that BI evolved from DSS and their architectures
have some similarities, but there are some differences between BI and DSS. First, BI is
built with a DW and DSS do not need to include such a feature. BI is therefore better built
to support larger organizations but DSS can support any organization. Second, BI is de-
signed to support decision makers with timely and accurate information but indirectly while
DSS is designed to directly support specific decision making. Third, BI was developed by
software companies and DSS methodologies were mainly developed in the academic world.
Fourth, BI is constructed to fit the organization and is constructed with commercially
available tools while DSS targets unstructured problems and lots of programming is needed
to support those complex problems. Fifth, BI focuses more on executive and strategic
problems and DSS is constructed to support analysts. However, they are similar and con-
sist of similar features such as data mining and predictive analysis. BI software has been
changing and more decision support tools are built into the system. At present, BI and DSS
are not the same but they have a close connection (Turban et al., 2007b). Based on the dis-
cussion above, we agree with Turban et al. (2007b) and consider BI and DSS similar but
not completely the same.
The definition of BI is also heavily discussed in the literature. We have found four different
definitions of BI. Eckerson (2003, p. 1) defines BI as “BI solutions create learning organizations
by enabling companies to follow a virtuous cycle of collecting and analyzing information, devising and acting
on plans, and reviewing and refining the results. To support this cycle and gain the insights BI delivers, or-
ganizations need to implement a BI system comprised of data warehousing and analytical environments”. In
this definition Eckerson (2003) emphasizes collecting and analyzing data as well as using BI
in an organization-wide setting. Eckerson (2003) has the only definition that includes data
warehousing as the source for the data to be analyzed, which he claims to be crucial to gain
the insights BI delivers. Bräutigam, Gerlach, and Miller (2006, p. 2) uses a similar definition
but leave out data warehousing, “Business Intelligence is defined as getting the right information to the
right people at the right time. The term encompasses all the capabilities required to turn data into intelli-
gence that everyone in your organization can trust and use for more effective decision making”. Bräutigam
et al. (2003) put emphasis on turning data into intelligence for all users in the organization
to assist in more effective decision making. The third definition is from Turban et al.
(2007a, p. 9), “BI’s major objective is to enable interactive access to data, enable manipulation of these da-
ta, and to provide business managers and analysts the ability to conduct appropriate analysis”, which has
a lot in common with Eckerson (2003) and Bräutigam et al. (2003). Turban et al. (2007a)
discusses both the analysis of data for different users, on different organizational levels and
13
the use of the output. Loshin (2003, p. 6) uses a somewhat different definition, “The proc-
esses, technologies, and tools needed to turn data into information, information into knowledge, and knowl-
edge into plans that drive profitable business action. Business Intelligence encompasses data warehousing,
business analytic tools, and content/knowledge management”, leaving out the organizational aspect.
Based on the characteristics of these definitions we have developed our own definition of
BI:
Using data warehousing and analytical tools, BI is extracting and analyzing relevant information and
making it accessible to the right member as a support in decision making processes.A BI systems in-
volves four different components: A DW, which contains business data extracted from dif-
ferent sources in the organization; business analytics (BA), a collection of methods used to
perform mining, manipulation, and data analysis; business performance management (BPM)
which involves business monitoring and performance analysis; and finally user interfaces e.g.,
dashboards and reports (Turban et al., 2007b). Another dimension of the BI environment
proposed by Eckerson (2003) illustrated in figure 3.2 show data sources that feed the BI
system with appropriate data.
Figure 3.2 - The BI environment (Eckerson, 2003)
3.1.1 Data Warehousing Environment
The data warehousing environment shown in figure 3.2 in the left circle describes the BI
environment from a technical point of view. Technical aspects such as retrieving informa-
tion from different sources, the extract, transform, and load (ETL) process, and a DW
(Eckerson, 2003). There are different sources that are used to retrieve data in order to feed
the DW. Most commonly, data is sourced from multiple independent operational systems
but also from external providers. It can also be retrieved from online transaction process-
ing (OLTP) or an enterprise resource planning system (ERP). In some cases, even web data
in the form of web logs is used to provide a DW with data (Turban et al., 2007a).
As a critical component in any BI, information quality plays a superior and important role.
English (1999) asserts that there are two significant definitions of information quality, inher-
ent and pragmatic. Inherent information quality is data accuracy and to what degree data
represents the real-world objects. In contrast, pragmatic information quality is the useful-
ness and value of data used to support processes and enable organizations to accomplish
their objectives. Based on the two perspectives, English (1999) defines information quality
and describes the phenomena as “consistently meeting knowledge worker and end-customer expecta-
tions” (English, 1999, p. 10). In order to understand what information quality is, English
(1999) asserts it is important to understand the concepts of data, information, knowledge
14
and wisdom. Data is basically raw material from which information is derived and it can be
viewed as entities, attributes or facts. Information is when one knows the meaning of data
and it becomes understandable. However, information quality in itself is useless, but under-
stood, it leads to value for users. English (1999) describes that knowledge is when informa-
tion becomes assimilated and has a meaning in a certain context. Wisdom is applied knowl-
edge and an understanding how the knowledge can be applied in different settings (Eng-
lish, 1999).
The center of Eckerson’s (2003) data warehousing environment consists of ETL processes,
used to prepare the DW with useful and appropriate data. In ETL processes, Extraction
concerns gathering information from different databases and systems. Transformation is the
process where the extracted data are converted into a form which can be placed into a DW
or another database. Load concerns putting the data into a DW (Turban et al., 2007b). The
core of BI is ETL since the process brings together and combines data from multiple
source systems into a DW. This approach enables all users to work with a single set of data
referred to as “a single version of the truth” (Eckerson & White, 2003, p. 4). If the ETL process
is well-designed and executed, the organizational information is stored in one central loca-
tion and organizational members do not need to argue whether the information is correct
(Eckerson & White, 2003). Therefore, organizations can use the information to improve
key processes and as a competitive weapon. However, any issues regarding data quality
need to corrected before loading the information into the DW. Poorly designed ETL proc-
esses are difficult and expensive to maintain, change, and update (Turban et al., 2007b).
The ETL process must be carefully planned and highlight that this is a critical and impor-
tant part since the ETL design and development work consumes approximately 70% per-
cent of the time spend in a BI project (Eckerson & White, 2003; Turban et al., 2007b).
In the center of Eckerson’s (2003) BI environment, a DW is placed in order to store data
for decision support. The DW can appear as a number of smaller data marts consisting of
data for a single subject or area. Another repository for data is an operational data store
(ODS) with constantly updated data that is aggregated from business operations. One of
the most common repositories used is an enterprise data warehouse (EDW) which is a
large scale DW that is used for decision support across the entire organization. The main
purpose of a DW is providing consistent, integrated, nonvolatile collection of data in sup-
porting managers in decision making processes (Turban et al., 2007b).
3.1.2 Analytical Environment
The right side in Eckerson’s (2003) proposed BI environment refers to business user activi-
ties with a non-technical approach. The DW is used to support decision-makers with in-
formation in different variances depending on types of area and use. Business users can vi-
sualize, query, report, mine, analyze, and most importantly act on data in the DW. In some
cases, more advanced ad hoc (on-demand) exploration of the DW can be performed by
business users (Eckerson, 2003). Business analytics can be based on basic exploration of
data or more advanced analytical environments for qualified users (Turban et al., 2007a).
3.1.2.1 Decision Support
Visualization is used to make data more understandable and clear to end users. Decision
makers can browse the interface and analyze data in real time and examine organizational
performance data (Eckerson, 2003). Spreadsheets are one of the most common end-user
tools and it is often supported by three-dimensional visualization tools. Dashboards and
scorecards are two important components in business performance management (BPM)
15
systems, also called corporate performance management (CPM) systems. Both concepts
have a broader context in which organizational strategy is included and BI is a part of. A
dashboard can consist of key performance indicators (KPI) which are pre-defined meas-
ures critical and important to monitor. Scorecards are used to compare actual results with
planned results, depending on pre-specified measures (Turban et al., 2007a).
Reporting can be divided into two categories, routine reports and ad hoc reports. Routine
or standard reports are generated and distributed to relevant subscribers periodically. Fur-
ther exploration of the data presented allows business users to perform analysis with meth-
ods such as drill-down in order to find problem areas. However, reports can also be per-
formed ad hoc. Meaning that significant, specified, and relevant data can be compiled and
retrieved by business users (Turban et al., 2007a).
In the analytical environment, methods for drill-down, data mining, and queries can be
supportive for decision makers. Drill-down analysis is a process where the level of granu-
larity will result in more detailed data, excluding irrelevant and unnecessary data. Data min-
ing is used where reports and queries are inapplicable. The data mining process is used to
discover patterns that are impossible to interpret by humans, that can be of relevance and
guide decision making. Queries can be performed with structured query language (SQL)
and retrieve specific information determined by business users (Turban et al., 2007a).
3.2 Decision Making Processes
In the wide range of different decision types, Simon (1960) distinguishes two extremes, pro-
gramed decisions and non-programed decisions. The term program is borrowed from, and used in
the same way as in the computer world (Simon, 1960). A program is essentially a list of in-
structions or strategies executed in a certain order (Schneider, 2003). Thus, programmed
decisions are repetitive and routine. They follow definite processes or procedures which are
well-known, defined, and do not have to be treated as new every time they occur (Simon,
1960). Examples of programmed decisions are: sending out invoices; reorder supplies; pay-
ing a vendor. Non-programmed decisions on the other hand are unstructured and unusu-
ally consequential. No best practice for handling the problem exists because the problem
has not occurred before, the problem is of such complex nature, or the problem has such
high importance that it requires additional attention and handling (Simon, 1960). The deci-
sion to move manufacturing abroad or outsource the IT department are typical non-
programmed decisions. Simon (1960) points out how decisions exist in every shade of gray
in this spectrum and programmed and non-programmed are simply the far extremes.
Although the scope and impact of a decision varies greatly from one level in the organiza-
tional to another Simon (1960) argues that some generalization is possible. According to
Simon (1960), decision making includes four distinct phases. The first phase is finding situ-
ations for making a decision, intelligence. The second phase is referred to as design, where dif-
ferent courses of action are investigated. In the third phase, one of the available courses of
action is chosen. Simon (1960) calls this the choice activity. The fourth phase is a review, in
which previous choices are evaluated. These phases are illustrated in figure 3.3 below.
16
Figure 3.3 – The decision making model (Simon, 1960)
3.2.1 Managerial Activity
Anthony (1965) describes managerial activities in two different divisions, planning and con-
trol. The planning involves deciding what direction to take and the control means assuring
that desired outcomes are obtained. Based on these two main divisions Anthony (1965)
suggests three categories of managerial activities that he claims requires different ap-
proaches when developing systems. The first category strategic planning, involves the process
of deciding objectives; changes for the objectives; resources needed for the objectives and
policies that govern acquisition, use, and disposition of resources. Management control is the
process in which managers assure that resources are obtained and used efficiently in order
to achieve the organizational objectives. The third category operational control encompasses
that specific tasks are carried out efficiently. The main difference between management
control and operational control is that management control is concerned with people and
operational control with tasks (Anthony, 1965).
3.2.2 Management Information Systems
Gorry and Scott Morton’s (1989) framework for management information systems (MIS)
suggest that such system is best used from a decision making perspective. MIS work does
not always use a comprehensive approach, excluding organizational aspects that prevent
full appreciation. This generates temporarily solutions and inefficient allocation of re-
sources. The role of information systems in organizations is now focused on how systems
can capture parts in the human decision making process. As a result, organizations have re-
alized the potential of information systems, supporting sophisticated unstructured deci-
sions (Gorry & Scott Morton, 1989).
The framework is based on managerial activities and should not be used to describe infor-
mation systems. It is used only as a way to understand decisions made in organizations, in-
formation systems should only exist to support decisions (Gorry & Scott Morton, 1989).
The framework (figure 3.4) illustrates Anthony’s (1965) and Simon’s (1960) in combination
which provides two perspectives used to examine problems and purposes of information
systems activity.
The left side represents Simon’s (1960) programmable and non-programmable decisions.
Gorry and Scott Morton (1989) call these two categories structured and unstructured deci-
sions. The unstructured decision is where none of the three, intelligence, design or choice
in Simon’s (1960) decision making model (figure 3.4) is structured. The complete struc-
tured decision is where all three parts are structured and assists satisfactory decisions to be
17
made. Gorry and Scott Morton (1989) have added another dimension, semi-structured de-
cisions that encompasses that one or two of the phases are unstructured. The conclusion is
that above the horizontal line, largely structured decisions can be made with MIS. Deci-
sions below the horizontal line are largely unstructured and supported by decision support
systems (DSS) (Gorry & Scott Morton, 1989).
The top of Gorry and Scott Morton’s (1989) framework (figure 3.4) represents Anthony’s
(1965) managerial activities. Strategic planning involves predicting the future of the organi-
zation, consisting of a small group of high-level people in a non-routine environment
which makes it difficult to assess quality in planning processes. Management control in-
volves interpersonal interaction often driven by the strategic planning process. The opera-
tional control category consists of execution of tasks, with less judgment and this is what
distinct it from management control. It is important to be aware of that the distinction be-
tween operational control, management control, and strategic planning is difficult to de-
termine (Gorry & Scott Morton, 1989).
Managerial decisions that are below the line have a greater impact on the organization be-
cause they normally deal with unstructured decisions. Improvements in understanding de-
cisions will move the decision and it can be moved above the middle line and free up man-
agers for other tasks. Structured decisions are as Gorry and Scott Morton (1989) describe
them “organizational independent” which means they are almost the same in every organi-
zation although details may differ. As decisions become semi-structured or unstructured
the absence of routine procedures and lack of formalization between the parts in Simon’s
(1960) decision making model increase. In order to improve quality of decisions it is impor-
tant to have the right information input or change the entire decision process, or both. By
improving quality of information input, quality of managerial decision making may in-
crease. However, it will not have any major enhancements, since they rather need new un-
derstandings or methods, to understand the information that is already available (Gorry &
Scott Morton, 1989).
Gorry and Scott Morton’s (1989) implication of their framework suggest three different
perspectives, system design differences, different organizational structure, and model dif-
ferences considering Anthony’s (1965) managerial categories in mind. The first, system de-
sign differences implies that since operational control and strategic planning have different
objectives it is unnecessary to connect systems across boundaries since they do not have
similar objectives. Secondly, organizational structure involves defining problems and must
be dominated by managers involved in that specific area. Managers must also be analytical
and reflective rather that procedural and communicative. Third, strategic decisions do not
occur frequently and operational occur daily. This prove that models for decisions need to
be different and work efficient, have access to current data, be structured, and be easy to
change (Gorry & Scott Morton, 1989).
It should be mentioned that the activities within Gorry and Scott Morton’s (1989) frame-
work in figure 3.4 are only examples of different activities. The activities do not have any-
thing to do with the framework and organizations can apply its own activities as they wish.
In this study we will use the original activities as examples to provide an understanding on
what the framework could look like, when used in an organization.
18
Figure 3.4 - A Framework for Management Information Systems (Gorry & Scott Morton, 1989)
3.2.3 Intuition
Decision makers are sometimes excluding any form of logical analysis or rely only on in-
formation. Instead, different modes, referred as “intuition”, “inner voice”, “gut instinct”,
or “hunches” are used but decision makers have problems explaining the phenomena more
in detail. Emotions may be essential and supportive in order to filter various options quick-
ly, even if decision makers are not consciously aware of the screening themselves. How-
ever, human nature can easily obscure by observing and recognize patterns where none ex-
ists (Hayashi, 2001). According to Hayashi (2001), humans use patterns to find a pattern
and refers to Herbert A. Simon who proves that experienced people chunk information so
they can retrieve and store it easily. This because experienced and experts have the ability
to “see patterns that elicit from memory the things they know about such situations” (Hayashi 2001, p.
179). Hayashi (2001, p. 179) further explains the phenomena by quoting AOL’s former
president Robert W. Pittman “Staring at data is looking at a jigsaw puzzle. You have to figure out
what the picture is. What does it all mean? It is not just a bunch of data. There is a message in there.
Every time I get another data point I have added another piece to the jigsaw puzzle, and I am closer to see-
ing the answer. And then, one day, the overall picture suddenly comes to me.”
Humans have to review themselves because they have a tendency toward overconfidence.
This implies that humans overestimate their ability in just nearly everything. It is therefore
important to find out if decisions and judgments were accurate, and the use of feedback
19
can be very important. Otherwise, we do not know if we made any mistakes and cannot
learn or do anything about them. Because of this problem, it is very common that decision-
makers use self-checking procedures to ensure that intuitive decisions are successful (Haya-
shi, 2001). Another common method for intuitive decision making is cross-indexing. This
means that information can be combined from different sources. Hayashi (2001) state that
the amount of material used and can be cross-indexed enhances intuitional process. In or-
der to clarify Hayashi (2001, p. 181) refers to former CEO Michael Eisner of the Walt Dis-
ney Company who explains cross-indexing as “When you see a gas station sign or a certain forma-
tion in the clouds” intuition tells you something. Further on Eisner explains intuition as “You
use reams of historical information about yourself that you remember from when you were a child can pop
into your mind. Gut instincts are the sum total of those experiences – millions and millions and millions of
them. And that sum total enables you to make reasonable decisions.” (Hayashi, 2001, p. 183). Haya-
shi (2001) concludes that intuitive feelings guide our decision making to a stage until our
conscious mind is able to make good decisions.
20
4 Empirical Findings
In this part of the study we will present the empirical findings. All information from the in-
terviews will not be presented since the introduction questions are not relevant for the
study. The main focus in this chapter is to cover the interview questions (Appendix 1) re-
lated to our research questions and the purpose of our report. We have conducted four in-
terviews. Two interviews were conducted with system owners and the other two were con-
ducted with people who work with IT management. Relevant part of the interviews will be
presented below. After three of the interviews we got the opportunity to see the systems
used and can therefore use this experience in our conclusion and analysis since we consider
this as a relevant part of the empirical findings.
4.1 Husqvarna AB
Respondent and Position
Lennart Dorthé, CIO
Jan Winblad, Application Manager – BI
Place and Date Huskvarna, 2007-11-22
Husqvarna offers products for consumers and professionals within three main product ar-
eas: forestry, lawn and garden, and construction. With a presence in over 100 countries,
Husqvarna employs more than 11,400 people world-wide. Their IT department, located in
both Sweden and the US, is responsible for Husqvarna’s IT in every operating area
throughout the entire organization. IT is divided in application areas with global responsi-
bility for that application or concept. Huqvarna uses a “best of breed” strategy and buys
the system they think will fit the organization’s needs best.
BI is nothing new in the Husqvarna organization. As one of the first organizations in Swe-
den, Husqvarna started working with extraction of operational data from different sources
and systems to an environment optimized for analysis, the foundation of BI. The IT de-
partment first discovered the BI concept and saw the potential business value. They dem-
onstrated it to a group of business users who became interested and wanted it imple-
mented. Dorthé explains that they in the early stages focused on improving and streamlin-
ing supply chain processes, since these processes use units and quantities which are less
complicated to deal with. The next step was to include financial processes in their BI solu-
tion, a more complex task with more factors to consider such as exact definitions of finan-
cial measures, fluctuating currencies, and bonuses for sales people. “Providing decision makers
with incorrect numbers that they use in their analysis and decision making is extremely dangerous” Win-
blad explains. Dorthé points out that defining and agreeing on financial measures and KPIs
is time-consuming and involves people all across the organization. Today, the most fre-
quent users of their BI system are executives and controllers who use BI for strategic deci-
sion making. However, BI is also used in different supply chain functions to analyze opti-
mal replenishment levels.
Further, Dorthé explains that BI has changed Husqvarna’s business model. Before the BI
system was implemented, reports were created throughout the organizations, in the facto-
ries and sales companies, a manual process that in some cases took up to three months to
complete. Now, this data is refreshed once a day and some data is refreshed as many as
seven times daily. Production line managers have even expressed their wishes for expand-
ing the solution to show real-time data. Winblad claims that this is not needed in most situ-
ations. By analyzing financial information in the DW, top management gets a full picture of
21
how well different sales companies and branches perform. This makes it easier to help local
branches and sales managers focus on certain areas, countries, or products with higher
profitability. Using their BI system Husqvarna’s executives can now follow long-term
trends, which earlier was impossible.
One of the main objectives with Husqvarna’s BI investment was to get everyone to “speak
the same language”. To be able to implement BI, sales companies world-wide were forced
to decide on item codes. Earlier, the same item code could be used for a chain saw in one
country and a lawn mower in another, which made item-level audits impossible and the de-
cision support consisted of high level summaries without any ability to show details,
Dorthé explains.
Dorthé claims that the information generated in the BI is correct, current, and relevant.
However, both Dorthé and Winblad agree that the information from the BI will never
meet everyone’s needs. “IT systems evolve and change and business users have new KPIs to track”,
Winblad explains.
Dorthé states that BI is limited to gather and show information but never make decisions
for the user. The outcome depends on the person making the decision. “The more useful in-
formation you have access to, the higher it the probability for making a better decision” Dorthé claims.
Husqvarna’s strategy is not to push data and reports to the business users using email, even
though this functionality exists. Instead, they want users to use the system themselves to
get the information they need in their job and make them aware of additional tools for
deeper analysis. Dorthé claims that the BI system is used frequently and it has received
great appreciation from the user community. One reason might be that the user friendly in-
terface. “In 10 years, BI is probably used by everyone throughout the entire organization”, Dorthé says.
4.2 Fläkt Woods AB
Respondent and Position Esbjörn Sjögren, Controller and System Owner – BI
Place and Date Jönköping, 2007-11-22
Fläkt Woods is a global supplier of air solutions. They employ more than 3,000 people and
have a local presence in 95 countries. In their Jönköping branch, Fläkt Woods has used
computers in their business since the 1970s, mostly Manufacturing Planning Systems
(MPS). A couple of years ago they started seeing a need for BI in their organization.
Sjögren explains how business users needed help from IT or employees with advanced
programming skills to run reports. The information was available in their system but it
could only be accessed using SQL programming which most business users did not know.
Also, having different users accessing data in the database without an appropriate user in-
terface was not the best solutions since mistakes could easily be made in live production
data. The production systems also predicted an increase in performance if the data was
moved outside the system, into a DW, to facilitate data analysis. A BI system based on Mi-
crosoft’s tools was implemented in the Swedish organization.
Fläkt Woods uses the BI system the way it was planned, within two main areas: finance and
sales. However, Sjögren states that the marketing department uses some information from
the BI. The BI assists business users to prepare financial statements and reports and in
finding the root cause to errors and mistakes, such as incorrect margins, in situations where
the specific order is unknown. The user interface enables fast access to the numbers and
22
time is saved when reports can be reused with data refreshed to show current numbers by
pressing a button. Also, Sjögren explains that it is easy to drill-down among orders to find
the one that is incorrect. The marketing department uses the BI system to see how well dif-
ferent markets perform. Predefined dimensions in the DW enable presentation of products
sorted by time, units, or margins etcetera.
The information from the BI system is used all across the organization. The users know
how much more information they have access to and that it can be compiled faster than
before. Sjögren explains that some users now have higher demands for access to reports.
They also have higher demand for detailed information such as sales numbers for specific
products. Problems occur when the users accept the system generated numbers without
challenging or critically examine them. Sjögren states that it does not matter if the system
works well when the input is of poor quality. You could say “garbage in, garbage out”, Sjögren
explains.
Sjögren experiences the BI generated decision support relevant, correct, and up-to-date.
The general data in the DW is updated every night, some data even more often. Access to
the data is easy in the Excel-based environment the BI solution provides. However,
Sjögren expresses the importance of data quality and the need for continuous work with
quality assurance. “With experience in your role, you develop a feeling for when the computer generated
numbers are correct and when they are off”, Sjögren explains. If the decision support lacks infor-
mation or important numbers, you know where in the systems to get these numbers from.
Since the BI system uses year to date (YTD) data, progressively build up during the year,
analysis is limited during the first months of the year. “If you only have one order to analyze, it is
hard to know if the margin is right since it is based in limited information and statistical analysis is impos-
sible”, Sjögren explains. The user selects the information to be displayed and Sjögren does
not think the BI system provides too much information. “However, if you select to have a lot of
information displayed at the same time, of course it will be hard to work with”, Sjögren concludes.
Even though the BI system provides business users with better decision support, Sjögren
claims that intuition is used in all decision making. “Decisions are made for future and the infor-
mation in the BI system is historical information”, Sjögren states. As an example he uses the fore-
cast, which always is slightly off. Taking this into consideration, the forecast is useful even
though it is slightly off.
Fläkt Woods has realized the potential of BI and plans further investing in new functional-
ity. Sjögren states that it could be graphically improved and include more features.
4.3 Myresjöhus AB
Respondent and Position Lars-Göran Wiss, Manager, ERP
Place and Date Myresjö, 2007-12-10
Myresjöhus employs more than 600 people in Sweden. They market, produce, and build
smaller residential houses in wood for the Nordic market. The organization consists of two
subsidiaries, Smålandsvillan and a company handling land, Myresjö Mark. Myresjöhus has
used computer systems to improve their business for more than 30 years. The accounting
and finance systems were the first systems to be implemented. In the early 1980s, the first
production and manufacturing systems were introduced.
23
Wiss explains that the business users constantly need to know how well they perform. They
are interested in total sales numbers, but also how total sales are divided between house
sales and land sales. Management is interested in how well the organization and different
sales offices perform. Myresjöhus’ ERP had these numbers but it was difficult and time-
consuming to collect them in reports. They needed a better tool to measure three critical
indicators: “How much do we deliver?”, “What are our sales?”, and “What is our backlog?”. The so-
lution was a BI system that was implemented during the first quarter of 2006.
The BI system provides good answers, faster, to all those questions. “The main goal with the
investment was to provide the business users with faster reporting”, Wiss explains. Not only is the de-
cisions support generated faster, the business users do not have to rely on Excel to the
same extent as they used to. All the source systems feed the DW with data and it is easy to
see when something is incorrectly entered into the system.
The decision support generated in the BI system is easy to understand and use. Depending
on position in the organization, the BI system is used differently. Some users, such as em-
ployees within sales, use the system daily. Other functions, such as management, use it
more on a weekly basis. Wiss explains that the most users use the improved reporting func-
tionality. “Experienced users tend to use the drill-down capabilities more”, Wiss explains. Since the
implementation, no one has expressed any complaints.
Wiss claims that the system is used the way it was planned. However, some functionality
and reports have been added that was not planned for such as automated tracking of the
number of errors completed houses have, which should be zero. Some users have re-
quested to change the time between receiving reports, which is easy for the system owner.
The information in the DW is refreshed every night which, according to Wiss, is enough.
“It is more a question of business user not being able to enter the numbers in to the source systems fast
enough”, Wiss explains.
It is hard to say whether the business users make better decision today compared to before
the BI system was implemented. However, decision makers have access to better decision
support, faster, and the information is more complete. With more complete information,
decisions that earlier were unstructured now are more structured. However, decision sup-
port has a great impact on decision making but is only a support.
Wiss states that in the future, BI could help improve manufacturing processes. “It is a ques-
tion of maturity. The users understand that they can get more information from existing systems which re-
sults in new requests and higher demands”, Wiss explains. However, Myresjö has no plans in fur-
ther investing in BI at the moment. Focus is to build on the current system and improving
and adding reports. Further, Wiss states that it is important to get more users to use exist-
ing BI tools, not only the reporting functionality.
4.4 Kinnarps AB
Respondent and Position Pierre Condradsson, IT Manager
Place and Date Kinnarp, 2007-12-13
Kinnarps is one of the largest suppliers of office solutions in Europe. Their headquarters,
along with five factories, are located in Sweden. Kinnarps employs 2,000 people and they
are present in 34 countries. They started working with computer systems in the early 1970s.
During that time they also implemented their first ERP system. In 1996, Kinnarps imple-
24
mented a new ERP system. The new system worked very well with Kinnarps’ business
processes but had a poor user interface and the reporting capabilities were even worse,
which created a need for a complementing tool for analysis and reporting. In 1998, Kin-
narps started working with Cognos and together they implemented a BI system.
Kinnarps has a horizontal organization and they always try to have as much decision mak-
ing as far down in the organizations as possible. Conradsson states that BI plays an impor-
tant role in enabling this and the BI system is used throughout the entire organization. “BI
is used in different functions from financial reporting for top management and CFO, to associates working
in production, to sales statistics sales in different sale companies”, Conradsson explains. According to
Conradsson, the BI system is used in every business process, including sales, production,
supply chain as well as finance and accounting.
Earlier, business users could look at the same numbers but see different values. This was in
many cases due to timing, which was an issue before the DW was implemented. Having a
single version of the truth makes it easier to agree that the decision support is correct. The
decision support generated in the BI system is current thanks to daily updates in the DW,
which is frequent enough for the organizational needs. However, production associates
have requested more frequent updates and in some cases, information is extracted straight
from the source, the ERP system, into the BI system.
After the implementation of the BI system, the need for decision support changed. Users
now know that improved decision support is available and new requirements and needs
have arisen. “Better tools for analysis and reporting have led to increased need for better decision support”,
Conradsson explains. Also, the BI system has enabled top management to monitor how
the organization performs using new KPIs. Conradsson claims that more decisions are
made based on solid facts compared to before the BI system was implemented. Business
users and top management want to know underlying causes to business problems and un-
derstand cause-effect correlations, something the BI system has made possible.
Conradsson states that most employees have a feeling for when numbers are off. “The num-
bers generated by the BI system are double checked on a regular basis to make sure that decision makers
have as correct decision support as possible”, Conradsson explains. The BI system enables presen-
tation of information from different sources in both tables and graphs, which is easier than
finding the number in different source systems. Another improvement is the system’s abil-
ity to present number aggregated. Conradsson believes the BI system will never be com-
pleted. “The BI system is constantly improved and extended as new systems are implemented in the or-
ganization and demands for new needs and KPIs arise”, Conradsson explains.
Conradsson claims that the decision maker makes the final decision. However, the BI sys-
tem enables efficient and continuous reporting, which allows business user or top man-
agement to agree on certain actions when different KPIs are met. “This is an increasing phe-
nomenon, especially in the sales organization. I believe this is related to the improved information sharing,
which makes it easier to set up rules such as ‘If the discrepancy is this much, this person makes the decision.
If the discrepancy is larger, this person makes the decision’”, Conradsson explains.
Conradsson states that the need for analytics will increase in the future. “Being able to find
patterns in data and understanding what impact ‘this change will have on that’”, Conradsson explains.
However, no further investments are planned. Conradsson explains that their BI environ-
ment is very flexible and scalable. The only BI related investment would be more licenses
as subsidiaries are integrated and more and more users start using the system.
25
4.5 Summary of Empirical Findings
All the interviewed organizations use BI extensively. They explain how BI provides them
with correct and updated data. Decision making has become easier when everyone has ac-
cess to the same data, something that was a problem before the implementation. With one
centralized data source, the data quality has improved and so has control. Also, it is easier
to access decision support when users do not need to search in multiple source systems to
find relevant information. Organizations report better decision support and new opportu-
nities to use data, that has been available before but not accessible, easier and faster. The
improved analytical capabilities have made it easier and faster to find problem areas and er-
rors. New functionality, such as drill-down and aggregation of data has made presentation
of data better and easier. Decisions have become more structured due to more complete
information. Making strategic decisions are complicated since more unstructured variables
influence such types of decisions. The organizations all agree that humans are involved and
influence decision making, in the end humans make the final decision. The BI investments
are seen as on-going processes. All interviewed organizations reported they will increase
their use in the future.
26
5 Analysis
In this chapter, we apply the theories presented in the frame of reference on our empirical findings to find dif-
ferences and similarities. The chapter is divided in two parts, one for each research question.
To not lose focus during the analysis, we have divided this chapter into two sub-chapters,
which aim to answer one research question each. In each sub-chapter, our frame of refer-
ence will help us reflect over and discuss our empirical findings. The first sub-chapter, deci-
sion support, concerns the research question; “How has decision support changed after a BI im-
plementation?” and the second sub-chapter, decision making processes, concerns; “How has
BI changed the way organizations make decisions?”.
Using theories and empirical findings in an iterative process, we will analyze reoccurring
statements and try to find similarities and differences between interviews. We will first
study our empirical material and then use theories from our frame of reference to try to
explain the phenomena. During this iterative process, we strive to better understand the
empirical findings and add our own reflections and thoughts. This process is illustrated in
figure 5.1.
Figure 5.1 - Analysis model
5.1 Decision Support
Eckerson’s (2003) BI environment consists of two parts, the DW environment and the
analytical environment. The data warehouse environment consists of ETL processes, which
combine data from multiple source systems, to prepare the DW with useful and appropri-
ate data. Comparing this to our empirical findings, we have found that organizations run
this process at least on a daily basis. However, if necessary, the ETL processes feed the
DW several times a day since decision makers may need current and updated decision sup-
port. For example, Winblad (J. Winblad, personal communication, 2007-11-22) explains
that some data in Husqvarna’s DW is refreshed as many as seven times daily. Our opinion
is that the more often data is updated, monitoring organizational performance and acting
upon abnormal variation becomes easier.
A DW enables users to work with a single set of data. Eckerson and White (2003) refers to
this as a single version of the truth. The results from the conducted interviews show that
organizations use this approach. Conradsson states that having a single version of the truth
makes it easier to agree that the decision support is correct (P. Conradsson, personal com-
munication, 2007-12-13). Similar, Husqvarna’s main objectives with their BI investment
was to get a common data set for decision support through a DW (L. Dorthé, personal
27
communication, 2007-11-22). However, we think it is important for organizations to have
clear procedures and guidelines for external data collection, such as what source to get cur-
rent exchange rate from, to achieve a single version of the truth. Another reflection is that
arguing about numbers, KPIs and other measures can be limited and time can be saved
when organizations have decision support based on a single version of the truth.
Eckerson and White (2003) argue that to be able to use decision support in decision mak-
ing, data quality is crucial. ETL processes are a natural part in any BI system to ensure that
high data quality is obtained. English (1999) argues that data quality is meeting business us-
er and end-customer expectations. Since ETL processes try to assure data quality, this may
not necessarily imply that the end-users are satisfied since opinions of what quality is may
vary. Our empirical findings show that organizations are aware of this issue and work ac-
tively to minimize mistakes and poor data quality. Sjögren (E. Sjögren, personal communi-
cation, 2007-11-22) states “garbage in, garbage out”, if data input is incorrect, it does not mat-
ter how well the system works, the output will still be incorrect. Winblad (J. Winblad, per-
sonal communication, 2007-11-22) states that “providing decision makers with incorrect numbers
that they use in their analysis and decision making is extremely dangerous”. To minimize the impact
of incorrect data, Kinnarps even double checks numbers generated by the BI system on a
regular basis to ensure that decision makers have as correct decision support as possible.
The general impression among all the interviewed organizations is that the data in their
DWs is correct and up-to-date. We think it would be interesting to examine to what extent
their organizational data actually is correct. However, we think it is impossible to have
completely correct data. The fact that our respondents believe they have correct and cur-
rent data might imply that organizations spend appropriate time and resources in develop-
ing and maintaining ETL processes for even better decision support. Eckerson and White
(2003) and Turban et al. (2007b) claim approximately 70% of time spent in a BI project
should be spent designing and developing ETL processes. Organizations may not realize
the importance of developing and maintaining ETL processes which requires a lot of re-
sources.
Our definition of BI states that decision support should be accessible to the right member
in the organization. This implies that organizational members do not need to spend time
searching for decision support in different source systems. Instead, necessary information
should be available using the BI interface, which is extremely time saving (Turban et al.,
2007b). All respondents agree that gathering data for decision support used to be a manual
and time consuming process. They describe a new situation where decision support is ac-
cessible in a timely manner. Fläkt Woods states that the information has always existed but
now, with a central storage, it is easier to gather relevant information from the source sys-
tems (E. Sjögren, personal communication, 2007-11-22). Using BI, Turban et al. (2007a)
argue that both routine and ad hoc reporting is easy. This enables easy access to decision
support for every decision making situation. Our interviews show that organizations use
both routine and ad hoc reports, as Wiss puts it, “business users constantly need to know how well
they perform” (L-G. Wiss, personal communication, 2007-12-10).
Eckerson (2003) claims that visualization makes data easier to understand. The user tools
provided in BI systems enable users to interpret data and perform data analysis. According
to Turban et al. (2007a) drill-down functionality could be used to analyze data and find
problem areas. All respondents mention improved decision support with ability to both ag-
gregate data and drill-down among data in order to fit different decision situations as well
as possible. Sjögren (E. Sjögren, personal communication, 2007-11-22) explains that it is
easy to drill-down among orders to find the incorrect one. Our empirical findings imply
28
that BI systems provide improved decision support. Users now have better overview when
numbers from multiple source systems are presented in spreadsheets and dashboards, ta-
bles, and graphs.
According to the respondents, the decision support is considered to be easier to use when
users decide information to be displayed. Information not relevant for the specific decision
making situation can easily be opted out and not displayed (Turban et al., 2007b). Even if
the respondents claim it is easy to gather and display information, it is of course a situation
were the users need to mature in their usage of the BI system. We believe users who have
matured in their usage are starting to realize the benefits BI generates. With an increased
level of maturity new exciting areas of use will probably be discovered. It may also depend
on computer skills, an experienced computer user will probably use more BI functionality
for generating ad hoc decision support at an earlier stage, compared to a user with limited
computer experience.
5.2 Decision Making Processes
Gorry and Scott Morton (1989) base their framework on decision making processes and
they point out that the framework should not be seen as a way to describe information sys-
tems. Since BI systems’ main objective is to assist decision making, we believe Gorry and
Scott Morton’s (1989) framework is useful to describe how BI could support and change
decision making. Gorry and Scott Morton (1989) argue that when organizations understand
the underlying parameters of decisions, it is possible to make them more structured, and
save time for other tasks. This because decisions can be supported by the system itself with
less influence from decision makers and therefore, decisions become more structured. We
argue that BI systems, using its analytical environment, could transform either semi-
structured or unstructured decisions to become more structured.
As described in 5.1, BI enables faster access to decision support, which has a direct impact
on time spent in decision making processes. This may imply that more efficient processes
can be achieved due to time savings. Husqvarna reports improved processes with instant
access to information, a process that used to be manual and took up to three months to
complete (L. Dorthé, personal communication, 2007-11-22). Conradsson (P. Conradsson,
personal communication, 2007-12-13) states that Kinnarps’ BI system has made decision
making processes more structured such as predetermined action when different KPIs are
met. Myresjöhus mentions that unstructured decisions have become more structured due
to more complete information (L-G. Wiss, personal communication, 2007-12-10).
The top of Gorry and Scott Morton’s (1989) framework includes Anthony’s (1965) catego-
ries of managerial activities; operational control, management control, and strategic plan-
ning. The time horizon and complexity concerning decisions between these categories dif-
fer. Strategic decisions involve activities with a longer time horizon and they do not occur
frequently whereas operational decisions are made on a daily basis and concern simple
tasks (Gorry & Scott Morton, 1989). According to Gorry and Scott Morton (1989), deci-
sion making on each organizational level requires different decision support. In Myres-
jöhus, the BI system is used differently depending on position in the organization. Some
users, such as employees within sales, use the system daily whereas other functions, such as
management, use it more on a weekly basis (L-G. Wiss, personal communication, 2007-12-
10). Turban et al. (2007a) state that BI supports decision making on every hierarchical level.
We argue that BI is applicable to Gorry and Scott Morton’s (1989) framework and could
assist any member of any organizational level. This is also in line with the results of our
empirical study. Our empirical findings show that BI is mainly used for operational and
29
management control. However, Husqvarna and Kinnarps also use BI to support strategic
planning. We believe the limited use of BI in strategic planning is due to the more unstruc-
tured and complicated parameters strategic decisions tend to concern. Both Husqvarna and
Kinnarps have a high maturity level of BI, which we believe might be the reason why BI is
so wide-spread.
We have found similar areas of BI usage in our empirical findings. Most frequently, BI is
used to assist decision makers in financial and sales departments to generate reports and
enable analysis. However, some differences have been noticed. Husqvarna uses BI in most
departments to support decision making. Kinnarps even claims they use BI in every pri-
mary process. “[In Kinnarps,] BI is used in different functions from financial reporting for top manage-
ment and CFO, to associates working in production, to sales statistics in different sale companies”, as
Conradsson (P. Conradsson, personal communication, 2007-12-13) explains. From our
empirical findings we conclude that Anothony’s (1965) categories of managerial activities
are covered. All respondents believe their BI investment is an on-going process where new
areas of use constantly are discovered, connected, and included in the BI system.
Husqvarna believes, in 10 years, BI will be used by everyone throughout the entire organi-
zation. Kinnarps believes BI usage will increase as affiliated companies are integrated in the
system. Fläkt Woods and Myresjöhus have a different focus and believe they will add more
functionality and get the existing users to better use the analytical functionality of their BI
systems.
Any decision making process involves humans to make the final decision. All respondents
point out their BI system is only supportive in this process and the decision maker puts the
decision into action. Further, the respondents explain that BI generated decision support
has a great impact on decision making processes, but indirect and supportive, which Tur-
ban et al. (2007b) agree with. Indirect decision support does not provide an answer to a
problem but provides support to solve it, such as numbers or graphs (Turban et al. 2007b).
Dorthé (L. Dorthé, personal communication, 2007-11-22) states that “BI is limited to gather
and show information but never make decisions for the user. The outcome depends on the person making the
decision”. We think human ability to make decisions and systems’ ability to provide decision
support is the best combination for effective decision making. As Hayashi (2001) explains,
the main objective with cross-indexing is to gather information from different sources,
which also is the main purpose of a DW. Therefore, we conclude that BI easily can be ap-
plied to cross-indexing and support intuitive processes. When a decision maker uses cross-
indexing, in this case BI and the generated decision support, Hayashi (2001) argues that in-
tuition tells you something, feelings that guide the decision making until satisfactory deci-
sions are made. Decision support or BI systems cannot replace human intuitive ability. De-
cision makers have the ability to apply previous experiences combined with decision sup-
port as they may see patterns they recognize from similar situations (Hayashi, 2001). Fläkt
Woods states that “with experience in your role, you develop a feeling for when the computer generated
numbers are correct and when they are off” (E. Sjögren, personal communication, 2007-11-22).
30
6 Conclusions
In this chapter, we present the outcome of our analysis in a few concluding statements. The results are pre-
sented and divided between our research questions.
Our analysis show positive effects on organizations after a BI implementation. As we dis-
covered, BI is wide-spread in manufacturing organizations and is becoming more inte-
grated in the organization.
How has decision support changed after a BI implementation?
Control: We have identified that BI improves the control of decision support. This be-
cause organizations can unify source systems meaning that the input of the decision sup-
port is stored in one place. This also enables quality control of information gathered from
source systems throughout organizations. Another aspect is that BI improves the ability to
analyze decision support with more flexibility compared to before the implementation. On-
ly relevant data for decision making is presented which allows better decision support and
better decisions.
Time: BI has proven to save organizations time by enabling decision support to be easily
accessible in a timely manner. Decision support can now be used by members routinely or
by performing ad-hoc analysis, with correct, updated, and sufficient information. Earlier,
this process was complicated and time consuming as information was scattered throughout
different location in the organization.
How has BI changed organizational decision making?
Improved decision making: BI provides faster and easier access to decision support
which has a direct impact on decision making. Organizations experience improved and
more efficient processes. This is a result of BI’s ability to provide more accurate and cur-
rent data for analysis. Since decision makers have a unified data storage, insight in other
functions or areas increases the ability to make better decision.
Unstructured decisions are now more structured: By using BI, decisions that earlier
were unstructured or semi-structured are now more structured. Especially in financial and
operational functions as such functions involve specific procedures, which are easier to
structure. With less time spent in decision making processes, decision makers now have
more time for other activities. This means, variables that are below the horizontal are re-
ferred to as unstructured decisions are moving upward and become more structured with
BI.
Intuition in decision making: Even though BI has improved quality of decision support
substantially, humans will always make final decisions influenced by intuition and experi-
ence. BI has the ability to reduce the unstructuredness in decision making processes by
transforming decisions to a lower level of unstructuredness. When decisions are less un-
structured, intuition becomes less important. However, intuition can also support decision
makers when past experiences provide new ideas or explain patterns.
31
7 Final Discussion
In this chapter we reflect on our work and give suggestions for further research concerning Business Intelli-
gence. We also acknowledge people that have supported our thesis work.
7.1 Reflections
Writing about BI has been interesting and we believe this thesis has contributed with new
knowledge. Our approach with recognized theories and new concepts is interesting and
may contribute to new viewpoints and thoughts. Turban et al. (2007a/b) are highly present
in our thesis. We would have preferred having other references but have concluded during
our literature study that Turban et al. (2007a/b) are well-known and highly represented and
referred to in BI literature. The qualitative approach was appropriate for our study since it
provided deeper understanding of our research topic, something a quantitative approach
could not. Using a quantitative approach, the characteristics of the empirical findings would
have been different and we might have discovered other aspects and come to different
conclusions. When selecting respondents, we decided to have between four and seven in-
terviews. However, after conducting four interviews we felt we had sufficient information
to fulfill our purpose. Our interview method, semi-structured, suited our thesis well and it
would have been difficult to fulfill our purpose using a different method. A better connec-
tion between our theoretical framework and the interview questions could have improved
our empirical findings and analysis. However, we feel we have answered our research ques-
tions and fulfilled our purpose.
A different sampling method could have provided higher generalization of our study.
However, using convenience sampling to select our respondents, we contacted 11 organi-
zations. Five used BI systems according to our definition and had more than one year ex-
perience of BI, hence fulfilled our criteria. One did not want to participate since their BI
system included sensitive information. The other four agreed to participate in our study.
Even though our sampling technique may be a weakness in our study, we believe the simi-
lar results from our empirical findings allow us to generalize our study within our presented
problem delimitation.
7.2 Suggestions for Further Studies
During the process of writing this thesis, we have discovered that there is limited research
concerning BI and this area needs to be further explored. Accordingly, we have many sug-
gestions of research topics. We believe it would be interesting to investigate the amount of
time a BI system could save an organization and which functions that benefit the most
from BI initiatives. Another topic could be the connection between BI and financial re-
turns with its problematic aspects of measuring intangible benefits. From a vendor’s per-
spective, to investigate how organizations and users would like to improve their BI solution
and why. Our respondents pointed out that decision makers use BI for support in decision
making and further studies could concern intuition and its role in decision making proc-
esses. From a technical point of view, future sources of data and other areas that can be in-
tegrated in BI systems for better decision support is another research topic. We believe it
would be interesting to research if there is a need or interest for organizations to connect
to suppliers’ systems to perform data analysis for better planning and process performance,
as new technologies for distributed data processing evolve. This thesis has not focuses on
HCI and another area for further research could be how to improve the user interface of
BI systems.
32
7.3 Acknowledgements
We would like to conclude by thanking the people that have helped us through this thesis
writing process. Our tutor Jörgen Lindh, for guidance and valuable feedback. We would al-
so like to thank Hiren Jansari and Johanna Reinholtz at SYSteam for demonstrating BI
products and providing us with valuable insight in the BI concept. All our respondents for
taking their time and participating in interesting and very informative interviews. Further-
more, we would also like to thank our fellow students for good advice during seminars.
33
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35
Appendix 1 – Interview Questions, English
Personal
Position?
Tasks and responsibilities?
How long have you had your current position and how long have you worked in this or-
ganization?
Previous tasks and responsibilities?
Organization
Could you briefly describe your organization?
How long have you used computer systems?
What have the computer systems historically been used for?
What is the hierarchical structure like?
Business Intelligence system
Why did you invest in BI?
Which BI system are you using?
Who implemented the system and who is responsible for maintenance?
In what areas do you use BI?
- For which decisions do you use BI? (Operational, Managerial, Strategic)?
- Who uses your BI as a support in decision making?
Has BI changed you organization and if yes, how? (Positive and Negative)
How frequently is BI used in decision making?
- Are you using BI as planned?
Has BI improved your decision support and if yes, how?
What sources are used for the decision support? Which are the most important?
Do you think the decision support is relevant, correct, up-to-date, and enough?
- Are decisions made with insufficient decision support?
Is the decision support available in a timely manner?
- How and why?
Is it easy to understand and use the decision support generated from your BI system?
- How and why?
What are your thoughts on the amount of decision support?
To what extent are decisions made, based on decision support from your BI system?
Does your organization make better decisions now; compared to before the BI system was
implemented?
- Can the decision support be improved and if yes, how?
Has the degree of intuition in decision making decreased since the implementation of BI?
What do your future needs for BI look like?
What pros and cons do you see with your BI system?
What are your plans for future BI investments?
Do you believe your BI system fulfill expectations?
36
Appendix 2 – Interview Questions, Swedish
Person
Befattning?
Uppgifter och ansvarsområden?
Tid i befattning och företaget?
Tidigare uppgifter och ansvar?
Organisation
Kan du kort beskriva er verksamhet?
Hur länge har ni använt datorsystem?
Vad har datorsystemen använts till historiskt?
Hur ser den hierarkiska strukturen ut?
Beslutsstödsystem
Varför har ni valt att investera i BI?
Vilket BI-system använder ni?
Vem implementerade, och vem ansvarar för driften för BI-systemet?
Inom vilka områden använder ni BI?
- Till vilka typer av beslut används ert BI (Nivå s, t, o)?
- Vilka använder ert BI för beslutsfattande (Nivå s, t, o)?
Har BI medfört förändringar i ert företag och i så fall vilka (positiva, negativa)?
Hur frekvent används BI som beslutsunderlag?
- Utnyttjas BI-systemet som det var tänkt?
Hur såg ert beslutsunderlag och ut före implementeringen?
Har beslutsunderlaget förbättrats och i så fall hur?
Vilka källor används till beslutsunderlaget och vilka är viktigast?
Känns beslutsunderlaget relevant, korrekt, aktuellt och tillräcklig?
- Tas beslut med ett otillräckligt beslutsunderlag?
Finns beslutsunderlaget tillgängligt på ett tidsmässigt tillfredsställande sätt?
- På vilket sätt och varför?
Är beslutsunderlaget lätt att utläsa och använda?
- På vilket sätt?
Vad anser ni om mängden beslutsunderlag?
Hur stor del av besluten baseras på beslutsunderlaget genererat av ert BI?
Tas bättre beslut genom beslutsunderlaget nu än innan BI-systemet?
- Kan beslutsunderlaget förbättras och i så fall hur?
Har mänskligt beslutsfattande minskat med BI-implementeringen?
Hur ser behovet ut framöver?
Vilka fördelar och nackdelar tycker ni BI medför?
Finns det tankar om ytterligare investeringar i BI?
Stämmer ert BI med förväntningarna?
doc_664672443.pdf
Business Intelligence The impact on decision support and decision making processes
J ÖNKÖP I NG I NT E RNAT I ONAL BUS I NE S S SCHOOL
JÖNKÖPING UNIVERSITY
Busi ness I ntel l i gence
The impact on decision support and decision making processes
Bachelor’s thesis in Informatics
Author: Daniel Andersson
Hannes Fries
Per Johansson
Tutor: Jörgen Lindh
Jönköping January 2008
Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics Bachelor’s thesis in Informatics
Title: Title: Title: Title: B BB Business Intelligence usiness Intelligence usiness Intelligence usiness Intelligence – –– – Impact on decision support and decision making Impact on decision support and decision making Impact on decision support and decision making Impact on decision support and decision making
Author: Author: Author: Author: Daniel Andersson Daniel Andersson Daniel Andersson Daniel Andersson
Hannes Fries Hannes Fries Hannes Fries Hannes Fries
Per Johansson Per Johansson Per Johansson Per Johansson
Tutor: Tutor: Tutor: Tutor: Jörgen Lindh Jörgen Lindh Jörgen Lindh Jörgen Lindh
Date Date Date Date: 2008 2008 2008 2008- -- -01 01 01 01- -- -17 17 17 17
Subject terms: Subject terms: Subject terms: Subject terms: Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, I Business Intelligence, Decision Support, Decision Making, In nn ntuition tuition tuition tuition
Abstract
Introduction
Historically, decision support systems have been used in organizations to facilitate better
decisions. Business Intelligence has become important in recent years because the business
environment is more complex and changes faster than ever before. Organizations have
started to realize the value of existing information in operational, managerial, and strategic
decision making. By using analytical methods and data warehousing, decision support can
now be used in a flexible way and assist decision makers in decision making processes.
Problem
Increasing investments in Business Intelligence indicate that it can bring value to organiza-
tions. Benefits such as the ability to access relevant and timely decision support when it is
needed can be of tremendous value when the use of existing information has become more
a question of survival or bankruptcy for an organization, than profit or loss. Thus, it would
be interesting to see how decision support and decision making have changed in organiza-
tions after implementing a Business Intelligence system.
Purpose
The purpose of this thesis is to investigate if and how Business Intelligence has changed
decision support and decision making processes.
Method
A deductive approach using a qualitative method has been used with semi-structured elite
interviews. The thesis aims to investigate the manufacturing industry located in the
Jönköping region in Sweden. The interviewed organizations are Husqvarna AB,
Fläkt Woods AB, Myresjöhus AB, and Kinnarps AB.
Conclusions
Our analysis shows positive effects of Business Intelligence in organizations with im-
provements of decision support due to timeliness, accessibility, quality, and better control
of organizational information. As improvements in decision support has occurred, decision
making has become better. Complicated problems are now easier to interpret by decision
makers. Our research also concludes that intuition still has a major impact in decision mak-
ing processes.
i
Table of Contents
1 Introduction............................................................................... 1
1.1 Background ............................................................................................1
1.2 Problem Discussion................................................................................2
1.3 Purpose..................................................................................................2
1.4 Problem Delimitation ..............................................................................2
1.5 Interested Parties ...................................................................................2
1.6 Positioning..............................................................................................3
1.7 Definitions...............................................................................................3
2 Method....................................................................................... 4
2.1 Categorization of Knowledge..................................................................4
2.2 Method Approach...................................................................................5
2.2.1 Qualitative and Quantitative Research ...................................................5
2.3 Data Collection.......................................................................................6
2.3.1 Literature ................................................................................................6
2.3.2 Personal Interviews ................................................................................7
2.3.2.1 Outlining of Interview Questions.....................................................................................8
2.3.3 Interpretation of the Empirical Findings ..................................................8
2.4 Selection of Respondents.......................................................................9
2.5 Method Validation...................................................................................9
3 Frame of Reference ................................................................ 11
3.1 Business Intelligence............................................................................12
3.1.1 Data Warehousing Environment...........................................................13
3.1.2 Analytical Environment .........................................................................14
3.1.2.1 Decision Support...........................................................................................................14
3.2 Decision Making Processes .................................................................15
3.2.1 Managerial Activity ...............................................................................16
3.2.2 Management Information Systems.......................................................16
3.2.3 Intuition.................................................................................................18
4 Empirical Findings.................................................................. 20
4.1 Husqvarna AB ......................................................................................20
4.2 Fläkt Woods AB....................................................................................21
4.3 Myresjöhus AB .....................................................................................22
4.4 Kinnarps AB .........................................................................................23
4.5 Summary of Empirical Findings............................................................25
5 Analysis................................................................................... 26
5.1 Decision Support ..................................................................................26
5.2 Decision Making Processes .................................................................28
6 Conclusions ............................................................................ 30
7 Final Discussion ..................................................................... 31
7.1 Reflections............................................................................................31
7.2 Suggestions for Further Studies...........................................................31
7.3 Acknowledgements ..............................................................................32
References ................................................................................... 33
ii
Figures
Figure 2.1 - The inductive versus the deductive approach..................................5
Figure 3.1 - The connection between theories in the frame of reference ..........11
Figure 3.2 - The BI environment (Eckerson, 2003) ...........................................13
Figure 3.3 – The decision making model (Simon, 1960) ...................................16
Figure 3.4 - A Framework for Management Information Systems (Gorry & Scott
Morton, 1989) .......................................................................................18
Figure 5.1 - Analysis model...............................................................................26
Appendices
Appendix 1 – Interview Questions, English.......................................................35
Appendix 2 – Interview Questions, Swedish .....................................................36
1
1 Introduction
In this chapter we introduce the reader to the phenomena of decisions and parts of the historical evolution of
computerized support for decision making. We describe and problematize the phenomena of Business Intel-
ligence and thereafter state our research questions and present the purpose of this study. We also delimit the
problem, determine major stakeholders, briefly describe some other studies concerning Business Intelligence
and some fundamental concepts.
The field of Business Intelligence (BI) has become a popular area in recent years as a new
approach to gather and analyze data for business use. We believe there is a lack of research
conducted within this field. Since we have an interest in the area of BI and the fact that the
phenomena is fairly new, we believe we have a good opportunity to contribute with new
knowledge within this topic. Werner (2007) argues that BI is one of the most popular areas
for IT management to invest in, which makes BI an even more interesting topic.
1.1 Background
Humans have always been forced to make various decisions in different situations. Deci-
sions can be very simple, without major impact and consequences, or be of a very complex
nature, with a huge impact on millions of people. In organizations, managers have always
been making decisions concerning operational, managerial, and strategic matters and these
decisions can have an impact on the organizations’ stakeholders, ranging from the employ-
ees to the government. Managers often try to predict and understand the outcome of dif-
ferent decisions they make. This could be extremely difficult because speculating in a future
context is always a daunting task. However, a successful prediction can be very helpful in
order to select the right decision to achieve the desired outcome or best alternative.
Already in the 1950s, organizations realized the potential of computers and they began to
play an important role. Between the mid 1950s and the early 1970s, the use of computers
and information systems (IS) in organizations grew tremendously but few of these systems
had an impact on mangers’ decision making. In the 1970s, new systems were developed to
support managers in accessing relevant business information needed for decision making
(Gorry & Scott Morton, 1989). These systems provided business managers with static, two
dimensional reports without any analytical capabilities (Turban, Aronson, Liang & Sharda,
2007b). In the 1980s, the systems evolved and started moving away from static reports. In-
stead, focus was shifted to monitoring the organizations’ progress and performance to-
wards critical goals (Burkan, 1991). Organizations did not only need information systems to
support their ongoing operations, they needed systems that could assist managers with val-
ue added information in their decision making processes (Fernandez, Labib, Walmsley &
Petty, 2003). McNurlin and Sprague (2004) describe decision support systems (DSS) as a
management information system (MIS) in conjunction with analytics. To be able to cope
with the huge information flow, DSS have moved towards an attempt to create an intelli-
gent DSS. This attempt provides a great promise to improve both individual performance
and organizational performance and is designed to support, not replace human decision
makers (Dalal & Yadav, 1992). The Gartner Group introduced the term BI in the mid-
1990s but the concept has evolved from MIS, used to support reporting in the 1970s (Tur-
ban et al., 2007b). Today, the term BI is widely used when discussing support in organiza-
tional decision making, instead of traditional terms like MIS and DSS.
2
1.2 Problem Discussion
Today, the organizations’ business environment is becoming more complex and changes
faster than ever before. This generates pressures on the organizations and forces them to
respond quickly. To be able to respond to these pressures in an appropriate manner, or-
ganizations have to make quick, operational, managerial, and strategic decisions, which can
be of a very complex nature and may require timely and relevant information, data, and
knowledge (Turban, Sharda, Aronson & King, 2007a). To be able to improve processes
and create additional business value, organizations have started to realize the value of exist-
ing information. As a result of the increasing demand, the number of BI vendors and tools
has increased substantially in the past years. Using analytical tools and data warehousing, BI
is extracting and analyzing relevant information and making it accessible to the right mem-
ber as a support in decision making processes. In this process, data is gathered from differ-
ent systems, which leads to large amounts of organizational data. To support decision mak-
ers in their decision making process to make more informed decisions this data needs to be
analyzed, distributed, and accessed by the right person, at the right time (Turban et al.,
2007a). The number of implementations and organizations using BI has increased during
the last years (Miller & Reinke, 2007). This might imply they have become more aware of
the usefulness of business information. Turban et al. (2007a) argues that providing updated
and accurate business information, which we address as decision support throughout this
study, is more a question of survival or bankruptcy for an organization than profit or loss.
BI’s major benefit is the ability to access relevant decision support when it is needed.
Based on this, it would be interesting to see how decision support and decision making
have changed in organizations after implementing a BI system.
• How has decision support changed after a BI implementation?
• How has BI changed the way organizations make decisions?
1.3 Purpose
We will investigate if and how BI has changed decision support and decision making proc-
esses.
1.4 Problem Delimitation
To be able to generalize our study in one business sector, we will focus on one industry;
manufacturing. According to Werner (2007) manufacturing organizations are currently one of
the most frequent investors in BI. The region of Jönköping has a relatively high number of
manufacturing organizations (SCB, 2005), therefore this region is appropriate for this
study. This study will only focus on two aspects, decision support and how it is considered
to have changed after a BI implementation. The second aspect is how decision making
processes have changed after implementing BI. We do not intend to include anything re-
garding technical or human-computer interaction (HCI) aspects of BI.
1.5 Interested Parties
Organizations with an interest in implementing a BI system could gain understanding of
how decision support and decision making have changed after BI implementations. Or-
ganizations that create and market BI systems may have an interest in seeing what the user
experiences are and how the systems have affected decision support and decision making.
3
In the academic world, researchers and students with an interest in BI might find this thesis
interesting when searching for new areas of research. Organizational managers could see
how decision support has changed and therefore develop an understanding of BI and how
it could affect their organization in the daily work and long-term planning.
1.6 Positioning
BI is currently a discussed topic in the Swedish business world (Werner, 2007). Exido
(2007), a Swedish research analyst company, predicts a 14 percent increase in BI invest-
ments in Sweden for 2007. The global BI market is increasing at a similar rate. Gartner
(2007) predicts the BI market in Europe, Middle East, and Africa (EMEA) to increase with
10 percent in 2007. Despite its popularity, Swedish research in this field is lacking. Our
study will therefore be based mostly on American literature. We do not believe there are
any major differences between BI systems in the US and Sweden and we therefore consider
our choice of literature appropriate. However, we have found a few studies conducted in
Sweden. Karlberg and Karlsson (2006) discuss BI from two perspectives, the concept and
the phenomenon. Similar, Biberic and Hodell (2007) evaluate BI from a rational decision
making model to understand how companies use BI. Olsson (2007) discusses the history
behind BI and describes current and future BI trends from a business perspective. Dagman
and Wigsten (2007) identify causes that constrain organizations to efficiently utilize a BI
process and they discuss how to create higher efficiency in a BI process.
1.7 Definitions
Business Intelligence – Using data warehousing and analytical tools, BI is extracting and
analyzing relevant information and making it accessible to the right member as a support in
decision making processes.
Data Warehouse – Central storage of data to support decision making in organizations.
Decision Support – BI generated information supporting decision makers in decision
making processes.
Decision Support Systems – Systems used to directly support specific decision making
processes.
ETL – Extract, Transform and Load is the process of extracting data from different
sources, converting it into an appropriate format and loading the data into a data ware-
house.
Key Performance Indicator – A financial or non-financial measure of how well an or-
ganization is performing.
Management Information Systems – Information systems used to analyze and solve
business problems.
4
2 Method
The data necessary to perform this study will be gathered through qualitative methods. The empirical find-
ings will be collected from primary sources through in-depth interviews with selected organizations. Data
from secondary sources will be collected from literature. The choice of methodology for this study will be pre-
sented and argued for below.
Goldkuhl (1998) argues a researcher who wants to develop knowledge must plan and de-
sign the process of acquiring knowledge. This process can be divided into two phases. The
first phase is planning and defining and the second phase is realization of the planned activities.
Development of knowledge starts when a researcher is interested in a phenomenon and
wants to know more about this specific area. Based on this interest, preliminary research
questions are formulated. The researcher thereafter tries to articulate pre-knowledge about
the phenomena. This pre-knowledge in conjunction with other knowledge in the area helps
the researcher to reformulate and determine the research questions. Thereafter, the re-
searcher determines the demanded knowledge - what knowledge to develop. The de-
manded knowledge is the determinant of what strategy and what method to use to conduct
the study (Goldkuhl, 1998). The major objectives in this research are to understand how
decision support and decision making processes have changed after implementing a BI so-
lution in an organization. Therefore, the research group needs to understand current litera-
ture about human and computer in conjunction in decision making processes. Our research
questions are based on discussions in order to fulfill the purpose and give appropriate guid-
ance of the thesis. The research questions have been affected, determined, and adjusted
based on literature the group has studied.
2.1 Categorization of Knowledge
Determining what type of knowledge to develop is referred to as categorization of knowledge.
Goldkuhl (1998) argues a researcher needs to determine what kind of knowledge to de-
velop. This is a process to establish the value of the developed knowledge and to determine
the appropriate strategy for the study. Goldkuhl (1998) presents different types of knowl-
edge which are appropriate in different research situations. Deciding on a strategy is neces-
sary to determine what type of knowledge to create and the chosen strategy should follow
the categorization of knowledge (Goldkuhl, 1998). In this study explanatory knowledge will
be created using an explorative strategy.
• Explanatory knowledge is an approach where the researcher explains why a phenome-
non is in a certain manner and mention causes, foundations, reasons or prerequisites
for the resulting relations. Often, explanatory knowledge means to test hypothesis
but there are other cases as well. One approach within explanatory knowledge is to
study effects which originate from a certain event (Goldkuhl, 1998).
• Explorative strategy is a research strategy where the researcher does not test hypothesis
but is an attempt to get better knowledge within a specific area. This approach is like-
ly to generate hypothesis (Goldkuhl, 1998).
To conduct this study in a suitable manner, the most appropriate type of knowledge is ex-
planatory knowledge. One of the approaches within this type of knowledge is to study effects
which originate from a certain event (Goldkuhl, 1998), in this case a BI implementation.
The explorative strategy aims at generating knowledge within a specific area and since existing
literature does not cover the intended research objectives we have to use the explorative
strategy.
5
2.2 Method Approach
Holme and Solvang (1997) argue that explaining a phenomenon or a situation using theo-
ries is not always easy. Even though it is a complex task, it is necessary in order to create
understanding and develop new theories. There are two approaches researchers use; induc-
tive and deductive (Holme & Solvang, 1997). The two approaches are illustrated in figure 2.1.
Using an inductive approach, the researcher collects empirical material. The empirical data
is analyzed and generalized and new theories are generated from the generalizations. The
deductive approach is more formalized. Unlike the inductive approach, it starts in theory
where the researcher derives a testable hypothesis or a theoretical proposition. Through
analysis of the collected empirical data, the hypothesis is accepted or rejected (Bailey, 1996).
Figure 2.1 - The inductive versus the deductive approach
The thesis work will start with a comprehensive literature study, to familiarize ourselves
with the concept of BI, systems for decision support, and areas related to decision making
processes. From the findings in the literature study, we will develop our research questions
and define the purpose of this thesis. The next step in our research process will be to gath-
er empirical material through interviews with manufacturing organizations using questions
related to existing theories. An analysis of the findings using these theories will follow and
the results aim to provide answers to our research questions. Based on this description of
planned activities, we argue that we follow the deductive approach.
2.2.1 Qualitative and Quantitative Research
The reality of today’s society is of complex nature and it is impossible to seize the reality of
this environment using only one method. There are two different categories of methods,
qualitative and quantitative methods. Both methods have their strengths and weaknesses and
the selection of which method to use should have its starting point in the purpose. The
primary focus of qualitative studies is explanatory and implies less standardization. Creating
a deep understanding of the study objects is one important feature of this method. The
other important aspect is to describe the whole of the context. One characteristic of this
method is to be close to the studied objects. Quantitative methods are characterized by a
higher degree of standardization and they imply higher control for the researcher but also
pre-determine possible answers. The planning and selection in quantitative methods is cha-
racterized by distance to the research objects (Holme & Solvang, 1997).
The qualitative approach generates an overall picture of social processes and contexts. Be-
cause of time restrictions and to be able to get an overview of the information, a limited
6
number of research objects are often selected. The qualitative approach also generates an
understanding of the total situation. The strengths of quantitative methods are their ability
to result in generalizable outcomes. The outcomes can lead to opportunities for the re-
searcher to make statistical statements. There are some characteristics associated with these
methods as well. Qualitative methods are flexible and the questions can be revised during
the meeting with an object and can be changed during the study. This is both a strength
and a weakness, since it can be difficult to compare the outcomes of different interviews.
Quantitative methods on the other hand are fixed and cannot be changed during the study,
it must be standardized. This gives the researcher control and the ability to generalize
(Holme & Solvang, 1997).
To gain deeper understanding in how decision support and decision making have changed
after an implementation of BI, a close interaction with the study objects is needed. This in-
teraction will take place during personal interviews with a low degree of standardization.
The objective is to get an overall picture of BI usage in manufacturing organizations, both
how it is utilized and what that impacts are on decision making. Based on this, the qualita-
tive approach was found to be the most appropriate for this study since we will focus on
qualitative changes which have occurred after a BI implementation.
2.3 Data Collection
There are two different types of data, primary and secondary data. Primary data is informa-
tion gather by the researcher using a certain method. Holme and Solvang (1997) argue that
primary data is gathered when the researcher is close to the study objects and the inter-
viewed object has experienced the situation itself. Secondary data is information gathered
by other researchers in earlier studies (Lundahl & Skärvad, 1999; Holme & Solvang, 1997).
To gather information using qualitative methods four approaches are typically used. First,
the researcher can participate in a setting. Secondly, a researcher can conduct a direct ob-
servation. The third and fourth approaches to gather information are personal interviews
and analyzing documents and material culture (Marshall & Rossman, 1999). In this study
we are going to use literature studies and conduct personal interviews.
2.3.1 Literature
When a researcher describes the context and history of a phenomenon, an important part
of research work consists of reviewing documents. This approach can be seen as supple-
mentary to other qualitative approaches to gather information. Research journals concern-
ing the topic are one example if they are relevant for the study. One obvious drawback us-
ing literature in a study is the need for interpretation by the researcher (Marshall & Ross-
man, 1999). The sources used in this study have all been processed in a way suggested by
Holme and Solvang (1997). They describe four steps to analyze a source; observation,
source, interpretation and usefulness. In the first step, observation, the researcher tries to
find sources which enlightens the questions determined in the problem discussion and also
tries to get an overview of relevant existing literature. In the second phase, source, is im-
portant to determine who the author is to establish the source’s trustworthiness. The third
step, interpretation, deals with the problems of interpreting what the source describes and
try to analyze the information. In the last step, usefulness, the researcher determines how
useful the source is to fulfill the purpose (Holme & Solvang, 1997).
Since we will use a deductive approach in this study and have the starting point in literature
it is of great importance to asses the sources used. The observation step was conducted
7
early in the study and the reliability of sources used are high since most of the literature
used within the report are written by well-known writers or published in well-known re-
search journals. The third and fourth steps are more problematic since we need to interpret
what the author meant and determine how useful the information is. Hence, we believe to
have understood the context and meaning of the literature and that it is applicable to the
study. To fulfill our purpose we intend to use literature regarding BI, decision support, and
decision making.
2.3.2 Personal Interviews
Interviews have, like most methods, strengths and weaknesses. Interviews generate large
amounts of data quickly and the ability to follow up the result immediately. An interviewer
needs participation and cooperation from the interview object, and a problem may be the
interview object’s unwillingness of answering all questions. The interviewer can also have
problems when it comes to understanding a certain behavior because of differences in cul-
tures, languages or the interviewer’s lack of skill. The interview object can also have reasons
for not being truthful (Marshall & Rossman, 1999).
One common approach to distinguish between interviews is the degree of standardization.
If the questions have a high degree of standardization the questions and the order of the
questions is the same for all interviews in the study. If the interview is unstandardized, the
outlining of the questions is different from interview to interview but as long as the need
for information is covered, this type of interview can be a good alternative. Many inter-
views are not either of these types, which can be seen as extremes, but somewhere in be-
tween these interviews types. Lundahl and Skärvad (1999) argue these interviews can be
addressed as semi-standardized interviews. In semi-standardized interviews, the interviewer
asks certain questions to all respondents and then asks attendant questions to get a deeper
understanding. Unstandardized interviews are appropriate when the researcher conduct qu-
alitative studies where the researcher wants to develop an understanding of a respondent’s
situation or its motives and ideas about a certain phenomena. Standardized interviews are
appropriate when the researcher needs more quantitative data (Lundahl & Skärvad, 1999).
We will use semi-standardized interviews since we want to cover certain question areas.
However, we do not want to be tied to asking questions in a certain order and we want to
be able to ask attendant questions to get a deeper understanding of BI usage.
In this report we will use what Marshall and Rossman (1999) addresses as elite interviewing.
This type of interviewing aims at interviewing individuals with a certain amount of influ-
ence in an organization. The interview objects are selected based upon their knowledge or
expertise in a certain area. Elite interviewing gives the researcher opportunity to gain valu-
able information because of the interview objects’ position in the organization. They can
also give an overview of internal and external relations as well as legal and financial aspects.
There are problems with this type of approach. Elites can be hard to interview since they
have limited time and the interviewer may be forced to adapt the interview based on the el-
ites’ wishes. To conduct a successful elite interview puts pressure on the interviewer to de-
velop competence in related topics. However, if the interviewer is well-prepared, this type
of interview generates quality information (Marshall & Rossman, 1999).
Using a tape recorder is desired to minimize the amount of notes and direct full attention
to the respondent (Ritchie & Lewis, 2003). We intend to use a tape recorder during the in-
terview since we do not want to miss important information. Bailey (1996) argues the re-
searcher needs to explicitly clarify the use of tape recorder for the interview object and ask
for its permission. One of the reasons to conduct a face-to-face interview is the possibility
8
to observe the body language of the respondent. Bailey (1996) claims this is very important.
To be able to cope with the difficulties with elite interviewing we have studied a large
amount of literature related to BI to be prepared for the interview situation. Time is an im-
portant issue and we intend to contact our interview objects early in the research process.
2.3.2.1 Outlining of Interview Questions
Lundahl and Skärvad (1999) argue that personal interviews build on confidence between
the respondent and the researcher. Further, interviews should start with questions of less
controversial nature. Holme and Solvang (1997) also presents this view on interviews but
they also argue that the interview should end with unproblematic questions because this
gives the respondent and the researcher time to neutralize tensions which may have oc-
curred during the more controversial part of the interview (Holme & Solvang, 1997).
The outlining of the questions has been influenced by the authors mentioned above. The
starting questions concern the respondent’s background within the organization and what
position they have as well as questions regarding the organization. These questions are gen-
eral and easy to answer. This will be done to build a confidence between respondent and
interviewer and the fact that position can influence the answers. After these opening ques-
tions, the interviewer will move on to BI and the questions asked will be based on the
theoretical framework. The final questions will be of less controversial nature and we have
decided to let the respondent speculate in the future of BI. When the interviews take place,
the interviewers will always ask the questions like how and why. This approach was chosen
to get a deeper understanding of each organization’s specific view of their own BI system
and to get a clearer picture of the respondents’ thoughts about their BI system. We also be-
lieve when how and why questions are asked, respondents will give more comprehensive and
detailed answers and richer descriptions of each question.
Before the interviews are conducted, we will send the questions to the respondents. This
will be done so that the they can familiarize themselves with our research and prepare an-
swers to our questions. There are drawbacks using this approach, such as the fact that re-
spondents may prepare the answers they think we want to receive and we might not get
spontaneous reactions to our questions (Thomas, 2004). We believe the answers will be
more valid and more complete if the respondents get the opportunity to prepare.
2.3.3 Interpretation of the Empirical Findings
According to Fischer (2007), researchers often discover two contradicting problems when
analyzing research material. The first problem is the law of the missing middle. Researchers
who start writing their results right after collecting empirical material often miss out the in-
termediate stages of sorting and sifting and miss the line of argument. The second problem
is the dilemma of drafting. Researchers never really understand their empirical material until
they write it up. It is the process of writing that forces researchers to really understand the
material. This implies an iterative process of sorting and sifting followed by writing (Fisch-
er, 2007; Starrin & Svensson, 1994). These interpretations and understandings are based on
knowledge and experience but also imagination and creativity. According to Starrin and
Svensson (1994) this is legitimate when a researcher tries to interpret interview material.
To be able to cope with the huge amount of data we will gather during the interviews we
have decided to only present and analyze the material which directly relates to the research
questions and the theories presented below. After each interview we will listen to the re-
corded material once and then listen a second time to get a deeper understanding of the
9
context and the respondents’ answers. During the second listening notes will be taken. The
recording will be stopped and rewind several times to assure nothing important is left out.
This to assure that the presented empirical material is correct and presented in the right
context. To be able to compile the empirical findings in a sufficient text, where no impor-
tant information is left out, we will take notes and divide the answers between our research
questions. We have also chosen to present important quotes from the conducted inter-
views. We believe this approach will be helpful since we do not want to have too long texts
with information not related to our research questions. The information presented in the
empirical findings will not follow a chronological order, since we think that the interview
questions will probably not always follow the intended outline. This is due to the fact that
the respondents may answer questions before they are asked or the respondents might an-
swer questions similar to other questions and therefore it will not be necessary to present
these answers twice.
2.4 Selection of Respondents
Statistical generalizations are not central purposes within qualitative studies but nonethe-
less, the selection of respondents is an important process in the study. Since the purpose of
qualitative studies is to create a deeper understanding of a phenomenon, the selection
process is not random. The selected respondents must fulfill certain criteria which are theo-
retically or strategically determined (Holme & Solvang, 1997). We will base our selection of
respondents on convenience sampling. There are drawbacks with this type of sampling. For
example, Holme and Solvang (1997) claim conclusions from research based on conven-
ience sample may be misleading. We argue we have good reasons for using convenience
sampling since both the distances to the possible respondents and the time constraints will
not allow us to conduct personal interviews to the same extent if we randomly select or-
ganizations from the entire population. The benefits of using convenience sampling exceed
the drawbacks and we therefore argue that this approach is appropriate in this study.
To select respondents, the Affärsdata database will be used to search for manufacturing or-
ganizations. To limit the number of hits and only show manufacturing organizations, our
searches will be filtered using Svensk Näringsgrensindelning (SNI) codes, which follow the
General Name for Economic Activities in the European Union (NACE) standard. The appropriate
SNI codes will be found using the search criteria “Manufacturing”. We are only interested
in manufacturing organizations in the region of Jönköping and therefore the results will be
filtered by using Jönköping’s län and Västra Götaland’s län. Furthermore, we are only in-
terested in organizations with more than 300 employees since we believe these organiza-
tions have been working with BI for a longer time. We also intend to only interview or-
ganizations with more than one year experience of using BI. We believe those organiza-
tions have matured in their usage and therefore contribute with more interesting aspects of
how the organization has changed after the implementation. To be able to generate suffi-
cient empirical findings we will interview between four and seven organizations.
2.5 Method Validation
A study should, to be trustworthy, have a high degree reliability and validity. Those two as-
pects have a close connection. Reliability is determined by how accurate the researcher has
processed the information. Is the information the researcher presents reliable? Often there
is no problem to determine in qualitative studies since the respondent can participate and
give its opinion on how the researcher has interpreted the things the respondent described
during the interview. Validity is dependent on what we will research and measure and if
10
that is clear in the purpose of the report. To gain valid information is easier in qualitative
studies than in quantitative studies because the researcher is closer to the respondents and
the respondent gets the opportunity to control its participation. One problem to get valid
information is when the researcher does not understand what the respondent describes. It
can also be hard for the researcher to determine whether to be active or passive during the
interview. In different interviews it can differ in how valid information the researcher gains
depending on being active or passive. Another problem can be the relationship between re-
searcher and respondent. The respondent believes it should act in a certain way (Holme &
Solvang, 1997). To be able to generalize a study is very important since the study should
represent the reality and the result should be applicable to the entire population (Lundahl
& Skärvad, 1999).
To deal with the problems regarding reliability we will assure we are well-prepared before
conducting the interviews. Both the concept of BI and interview techniques will be studied.
We believe when we know the concept of BI well, and the respondents realize this, they
will give better and more advanced answers to our questions. After the interviews, we will
compile the interview material and send it via email to the respondents to assure that the
empirical material is correct and interpreted in the right context. We also believe this will
solve any translation mistakes that might have occurred, since the interviews will be con-
ducted in Swedish. Validity is harder to determine but since the interview questions clearly
are adapted to the research questions we believe we have high validity. During the process
of developing the interview questions we will have the difficulties concerning validity in
mind and try hard to adapt the interview questions to fit the purpose of this study. Also,
the selection of respondents will be carefully conducted. We will only interview respon-
dents with extensive knowledge and insight in how BI works to increase validity. To be
able to generalize our study we intend to conduct interviews until we have reached the point
of sufficient knowledge which means very few new statements and thoughts regarding BI
are discovered during the interviews. We will not be able to generalize our study for all in-
dustries in Sweden, but we believe generalization of the results from this study will be pos-
sible in one business sector; the Swedish manufacturing industry. Working with reliability
and validity during the entire research process we believe will increase generalization of this
thesis.
11
3 Frame of Reference
In this section we present some relevant theories for our study concerning Business Intelligence. We will also
focus on describing what decision support is and different levels and aspects of decision making.
The connections between the theories in the frame of reference are shown in figure 3.1 and
aims to clarify different parts in this chapter. It also illustrates different elements and the re-
lationship between them. Gorry and Scott Morton’s (1989) well-known framework for
management information systems (MIS) , which is widely present in the literature, serves as
the core base. Elements that are included to support this framework are; Anthony’s (1965)
description of managerial activities and Simon’s (1960) decision making model and deci-
sions.These parts are explained more in depth for a better understanding of the framework.
Eckerson’s (2003) BI environment is used to give an understanding of BI and its compo-
nents. It is divided into two parts, one technical side and one analytical side. The data wa-
rehousing environment contains data compiling from different source systems (also re-
ferred to as ETL), information quality, and a data warehouse (DW). On the other side, the
analytical environment describes decision support which is the outcome of BI systems
(Eckerson, 2003). Intuition has also been identified because it has a great impact in deci-
sion making processes (Hayashi, 2001). The final decision, made by a decision maker, is
shown on the right side of the model.
BI is applied on top of Gorry and Scott Morton’s (1989) framework. By doing this, we will
analyze if BI has contributed to changes in decision support. We will also examine if BI has
had any impact on organizational decision making from an operational, managerial, and
strategic point of view.
Figure 3.1 - The connection between theories in the frame of reference
12
3.1 Business Intelligence
Today, BI is used as an umbrella term for describing computerized decision support sys-
tems. However, BI evolved from DSS, a concept researchers started working on in the
1960s as computerized systems to assist in decision making and planning. As the develop-
ment of new types of DSS continued, the scope of the concept expanded and branched in-
to several different categories (Power, 2007). Power (2007) states that data-driven DSS in-
volve access and manipulation of organizational, sometimes external and real-time, data. It
could be simple files on a local machine or more advanced systems with additional func-
tionality, such as a DW. These systems generally enable analytical functionality and analysis
of historical data for support in decision making processes. “In general, business intelligence sys-
tems are data-driven DSS” (Power, 2007). The Gartner Group introduced the term BI in the
mid-1990s (Turban et al., 2007b). However, Watson (2005) states that BI is the result of a
continuous evolution. “Just because it has a new name doesn’t mean it is necessary new” (Watson,
2005, p. 4). Davenport and Harris (2007) conclude the entire field of systems for decision
support is referred to as BI.
However, Turban et al. (2007b) argue that BI evolved from DSS and their architectures
have some similarities, but there are some differences between BI and DSS. First, BI is
built with a DW and DSS do not need to include such a feature. BI is therefore better built
to support larger organizations but DSS can support any organization. Second, BI is de-
signed to support decision makers with timely and accurate information but indirectly while
DSS is designed to directly support specific decision making. Third, BI was developed by
software companies and DSS methodologies were mainly developed in the academic world.
Fourth, BI is constructed to fit the organization and is constructed with commercially
available tools while DSS targets unstructured problems and lots of programming is needed
to support those complex problems. Fifth, BI focuses more on executive and strategic
problems and DSS is constructed to support analysts. However, they are similar and con-
sist of similar features such as data mining and predictive analysis. BI software has been
changing and more decision support tools are built into the system. At present, BI and DSS
are not the same but they have a close connection (Turban et al., 2007b). Based on the dis-
cussion above, we agree with Turban et al. (2007b) and consider BI and DSS similar but
not completely the same.
The definition of BI is also heavily discussed in the literature. We have found four different
definitions of BI. Eckerson (2003, p. 1) defines BI as “BI solutions create learning organizations
by enabling companies to follow a virtuous cycle of collecting and analyzing information, devising and acting
on plans, and reviewing and refining the results. To support this cycle and gain the insights BI delivers, or-
ganizations need to implement a BI system comprised of data warehousing and analytical environments”. In
this definition Eckerson (2003) emphasizes collecting and analyzing data as well as using BI
in an organization-wide setting. Eckerson (2003) has the only definition that includes data
warehousing as the source for the data to be analyzed, which he claims to be crucial to gain
the insights BI delivers. Bräutigam, Gerlach, and Miller (2006, p. 2) uses a similar definition
but leave out data warehousing, “Business Intelligence is defined as getting the right information to the
right people at the right time. The term encompasses all the capabilities required to turn data into intelli-
gence that everyone in your organization can trust and use for more effective decision making”. Bräutigam
et al. (2003) put emphasis on turning data into intelligence for all users in the organization
to assist in more effective decision making. The third definition is from Turban et al.
(2007a, p. 9), “BI’s major objective is to enable interactive access to data, enable manipulation of these da-
ta, and to provide business managers and analysts the ability to conduct appropriate analysis”, which has
a lot in common with Eckerson (2003) and Bräutigam et al. (2003). Turban et al. (2007a)
discusses both the analysis of data for different users, on different organizational levels and
13
the use of the output. Loshin (2003, p. 6) uses a somewhat different definition, “The proc-
esses, technologies, and tools needed to turn data into information, information into knowledge, and knowl-
edge into plans that drive profitable business action. Business Intelligence encompasses data warehousing,
business analytic tools, and content/knowledge management”, leaving out the organizational aspect.
Based on the characteristics of these definitions we have developed our own definition of
BI:
Using data warehousing and analytical tools, BI is extracting and analyzing relevant information and
making it accessible to the right member as a support in decision making processes.A BI systems in-
volves four different components: A DW, which contains business data extracted from dif-
ferent sources in the organization; business analytics (BA), a collection of methods used to
perform mining, manipulation, and data analysis; business performance management (BPM)
which involves business monitoring and performance analysis; and finally user interfaces e.g.,
dashboards and reports (Turban et al., 2007b). Another dimension of the BI environment
proposed by Eckerson (2003) illustrated in figure 3.2 show data sources that feed the BI
system with appropriate data.
Figure 3.2 - The BI environment (Eckerson, 2003)
3.1.1 Data Warehousing Environment
The data warehousing environment shown in figure 3.2 in the left circle describes the BI
environment from a technical point of view. Technical aspects such as retrieving informa-
tion from different sources, the extract, transform, and load (ETL) process, and a DW
(Eckerson, 2003). There are different sources that are used to retrieve data in order to feed
the DW. Most commonly, data is sourced from multiple independent operational systems
but also from external providers. It can also be retrieved from online transaction process-
ing (OLTP) or an enterprise resource planning system (ERP). In some cases, even web data
in the form of web logs is used to provide a DW with data (Turban et al., 2007a).
As a critical component in any BI, information quality plays a superior and important role.
English (1999) asserts that there are two significant definitions of information quality, inher-
ent and pragmatic. Inherent information quality is data accuracy and to what degree data
represents the real-world objects. In contrast, pragmatic information quality is the useful-
ness and value of data used to support processes and enable organizations to accomplish
their objectives. Based on the two perspectives, English (1999) defines information quality
and describes the phenomena as “consistently meeting knowledge worker and end-customer expecta-
tions” (English, 1999, p. 10). In order to understand what information quality is, English
(1999) asserts it is important to understand the concepts of data, information, knowledge
14
and wisdom. Data is basically raw material from which information is derived and it can be
viewed as entities, attributes or facts. Information is when one knows the meaning of data
and it becomes understandable. However, information quality in itself is useless, but under-
stood, it leads to value for users. English (1999) describes that knowledge is when informa-
tion becomes assimilated and has a meaning in a certain context. Wisdom is applied knowl-
edge and an understanding how the knowledge can be applied in different settings (Eng-
lish, 1999).
The center of Eckerson’s (2003) data warehousing environment consists of ETL processes,
used to prepare the DW with useful and appropriate data. In ETL processes, Extraction
concerns gathering information from different databases and systems. Transformation is the
process where the extracted data are converted into a form which can be placed into a DW
or another database. Load concerns putting the data into a DW (Turban et al., 2007b). The
core of BI is ETL since the process brings together and combines data from multiple
source systems into a DW. This approach enables all users to work with a single set of data
referred to as “a single version of the truth” (Eckerson & White, 2003, p. 4). If the ETL process
is well-designed and executed, the organizational information is stored in one central loca-
tion and organizational members do not need to argue whether the information is correct
(Eckerson & White, 2003). Therefore, organizations can use the information to improve
key processes and as a competitive weapon. However, any issues regarding data quality
need to corrected before loading the information into the DW. Poorly designed ETL proc-
esses are difficult and expensive to maintain, change, and update (Turban et al., 2007b).
The ETL process must be carefully planned and highlight that this is a critical and impor-
tant part since the ETL design and development work consumes approximately 70% per-
cent of the time spend in a BI project (Eckerson & White, 2003; Turban et al., 2007b).
In the center of Eckerson’s (2003) BI environment, a DW is placed in order to store data
for decision support. The DW can appear as a number of smaller data marts consisting of
data for a single subject or area. Another repository for data is an operational data store
(ODS) with constantly updated data that is aggregated from business operations. One of
the most common repositories used is an enterprise data warehouse (EDW) which is a
large scale DW that is used for decision support across the entire organization. The main
purpose of a DW is providing consistent, integrated, nonvolatile collection of data in sup-
porting managers in decision making processes (Turban et al., 2007b).
3.1.2 Analytical Environment
The right side in Eckerson’s (2003) proposed BI environment refers to business user activi-
ties with a non-technical approach. The DW is used to support decision-makers with in-
formation in different variances depending on types of area and use. Business users can vi-
sualize, query, report, mine, analyze, and most importantly act on data in the DW. In some
cases, more advanced ad hoc (on-demand) exploration of the DW can be performed by
business users (Eckerson, 2003). Business analytics can be based on basic exploration of
data or more advanced analytical environments for qualified users (Turban et al., 2007a).
3.1.2.1 Decision Support
Visualization is used to make data more understandable and clear to end users. Decision
makers can browse the interface and analyze data in real time and examine organizational
performance data (Eckerson, 2003). Spreadsheets are one of the most common end-user
tools and it is often supported by three-dimensional visualization tools. Dashboards and
scorecards are two important components in business performance management (BPM)
15
systems, also called corporate performance management (CPM) systems. Both concepts
have a broader context in which organizational strategy is included and BI is a part of. A
dashboard can consist of key performance indicators (KPI) which are pre-defined meas-
ures critical and important to monitor. Scorecards are used to compare actual results with
planned results, depending on pre-specified measures (Turban et al., 2007a).
Reporting can be divided into two categories, routine reports and ad hoc reports. Routine
or standard reports are generated and distributed to relevant subscribers periodically. Fur-
ther exploration of the data presented allows business users to perform analysis with meth-
ods such as drill-down in order to find problem areas. However, reports can also be per-
formed ad hoc. Meaning that significant, specified, and relevant data can be compiled and
retrieved by business users (Turban et al., 2007a).
In the analytical environment, methods for drill-down, data mining, and queries can be
supportive for decision makers. Drill-down analysis is a process where the level of granu-
larity will result in more detailed data, excluding irrelevant and unnecessary data. Data min-
ing is used where reports and queries are inapplicable. The data mining process is used to
discover patterns that are impossible to interpret by humans, that can be of relevance and
guide decision making. Queries can be performed with structured query language (SQL)
and retrieve specific information determined by business users (Turban et al., 2007a).
3.2 Decision Making Processes
In the wide range of different decision types, Simon (1960) distinguishes two extremes, pro-
gramed decisions and non-programed decisions. The term program is borrowed from, and used in
the same way as in the computer world (Simon, 1960). A program is essentially a list of in-
structions or strategies executed in a certain order (Schneider, 2003). Thus, programmed
decisions are repetitive and routine. They follow definite processes or procedures which are
well-known, defined, and do not have to be treated as new every time they occur (Simon,
1960). Examples of programmed decisions are: sending out invoices; reorder supplies; pay-
ing a vendor. Non-programmed decisions on the other hand are unstructured and unusu-
ally consequential. No best practice for handling the problem exists because the problem
has not occurred before, the problem is of such complex nature, or the problem has such
high importance that it requires additional attention and handling (Simon, 1960). The deci-
sion to move manufacturing abroad or outsource the IT department are typical non-
programmed decisions. Simon (1960) points out how decisions exist in every shade of gray
in this spectrum and programmed and non-programmed are simply the far extremes.
Although the scope and impact of a decision varies greatly from one level in the organiza-
tional to another Simon (1960) argues that some generalization is possible. According to
Simon (1960), decision making includes four distinct phases. The first phase is finding situ-
ations for making a decision, intelligence. The second phase is referred to as design, where dif-
ferent courses of action are investigated. In the third phase, one of the available courses of
action is chosen. Simon (1960) calls this the choice activity. The fourth phase is a review, in
which previous choices are evaluated. These phases are illustrated in figure 3.3 below.
16
Figure 3.3 – The decision making model (Simon, 1960)
3.2.1 Managerial Activity
Anthony (1965) describes managerial activities in two different divisions, planning and con-
trol. The planning involves deciding what direction to take and the control means assuring
that desired outcomes are obtained. Based on these two main divisions Anthony (1965)
suggests three categories of managerial activities that he claims requires different ap-
proaches when developing systems. The first category strategic planning, involves the process
of deciding objectives; changes for the objectives; resources needed for the objectives and
policies that govern acquisition, use, and disposition of resources. Management control is the
process in which managers assure that resources are obtained and used efficiently in order
to achieve the organizational objectives. The third category operational control encompasses
that specific tasks are carried out efficiently. The main difference between management
control and operational control is that management control is concerned with people and
operational control with tasks (Anthony, 1965).
3.2.2 Management Information Systems
Gorry and Scott Morton’s (1989) framework for management information systems (MIS)
suggest that such system is best used from a decision making perspective. MIS work does
not always use a comprehensive approach, excluding organizational aspects that prevent
full appreciation. This generates temporarily solutions and inefficient allocation of re-
sources. The role of information systems in organizations is now focused on how systems
can capture parts in the human decision making process. As a result, organizations have re-
alized the potential of information systems, supporting sophisticated unstructured deci-
sions (Gorry & Scott Morton, 1989).
The framework is based on managerial activities and should not be used to describe infor-
mation systems. It is used only as a way to understand decisions made in organizations, in-
formation systems should only exist to support decisions (Gorry & Scott Morton, 1989).
The framework (figure 3.4) illustrates Anthony’s (1965) and Simon’s (1960) in combination
which provides two perspectives used to examine problems and purposes of information
systems activity.
The left side represents Simon’s (1960) programmable and non-programmable decisions.
Gorry and Scott Morton (1989) call these two categories structured and unstructured deci-
sions. The unstructured decision is where none of the three, intelligence, design or choice
in Simon’s (1960) decision making model (figure 3.4) is structured. The complete struc-
tured decision is where all three parts are structured and assists satisfactory decisions to be
17
made. Gorry and Scott Morton (1989) have added another dimension, semi-structured de-
cisions that encompasses that one or two of the phases are unstructured. The conclusion is
that above the horizontal line, largely structured decisions can be made with MIS. Deci-
sions below the horizontal line are largely unstructured and supported by decision support
systems (DSS) (Gorry & Scott Morton, 1989).
The top of Gorry and Scott Morton’s (1989) framework (figure 3.4) represents Anthony’s
(1965) managerial activities. Strategic planning involves predicting the future of the organi-
zation, consisting of a small group of high-level people in a non-routine environment
which makes it difficult to assess quality in planning processes. Management control in-
volves interpersonal interaction often driven by the strategic planning process. The opera-
tional control category consists of execution of tasks, with less judgment and this is what
distinct it from management control. It is important to be aware of that the distinction be-
tween operational control, management control, and strategic planning is difficult to de-
termine (Gorry & Scott Morton, 1989).
Managerial decisions that are below the line have a greater impact on the organization be-
cause they normally deal with unstructured decisions. Improvements in understanding de-
cisions will move the decision and it can be moved above the middle line and free up man-
agers for other tasks. Structured decisions are as Gorry and Scott Morton (1989) describe
them “organizational independent” which means they are almost the same in every organi-
zation although details may differ. As decisions become semi-structured or unstructured
the absence of routine procedures and lack of formalization between the parts in Simon’s
(1960) decision making model increase. In order to improve quality of decisions it is impor-
tant to have the right information input or change the entire decision process, or both. By
improving quality of information input, quality of managerial decision making may in-
crease. However, it will not have any major enhancements, since they rather need new un-
derstandings or methods, to understand the information that is already available (Gorry &
Scott Morton, 1989).
Gorry and Scott Morton’s (1989) implication of their framework suggest three different
perspectives, system design differences, different organizational structure, and model dif-
ferences considering Anthony’s (1965) managerial categories in mind. The first, system de-
sign differences implies that since operational control and strategic planning have different
objectives it is unnecessary to connect systems across boundaries since they do not have
similar objectives. Secondly, organizational structure involves defining problems and must
be dominated by managers involved in that specific area. Managers must also be analytical
and reflective rather that procedural and communicative. Third, strategic decisions do not
occur frequently and operational occur daily. This prove that models for decisions need to
be different and work efficient, have access to current data, be structured, and be easy to
change (Gorry & Scott Morton, 1989).
It should be mentioned that the activities within Gorry and Scott Morton’s (1989) frame-
work in figure 3.4 are only examples of different activities. The activities do not have any-
thing to do with the framework and organizations can apply its own activities as they wish.
In this study we will use the original activities as examples to provide an understanding on
what the framework could look like, when used in an organization.
18
Figure 3.4 - A Framework for Management Information Systems (Gorry & Scott Morton, 1989)
3.2.3 Intuition
Decision makers are sometimes excluding any form of logical analysis or rely only on in-
formation. Instead, different modes, referred as “intuition”, “inner voice”, “gut instinct”,
or “hunches” are used but decision makers have problems explaining the phenomena more
in detail. Emotions may be essential and supportive in order to filter various options quick-
ly, even if decision makers are not consciously aware of the screening themselves. How-
ever, human nature can easily obscure by observing and recognize patterns where none ex-
ists (Hayashi, 2001). According to Hayashi (2001), humans use patterns to find a pattern
and refers to Herbert A. Simon who proves that experienced people chunk information so
they can retrieve and store it easily. This because experienced and experts have the ability
to “see patterns that elicit from memory the things they know about such situations” (Hayashi 2001, p.
179). Hayashi (2001, p. 179) further explains the phenomena by quoting AOL’s former
president Robert W. Pittman “Staring at data is looking at a jigsaw puzzle. You have to figure out
what the picture is. What does it all mean? It is not just a bunch of data. There is a message in there.
Every time I get another data point I have added another piece to the jigsaw puzzle, and I am closer to see-
ing the answer. And then, one day, the overall picture suddenly comes to me.”
Humans have to review themselves because they have a tendency toward overconfidence.
This implies that humans overestimate their ability in just nearly everything. It is therefore
important to find out if decisions and judgments were accurate, and the use of feedback
19
can be very important. Otherwise, we do not know if we made any mistakes and cannot
learn or do anything about them. Because of this problem, it is very common that decision-
makers use self-checking procedures to ensure that intuitive decisions are successful (Haya-
shi, 2001). Another common method for intuitive decision making is cross-indexing. This
means that information can be combined from different sources. Hayashi (2001) state that
the amount of material used and can be cross-indexed enhances intuitional process. In or-
der to clarify Hayashi (2001, p. 181) refers to former CEO Michael Eisner of the Walt Dis-
ney Company who explains cross-indexing as “When you see a gas station sign or a certain forma-
tion in the clouds” intuition tells you something. Further on Eisner explains intuition as “You
use reams of historical information about yourself that you remember from when you were a child can pop
into your mind. Gut instincts are the sum total of those experiences – millions and millions and millions of
them. And that sum total enables you to make reasonable decisions.” (Hayashi, 2001, p. 183). Haya-
shi (2001) concludes that intuitive feelings guide our decision making to a stage until our
conscious mind is able to make good decisions.
20
4 Empirical Findings
In this part of the study we will present the empirical findings. All information from the in-
terviews will not be presented since the introduction questions are not relevant for the
study. The main focus in this chapter is to cover the interview questions (Appendix 1) re-
lated to our research questions and the purpose of our report. We have conducted four in-
terviews. Two interviews were conducted with system owners and the other two were con-
ducted with people who work with IT management. Relevant part of the interviews will be
presented below. After three of the interviews we got the opportunity to see the systems
used and can therefore use this experience in our conclusion and analysis since we consider
this as a relevant part of the empirical findings.
4.1 Husqvarna AB
Respondent and Position
Lennart Dorthé, CIO
Jan Winblad, Application Manager – BI
Place and Date Huskvarna, 2007-11-22
Husqvarna offers products for consumers and professionals within three main product ar-
eas: forestry, lawn and garden, and construction. With a presence in over 100 countries,
Husqvarna employs more than 11,400 people world-wide. Their IT department, located in
both Sweden and the US, is responsible for Husqvarna’s IT in every operating area
throughout the entire organization. IT is divided in application areas with global responsi-
bility for that application or concept. Huqvarna uses a “best of breed” strategy and buys
the system they think will fit the organization’s needs best.
BI is nothing new in the Husqvarna organization. As one of the first organizations in Swe-
den, Husqvarna started working with extraction of operational data from different sources
and systems to an environment optimized for analysis, the foundation of BI. The IT de-
partment first discovered the BI concept and saw the potential business value. They dem-
onstrated it to a group of business users who became interested and wanted it imple-
mented. Dorthé explains that they in the early stages focused on improving and streamlin-
ing supply chain processes, since these processes use units and quantities which are less
complicated to deal with. The next step was to include financial processes in their BI solu-
tion, a more complex task with more factors to consider such as exact definitions of finan-
cial measures, fluctuating currencies, and bonuses for sales people. “Providing decision makers
with incorrect numbers that they use in their analysis and decision making is extremely dangerous” Win-
blad explains. Dorthé points out that defining and agreeing on financial measures and KPIs
is time-consuming and involves people all across the organization. Today, the most fre-
quent users of their BI system are executives and controllers who use BI for strategic deci-
sion making. However, BI is also used in different supply chain functions to analyze opti-
mal replenishment levels.
Further, Dorthé explains that BI has changed Husqvarna’s business model. Before the BI
system was implemented, reports were created throughout the organizations, in the facto-
ries and sales companies, a manual process that in some cases took up to three months to
complete. Now, this data is refreshed once a day and some data is refreshed as many as
seven times daily. Production line managers have even expressed their wishes for expand-
ing the solution to show real-time data. Winblad claims that this is not needed in most situ-
ations. By analyzing financial information in the DW, top management gets a full picture of
21
how well different sales companies and branches perform. This makes it easier to help local
branches and sales managers focus on certain areas, countries, or products with higher
profitability. Using their BI system Husqvarna’s executives can now follow long-term
trends, which earlier was impossible.
One of the main objectives with Husqvarna’s BI investment was to get everyone to “speak
the same language”. To be able to implement BI, sales companies world-wide were forced
to decide on item codes. Earlier, the same item code could be used for a chain saw in one
country and a lawn mower in another, which made item-level audits impossible and the de-
cision support consisted of high level summaries without any ability to show details,
Dorthé explains.
Dorthé claims that the information generated in the BI is correct, current, and relevant.
However, both Dorthé and Winblad agree that the information from the BI will never
meet everyone’s needs. “IT systems evolve and change and business users have new KPIs to track”,
Winblad explains.
Dorthé states that BI is limited to gather and show information but never make decisions
for the user. The outcome depends on the person making the decision. “The more useful in-
formation you have access to, the higher it the probability for making a better decision” Dorthé claims.
Husqvarna’s strategy is not to push data and reports to the business users using email, even
though this functionality exists. Instead, they want users to use the system themselves to
get the information they need in their job and make them aware of additional tools for
deeper analysis. Dorthé claims that the BI system is used frequently and it has received
great appreciation from the user community. One reason might be that the user friendly in-
terface. “In 10 years, BI is probably used by everyone throughout the entire organization”, Dorthé says.
4.2 Fläkt Woods AB
Respondent and Position Esbjörn Sjögren, Controller and System Owner – BI
Place and Date Jönköping, 2007-11-22
Fläkt Woods is a global supplier of air solutions. They employ more than 3,000 people and
have a local presence in 95 countries. In their Jönköping branch, Fläkt Woods has used
computers in their business since the 1970s, mostly Manufacturing Planning Systems
(MPS). A couple of years ago they started seeing a need for BI in their organization.
Sjögren explains how business users needed help from IT or employees with advanced
programming skills to run reports. The information was available in their system but it
could only be accessed using SQL programming which most business users did not know.
Also, having different users accessing data in the database without an appropriate user in-
terface was not the best solutions since mistakes could easily be made in live production
data. The production systems also predicted an increase in performance if the data was
moved outside the system, into a DW, to facilitate data analysis. A BI system based on Mi-
crosoft’s tools was implemented in the Swedish organization.
Fläkt Woods uses the BI system the way it was planned, within two main areas: finance and
sales. However, Sjögren states that the marketing department uses some information from
the BI. The BI assists business users to prepare financial statements and reports and in
finding the root cause to errors and mistakes, such as incorrect margins, in situations where
the specific order is unknown. The user interface enables fast access to the numbers and
22
time is saved when reports can be reused with data refreshed to show current numbers by
pressing a button. Also, Sjögren explains that it is easy to drill-down among orders to find
the one that is incorrect. The marketing department uses the BI system to see how well dif-
ferent markets perform. Predefined dimensions in the DW enable presentation of products
sorted by time, units, or margins etcetera.
The information from the BI system is used all across the organization. The users know
how much more information they have access to and that it can be compiled faster than
before. Sjögren explains that some users now have higher demands for access to reports.
They also have higher demand for detailed information such as sales numbers for specific
products. Problems occur when the users accept the system generated numbers without
challenging or critically examine them. Sjögren states that it does not matter if the system
works well when the input is of poor quality. You could say “garbage in, garbage out”, Sjögren
explains.
Sjögren experiences the BI generated decision support relevant, correct, and up-to-date.
The general data in the DW is updated every night, some data even more often. Access to
the data is easy in the Excel-based environment the BI solution provides. However,
Sjögren expresses the importance of data quality and the need for continuous work with
quality assurance. “With experience in your role, you develop a feeling for when the computer generated
numbers are correct and when they are off”, Sjögren explains. If the decision support lacks infor-
mation or important numbers, you know where in the systems to get these numbers from.
Since the BI system uses year to date (YTD) data, progressively build up during the year,
analysis is limited during the first months of the year. “If you only have one order to analyze, it is
hard to know if the margin is right since it is based in limited information and statistical analysis is impos-
sible”, Sjögren explains. The user selects the information to be displayed and Sjögren does
not think the BI system provides too much information. “However, if you select to have a lot of
information displayed at the same time, of course it will be hard to work with”, Sjögren concludes.
Even though the BI system provides business users with better decision support, Sjögren
claims that intuition is used in all decision making. “Decisions are made for future and the infor-
mation in the BI system is historical information”, Sjögren states. As an example he uses the fore-
cast, which always is slightly off. Taking this into consideration, the forecast is useful even
though it is slightly off.
Fläkt Woods has realized the potential of BI and plans further investing in new functional-
ity. Sjögren states that it could be graphically improved and include more features.
4.3 Myresjöhus AB
Respondent and Position Lars-Göran Wiss, Manager, ERP
Place and Date Myresjö, 2007-12-10
Myresjöhus employs more than 600 people in Sweden. They market, produce, and build
smaller residential houses in wood for the Nordic market. The organization consists of two
subsidiaries, Smålandsvillan and a company handling land, Myresjö Mark. Myresjöhus has
used computer systems to improve their business for more than 30 years. The accounting
and finance systems were the first systems to be implemented. In the early 1980s, the first
production and manufacturing systems were introduced.
23
Wiss explains that the business users constantly need to know how well they perform. They
are interested in total sales numbers, but also how total sales are divided between house
sales and land sales. Management is interested in how well the organization and different
sales offices perform. Myresjöhus’ ERP had these numbers but it was difficult and time-
consuming to collect them in reports. They needed a better tool to measure three critical
indicators: “How much do we deliver?”, “What are our sales?”, and “What is our backlog?”. The so-
lution was a BI system that was implemented during the first quarter of 2006.
The BI system provides good answers, faster, to all those questions. “The main goal with the
investment was to provide the business users with faster reporting”, Wiss explains. Not only is the de-
cisions support generated faster, the business users do not have to rely on Excel to the
same extent as they used to. All the source systems feed the DW with data and it is easy to
see when something is incorrectly entered into the system.
The decision support generated in the BI system is easy to understand and use. Depending
on position in the organization, the BI system is used differently. Some users, such as em-
ployees within sales, use the system daily. Other functions, such as management, use it
more on a weekly basis. Wiss explains that the most users use the improved reporting func-
tionality. “Experienced users tend to use the drill-down capabilities more”, Wiss explains. Since the
implementation, no one has expressed any complaints.
Wiss claims that the system is used the way it was planned. However, some functionality
and reports have been added that was not planned for such as automated tracking of the
number of errors completed houses have, which should be zero. Some users have re-
quested to change the time between receiving reports, which is easy for the system owner.
The information in the DW is refreshed every night which, according to Wiss, is enough.
“It is more a question of business user not being able to enter the numbers in to the source systems fast
enough”, Wiss explains.
It is hard to say whether the business users make better decision today compared to before
the BI system was implemented. However, decision makers have access to better decision
support, faster, and the information is more complete. With more complete information,
decisions that earlier were unstructured now are more structured. However, decision sup-
port has a great impact on decision making but is only a support.
Wiss states that in the future, BI could help improve manufacturing processes. “It is a ques-
tion of maturity. The users understand that they can get more information from existing systems which re-
sults in new requests and higher demands”, Wiss explains. However, Myresjö has no plans in fur-
ther investing in BI at the moment. Focus is to build on the current system and improving
and adding reports. Further, Wiss states that it is important to get more users to use exist-
ing BI tools, not only the reporting functionality.
4.4 Kinnarps AB
Respondent and Position Pierre Condradsson, IT Manager
Place and Date Kinnarp, 2007-12-13
Kinnarps is one of the largest suppliers of office solutions in Europe. Their headquarters,
along with five factories, are located in Sweden. Kinnarps employs 2,000 people and they
are present in 34 countries. They started working with computer systems in the early 1970s.
During that time they also implemented their first ERP system. In 1996, Kinnarps imple-
24
mented a new ERP system. The new system worked very well with Kinnarps’ business
processes but had a poor user interface and the reporting capabilities were even worse,
which created a need for a complementing tool for analysis and reporting. In 1998, Kin-
narps started working with Cognos and together they implemented a BI system.
Kinnarps has a horizontal organization and they always try to have as much decision mak-
ing as far down in the organizations as possible. Conradsson states that BI plays an impor-
tant role in enabling this and the BI system is used throughout the entire organization. “BI
is used in different functions from financial reporting for top management and CFO, to associates working
in production, to sales statistics sales in different sale companies”, Conradsson explains. According to
Conradsson, the BI system is used in every business process, including sales, production,
supply chain as well as finance and accounting.
Earlier, business users could look at the same numbers but see different values. This was in
many cases due to timing, which was an issue before the DW was implemented. Having a
single version of the truth makes it easier to agree that the decision support is correct. The
decision support generated in the BI system is current thanks to daily updates in the DW,
which is frequent enough for the organizational needs. However, production associates
have requested more frequent updates and in some cases, information is extracted straight
from the source, the ERP system, into the BI system.
After the implementation of the BI system, the need for decision support changed. Users
now know that improved decision support is available and new requirements and needs
have arisen. “Better tools for analysis and reporting have led to increased need for better decision support”,
Conradsson explains. Also, the BI system has enabled top management to monitor how
the organization performs using new KPIs. Conradsson claims that more decisions are
made based on solid facts compared to before the BI system was implemented. Business
users and top management want to know underlying causes to business problems and un-
derstand cause-effect correlations, something the BI system has made possible.
Conradsson states that most employees have a feeling for when numbers are off. “The num-
bers generated by the BI system are double checked on a regular basis to make sure that decision makers
have as correct decision support as possible”, Conradsson explains. The BI system enables presen-
tation of information from different sources in both tables and graphs, which is easier than
finding the number in different source systems. Another improvement is the system’s abil-
ity to present number aggregated. Conradsson believes the BI system will never be com-
pleted. “The BI system is constantly improved and extended as new systems are implemented in the or-
ganization and demands for new needs and KPIs arise”, Conradsson explains.
Conradsson claims that the decision maker makes the final decision. However, the BI sys-
tem enables efficient and continuous reporting, which allows business user or top man-
agement to agree on certain actions when different KPIs are met. “This is an increasing phe-
nomenon, especially in the sales organization. I believe this is related to the improved information sharing,
which makes it easier to set up rules such as ‘If the discrepancy is this much, this person makes the decision.
If the discrepancy is larger, this person makes the decision’”, Conradsson explains.
Conradsson states that the need for analytics will increase in the future. “Being able to find
patterns in data and understanding what impact ‘this change will have on that’”, Conradsson explains.
However, no further investments are planned. Conradsson explains that their BI environ-
ment is very flexible and scalable. The only BI related investment would be more licenses
as subsidiaries are integrated and more and more users start using the system.
25
4.5 Summary of Empirical Findings
All the interviewed organizations use BI extensively. They explain how BI provides them
with correct and updated data. Decision making has become easier when everyone has ac-
cess to the same data, something that was a problem before the implementation. With one
centralized data source, the data quality has improved and so has control. Also, it is easier
to access decision support when users do not need to search in multiple source systems to
find relevant information. Organizations report better decision support and new opportu-
nities to use data, that has been available before but not accessible, easier and faster. The
improved analytical capabilities have made it easier and faster to find problem areas and er-
rors. New functionality, such as drill-down and aggregation of data has made presentation
of data better and easier. Decisions have become more structured due to more complete
information. Making strategic decisions are complicated since more unstructured variables
influence such types of decisions. The organizations all agree that humans are involved and
influence decision making, in the end humans make the final decision. The BI investments
are seen as on-going processes. All interviewed organizations reported they will increase
their use in the future.
26
5 Analysis
In this chapter, we apply the theories presented in the frame of reference on our empirical findings to find dif-
ferences and similarities. The chapter is divided in two parts, one for each research question.
To not lose focus during the analysis, we have divided this chapter into two sub-chapters,
which aim to answer one research question each. In each sub-chapter, our frame of refer-
ence will help us reflect over and discuss our empirical findings. The first sub-chapter, deci-
sion support, concerns the research question; “How has decision support changed after a BI im-
plementation?” and the second sub-chapter, decision making processes, concerns; “How has
BI changed the way organizations make decisions?”.
Using theories and empirical findings in an iterative process, we will analyze reoccurring
statements and try to find similarities and differences between interviews. We will first
study our empirical material and then use theories from our frame of reference to try to
explain the phenomena. During this iterative process, we strive to better understand the
empirical findings and add our own reflections and thoughts. This process is illustrated in
figure 5.1.
Figure 5.1 - Analysis model
5.1 Decision Support
Eckerson’s (2003) BI environment consists of two parts, the DW environment and the
analytical environment. The data warehouse environment consists of ETL processes, which
combine data from multiple source systems, to prepare the DW with useful and appropri-
ate data. Comparing this to our empirical findings, we have found that organizations run
this process at least on a daily basis. However, if necessary, the ETL processes feed the
DW several times a day since decision makers may need current and updated decision sup-
port. For example, Winblad (J. Winblad, personal communication, 2007-11-22) explains
that some data in Husqvarna’s DW is refreshed as many as seven times daily. Our opinion
is that the more often data is updated, monitoring organizational performance and acting
upon abnormal variation becomes easier.
A DW enables users to work with a single set of data. Eckerson and White (2003) refers to
this as a single version of the truth. The results from the conducted interviews show that
organizations use this approach. Conradsson states that having a single version of the truth
makes it easier to agree that the decision support is correct (P. Conradsson, personal com-
munication, 2007-12-13). Similar, Husqvarna’s main objectives with their BI investment
was to get a common data set for decision support through a DW (L. Dorthé, personal
27
communication, 2007-11-22). However, we think it is important for organizations to have
clear procedures and guidelines for external data collection, such as what source to get cur-
rent exchange rate from, to achieve a single version of the truth. Another reflection is that
arguing about numbers, KPIs and other measures can be limited and time can be saved
when organizations have decision support based on a single version of the truth.
Eckerson and White (2003) argue that to be able to use decision support in decision mak-
ing, data quality is crucial. ETL processes are a natural part in any BI system to ensure that
high data quality is obtained. English (1999) argues that data quality is meeting business us-
er and end-customer expectations. Since ETL processes try to assure data quality, this may
not necessarily imply that the end-users are satisfied since opinions of what quality is may
vary. Our empirical findings show that organizations are aware of this issue and work ac-
tively to minimize mistakes and poor data quality. Sjögren (E. Sjögren, personal communi-
cation, 2007-11-22) states “garbage in, garbage out”, if data input is incorrect, it does not mat-
ter how well the system works, the output will still be incorrect. Winblad (J. Winblad, per-
sonal communication, 2007-11-22) states that “providing decision makers with incorrect numbers
that they use in their analysis and decision making is extremely dangerous”. To minimize the impact
of incorrect data, Kinnarps even double checks numbers generated by the BI system on a
regular basis to ensure that decision makers have as correct decision support as possible.
The general impression among all the interviewed organizations is that the data in their
DWs is correct and up-to-date. We think it would be interesting to examine to what extent
their organizational data actually is correct. However, we think it is impossible to have
completely correct data. The fact that our respondents believe they have correct and cur-
rent data might imply that organizations spend appropriate time and resources in develop-
ing and maintaining ETL processes for even better decision support. Eckerson and White
(2003) and Turban et al. (2007b) claim approximately 70% of time spent in a BI project
should be spent designing and developing ETL processes. Organizations may not realize
the importance of developing and maintaining ETL processes which requires a lot of re-
sources.
Our definition of BI states that decision support should be accessible to the right member
in the organization. This implies that organizational members do not need to spend time
searching for decision support in different source systems. Instead, necessary information
should be available using the BI interface, which is extremely time saving (Turban et al.,
2007b). All respondents agree that gathering data for decision support used to be a manual
and time consuming process. They describe a new situation where decision support is ac-
cessible in a timely manner. Fläkt Woods states that the information has always existed but
now, with a central storage, it is easier to gather relevant information from the source sys-
tems (E. Sjögren, personal communication, 2007-11-22). Using BI, Turban et al. (2007a)
argue that both routine and ad hoc reporting is easy. This enables easy access to decision
support for every decision making situation. Our interviews show that organizations use
both routine and ad hoc reports, as Wiss puts it, “business users constantly need to know how well
they perform” (L-G. Wiss, personal communication, 2007-12-10).
Eckerson (2003) claims that visualization makes data easier to understand. The user tools
provided in BI systems enable users to interpret data and perform data analysis. According
to Turban et al. (2007a) drill-down functionality could be used to analyze data and find
problem areas. All respondents mention improved decision support with ability to both ag-
gregate data and drill-down among data in order to fit different decision situations as well
as possible. Sjögren (E. Sjögren, personal communication, 2007-11-22) explains that it is
easy to drill-down among orders to find the incorrect one. Our empirical findings imply
28
that BI systems provide improved decision support. Users now have better overview when
numbers from multiple source systems are presented in spreadsheets and dashboards, ta-
bles, and graphs.
According to the respondents, the decision support is considered to be easier to use when
users decide information to be displayed. Information not relevant for the specific decision
making situation can easily be opted out and not displayed (Turban et al., 2007b). Even if
the respondents claim it is easy to gather and display information, it is of course a situation
were the users need to mature in their usage of the BI system. We believe users who have
matured in their usage are starting to realize the benefits BI generates. With an increased
level of maturity new exciting areas of use will probably be discovered. It may also depend
on computer skills, an experienced computer user will probably use more BI functionality
for generating ad hoc decision support at an earlier stage, compared to a user with limited
computer experience.
5.2 Decision Making Processes
Gorry and Scott Morton (1989) base their framework on decision making processes and
they point out that the framework should not be seen as a way to describe information sys-
tems. Since BI systems’ main objective is to assist decision making, we believe Gorry and
Scott Morton’s (1989) framework is useful to describe how BI could support and change
decision making. Gorry and Scott Morton (1989) argue that when organizations understand
the underlying parameters of decisions, it is possible to make them more structured, and
save time for other tasks. This because decisions can be supported by the system itself with
less influence from decision makers and therefore, decisions become more structured. We
argue that BI systems, using its analytical environment, could transform either semi-
structured or unstructured decisions to become more structured.
As described in 5.1, BI enables faster access to decision support, which has a direct impact
on time spent in decision making processes. This may imply that more efficient processes
can be achieved due to time savings. Husqvarna reports improved processes with instant
access to information, a process that used to be manual and took up to three months to
complete (L. Dorthé, personal communication, 2007-11-22). Conradsson (P. Conradsson,
personal communication, 2007-12-13) states that Kinnarps’ BI system has made decision
making processes more structured such as predetermined action when different KPIs are
met. Myresjöhus mentions that unstructured decisions have become more structured due
to more complete information (L-G. Wiss, personal communication, 2007-12-10).
The top of Gorry and Scott Morton’s (1989) framework includes Anthony’s (1965) catego-
ries of managerial activities; operational control, management control, and strategic plan-
ning. The time horizon and complexity concerning decisions between these categories dif-
fer. Strategic decisions involve activities with a longer time horizon and they do not occur
frequently whereas operational decisions are made on a daily basis and concern simple
tasks (Gorry & Scott Morton, 1989). According to Gorry and Scott Morton (1989), deci-
sion making on each organizational level requires different decision support. In Myres-
jöhus, the BI system is used differently depending on position in the organization. Some
users, such as employees within sales, use the system daily whereas other functions, such as
management, use it more on a weekly basis (L-G. Wiss, personal communication, 2007-12-
10). Turban et al. (2007a) state that BI supports decision making on every hierarchical level.
We argue that BI is applicable to Gorry and Scott Morton’s (1989) framework and could
assist any member of any organizational level. This is also in line with the results of our
empirical study. Our empirical findings show that BI is mainly used for operational and
29
management control. However, Husqvarna and Kinnarps also use BI to support strategic
planning. We believe the limited use of BI in strategic planning is due to the more unstruc-
tured and complicated parameters strategic decisions tend to concern. Both Husqvarna and
Kinnarps have a high maturity level of BI, which we believe might be the reason why BI is
so wide-spread.
We have found similar areas of BI usage in our empirical findings. Most frequently, BI is
used to assist decision makers in financial and sales departments to generate reports and
enable analysis. However, some differences have been noticed. Husqvarna uses BI in most
departments to support decision making. Kinnarps even claims they use BI in every pri-
mary process. “[In Kinnarps,] BI is used in different functions from financial reporting for top manage-
ment and CFO, to associates working in production, to sales statistics in different sale companies”, as
Conradsson (P. Conradsson, personal communication, 2007-12-13) explains. From our
empirical findings we conclude that Anothony’s (1965) categories of managerial activities
are covered. All respondents believe their BI investment is an on-going process where new
areas of use constantly are discovered, connected, and included in the BI system.
Husqvarna believes, in 10 years, BI will be used by everyone throughout the entire organi-
zation. Kinnarps believes BI usage will increase as affiliated companies are integrated in the
system. Fläkt Woods and Myresjöhus have a different focus and believe they will add more
functionality and get the existing users to better use the analytical functionality of their BI
systems.
Any decision making process involves humans to make the final decision. All respondents
point out their BI system is only supportive in this process and the decision maker puts the
decision into action. Further, the respondents explain that BI generated decision support
has a great impact on decision making processes, but indirect and supportive, which Tur-
ban et al. (2007b) agree with. Indirect decision support does not provide an answer to a
problem but provides support to solve it, such as numbers or graphs (Turban et al. 2007b).
Dorthé (L. Dorthé, personal communication, 2007-11-22) states that “BI is limited to gather
and show information but never make decisions for the user. The outcome depends on the person making the
decision”. We think human ability to make decisions and systems’ ability to provide decision
support is the best combination for effective decision making. As Hayashi (2001) explains,
the main objective with cross-indexing is to gather information from different sources,
which also is the main purpose of a DW. Therefore, we conclude that BI easily can be ap-
plied to cross-indexing and support intuitive processes. When a decision maker uses cross-
indexing, in this case BI and the generated decision support, Hayashi (2001) argues that in-
tuition tells you something, feelings that guide the decision making until satisfactory deci-
sions are made. Decision support or BI systems cannot replace human intuitive ability. De-
cision makers have the ability to apply previous experiences combined with decision sup-
port as they may see patterns they recognize from similar situations (Hayashi, 2001). Fläkt
Woods states that “with experience in your role, you develop a feeling for when the computer generated
numbers are correct and when they are off” (E. Sjögren, personal communication, 2007-11-22).
30
6 Conclusions
In this chapter, we present the outcome of our analysis in a few concluding statements. The results are pre-
sented and divided between our research questions.
Our analysis show positive effects on organizations after a BI implementation. As we dis-
covered, BI is wide-spread in manufacturing organizations and is becoming more inte-
grated in the organization.
How has decision support changed after a BI implementation?
Control: We have identified that BI improves the control of decision support. This be-
cause organizations can unify source systems meaning that the input of the decision sup-
port is stored in one place. This also enables quality control of information gathered from
source systems throughout organizations. Another aspect is that BI improves the ability to
analyze decision support with more flexibility compared to before the implementation. On-
ly relevant data for decision making is presented which allows better decision support and
better decisions.
Time: BI has proven to save organizations time by enabling decision support to be easily
accessible in a timely manner. Decision support can now be used by members routinely or
by performing ad-hoc analysis, with correct, updated, and sufficient information. Earlier,
this process was complicated and time consuming as information was scattered throughout
different location in the organization.
How has BI changed organizational decision making?
Improved decision making: BI provides faster and easier access to decision support
which has a direct impact on decision making. Organizations experience improved and
more efficient processes. This is a result of BI’s ability to provide more accurate and cur-
rent data for analysis. Since decision makers have a unified data storage, insight in other
functions or areas increases the ability to make better decision.
Unstructured decisions are now more structured: By using BI, decisions that earlier
were unstructured or semi-structured are now more structured. Especially in financial and
operational functions as such functions involve specific procedures, which are easier to
structure. With less time spent in decision making processes, decision makers now have
more time for other activities. This means, variables that are below the horizontal are re-
ferred to as unstructured decisions are moving upward and become more structured with
BI.
Intuition in decision making: Even though BI has improved quality of decision support
substantially, humans will always make final decisions influenced by intuition and experi-
ence. BI has the ability to reduce the unstructuredness in decision making processes by
transforming decisions to a lower level of unstructuredness. When decisions are less un-
structured, intuition becomes less important. However, intuition can also support decision
makers when past experiences provide new ideas or explain patterns.
31
7 Final Discussion
In this chapter we reflect on our work and give suggestions for further research concerning Business Intelli-
gence. We also acknowledge people that have supported our thesis work.
7.1 Reflections
Writing about BI has been interesting and we believe this thesis has contributed with new
knowledge. Our approach with recognized theories and new concepts is interesting and
may contribute to new viewpoints and thoughts. Turban et al. (2007a/b) are highly present
in our thesis. We would have preferred having other references but have concluded during
our literature study that Turban et al. (2007a/b) are well-known and highly represented and
referred to in BI literature. The qualitative approach was appropriate for our study since it
provided deeper understanding of our research topic, something a quantitative approach
could not. Using a quantitative approach, the characteristics of the empirical findings would
have been different and we might have discovered other aspects and come to different
conclusions. When selecting respondents, we decided to have between four and seven in-
terviews. However, after conducting four interviews we felt we had sufficient information
to fulfill our purpose. Our interview method, semi-structured, suited our thesis well and it
would have been difficult to fulfill our purpose using a different method. A better connec-
tion between our theoretical framework and the interview questions could have improved
our empirical findings and analysis. However, we feel we have answered our research ques-
tions and fulfilled our purpose.
A different sampling method could have provided higher generalization of our study.
However, using convenience sampling to select our respondents, we contacted 11 organi-
zations. Five used BI systems according to our definition and had more than one year ex-
perience of BI, hence fulfilled our criteria. One did not want to participate since their BI
system included sensitive information. The other four agreed to participate in our study.
Even though our sampling technique may be a weakness in our study, we believe the simi-
lar results from our empirical findings allow us to generalize our study within our presented
problem delimitation.
7.2 Suggestions for Further Studies
During the process of writing this thesis, we have discovered that there is limited research
concerning BI and this area needs to be further explored. Accordingly, we have many sug-
gestions of research topics. We believe it would be interesting to investigate the amount of
time a BI system could save an organization and which functions that benefit the most
from BI initiatives. Another topic could be the connection between BI and financial re-
turns with its problematic aspects of measuring intangible benefits. From a vendor’s per-
spective, to investigate how organizations and users would like to improve their BI solution
and why. Our respondents pointed out that decision makers use BI for support in decision
making and further studies could concern intuition and its role in decision making proc-
esses. From a technical point of view, future sources of data and other areas that can be in-
tegrated in BI systems for better decision support is another research topic. We believe it
would be interesting to research if there is a need or interest for organizations to connect
to suppliers’ systems to perform data analysis for better planning and process performance,
as new technologies for distributed data processing evolve. This thesis has not focuses on
HCI and another area for further research could be how to improve the user interface of
BI systems.
32
7.3 Acknowledgements
We would like to conclude by thanking the people that have helped us through this thesis
writing process. Our tutor Jörgen Lindh, for guidance and valuable feedback. We would al-
so like to thank Hiren Jansari and Johanna Reinholtz at SYSteam for demonstrating BI
products and providing us with valuable insight in the BI concept. All our respondents for
taking their time and participating in interesting and very informative interviews. Further-
more, we would also like to thank our fellow students for good advice during seminars.
33
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Appendix 1 – Interview Questions, English
Personal
Position?
Tasks and responsibilities?
How long have you had your current position and how long have you worked in this or-
ganization?
Previous tasks and responsibilities?
Organization
Could you briefly describe your organization?
How long have you used computer systems?
What have the computer systems historically been used for?
What is the hierarchical structure like?
Business Intelligence system
Why did you invest in BI?
Which BI system are you using?
Who implemented the system and who is responsible for maintenance?
In what areas do you use BI?
- For which decisions do you use BI? (Operational, Managerial, Strategic)?
- Who uses your BI as a support in decision making?
Has BI changed you organization and if yes, how? (Positive and Negative)
How frequently is BI used in decision making?
- Are you using BI as planned?
Has BI improved your decision support and if yes, how?
What sources are used for the decision support? Which are the most important?
Do you think the decision support is relevant, correct, up-to-date, and enough?
- Are decisions made with insufficient decision support?
Is the decision support available in a timely manner?
- How and why?
Is it easy to understand and use the decision support generated from your BI system?
- How and why?
What are your thoughts on the amount of decision support?
To what extent are decisions made, based on decision support from your BI system?
Does your organization make better decisions now; compared to before the BI system was
implemented?
- Can the decision support be improved and if yes, how?
Has the degree of intuition in decision making decreased since the implementation of BI?
What do your future needs for BI look like?
What pros and cons do you see with your BI system?
What are your plans for future BI investments?
Do you believe your BI system fulfill expectations?
36
Appendix 2 – Interview Questions, Swedish
Person
Befattning?
Uppgifter och ansvarsområden?
Tid i befattning och företaget?
Tidigare uppgifter och ansvar?
Organisation
Kan du kort beskriva er verksamhet?
Hur länge har ni använt datorsystem?
Vad har datorsystemen använts till historiskt?
Hur ser den hierarkiska strukturen ut?
Beslutsstödsystem
Varför har ni valt att investera i BI?
Vilket BI-system använder ni?
Vem implementerade, och vem ansvarar för driften för BI-systemet?
Inom vilka områden använder ni BI?
- Till vilka typer av beslut används ert BI (Nivå s, t, o)?
- Vilka använder ert BI för beslutsfattande (Nivå s, t, o)?
Har BI medfört förändringar i ert företag och i så fall vilka (positiva, negativa)?
Hur frekvent används BI som beslutsunderlag?
- Utnyttjas BI-systemet som det var tänkt?
Hur såg ert beslutsunderlag och ut före implementeringen?
Har beslutsunderlaget förbättrats och i så fall hur?
Vilka källor används till beslutsunderlaget och vilka är viktigast?
Känns beslutsunderlaget relevant, korrekt, aktuellt och tillräcklig?
- Tas beslut med ett otillräckligt beslutsunderlag?
Finns beslutsunderlaget tillgängligt på ett tidsmässigt tillfredsställande sätt?
- På vilket sätt och varför?
Är beslutsunderlaget lätt att utläsa och använda?
- På vilket sätt?
Vad anser ni om mängden beslutsunderlag?
Hur stor del av besluten baseras på beslutsunderlaget genererat av ert BI?
Tas bättre beslut genom beslutsunderlaget nu än innan BI-systemet?
- Kan beslutsunderlaget förbättras och i så fall hur?
Har mänskligt beslutsfattande minskat med BI-implementeringen?
Hur ser behovet ut framöver?
Vilka fördelar och nackdelar tycker ni BI medför?
Finns det tankar om ytterligare investeringar i BI?
Stämmer ert BI med förväntningarna?
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