Description
The amount of data enterprises daily have to cope with has, however, constantly increased in the last years.
Business Intelligence
Dynamic SME
B u s i n e s s I n t e l l i g e n c e
T h o m a s F e l i x K a r r a s c h
Business Intelligence
Agenda:
1. De?nition
2. Advantages
3. Implementation
4. Recommendations
5. Attachments
6. Bibliography
1
2
3
6
8
11
1. De?nition
In today´s business environment, the ability to create new knowledge and un-
derstand the market forces has become a crucial factor for the maintenance of
market share and pro?tability.
1
This requires the ability to permanently retrieve,
store, analyze and communicate data. The amount of data enterprises daily
have to cope with has, however, constantly increased in the last years. Espe-
cially the internet has contributed to a never ending ?ow of data input, that has
made it dif?cult for the management to capture the information relevant for
proper decision making. As a result enterprises have developed a business in-
telligence (BI) KMT, which facilitates the retrieval of information out of data.
2
There are many different de?nitions for BI. While some differentiate between BI
as the management for internal and CI for external data,
3
others de?ne it “as
the ability to extract actionable insight from the internal and external data
available to an organization, for the purpose of supporting decision making
and improving corporate performance.” (see illustration 3)
4
This thesis will fol-
low the second de?nition and thus refers to BI as the integration of strategies,
processes and techniques to generate actionable intelligence of dispersed and
inhomogeneous data of an enterprise, the market or competitors.
5
BI can be
hence understood as a supply chain process for data. It sources data from the
organizational functions of an enterprise and the exterior, i.e. competitor or
market data and converts it into actionable intelligence (see illustration 4). As a
result BI helps to organize oceans of scattered data and extract all the relevant
information needed by the management to take good business decisions.
6
With the increasingly turbulent economic environment, that demands enter-
prises to remain constantly updated on market movements, the role of BI in the
business world has changed over the last years. From a basic subordinated tool
Business Intelligence
1
1
Cf. Kudyba, S./ Hoptroff, R. (2001), p. 5.
2
Cf. Hannig, U. (2002), p. 3.
3
Cf. Steyl, J. (2009): URL: see list of references.
4
Canes, M. (2009): URL: see list of references.
5
Cf. Institute for Business Intelligence (w/o Y.), URL: see list of references.
6
Cf. Harper, D. (w/o. Y.), URL: see list of references.
for the analysis of data, BI has emerged as complete new management ap-
proach which is imperative in the decision making process.
7
2. Advantages
As many SMEs are merely owner-managed, decisions are mostly taken based
on static reports, simple spreadsheets or instinct. While this routine is practica-
ble if an SME is small and easily manageable, the method soon reaches its limit
with quick market changes or intensifying competition, which demand decision
making based on relevant and current information.
8
The BI tool facilitating the extraction of information from data and providing
intelligence needed for strategic decision making, serves this need.
9
Through
gaining information i.e. of a new product or service of a competitor, it enables
enterprises to track the move-
ments and changing strategies of
the competition. Based on this, an
enterprise is able to identify gaps
in the market, which it can use to
?ll customer needs.
10
By gaining
relevant information about the
competitor, BI furthermore allows
an enterprise to determine its
weaknesses and thus provides insight about the competitive position in the
market environment.
11
As a result, BI provides an SME with a good overview
Illustration 1: Business intelligence leads to the
discovery of new opportunities
Source: Original scene from the video.
Business Intelligence
2
7
Cf. Olszak, C.M./ Ziemba, E. (2012), p. 130.
8
Cf. Canes, M. (2009): URL: see list of references.
9
Cf. Guarda, T. et al. (2013), p. 187f.
10
Cf. Frost, S. (w/o Y.), URL: see list of references.
11
Cf. Bose, R. (2007), p. 511f.
over threats and opportunities in the market, based on which it can derive
measurements for the improvement of its operations (see illustration 1).
12
Another advantage of BI is the ability to forecast trends.
13
Analytical techniques
of BI identify relationships or unusual patterns in the obtained data and are
hence able to visualize information that has not been recognized before.
14
By
the discovery of these anomalies, an enterprise becomes aware of emerging
trends and is able to adjust its strategy accordingly. The ability to discover pat-
terns and relationships data also holds advantages for the customer service.
Through the analysis of i.e. sales data, BI assesses the demography as well as
habits of the customers and determines correlating products or services. As a
result, BI offers valuable opportunities for up-selling
15
and cross-selling prod-
ucts
16
. This allows an enterprise to streamline its marketing efforts, resulting in
an improved customer experience and better use of resources.
17
3. Implementation
Researchers have proposed many different approaches for the design of a BI
process. This thesis will focus on the most commonly applied structure, which
consists of ?ve distinct phases (see illustration 2).
18
The process starts with the planning phase, in which the objectives for the
process are set.
19
At this point it is important that the decision makers, for
whose demand the process is ultimately created, communicate their intelli-
gence needs, as these establish the foundation for the later phases. Based on
Business Intelligence
3
12
Cf. Vedder, R.G. et al. (1999), p. 110.
13
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
14
Cf. Guarda, T. et al. (2013), p. 188.
15
up-selling is a sales strategy in which the seller encourages a customer to purchase more ex-
pensive items or upgrades for a higher sales pro?t
16
cross-selling is the encouragement of an existing customer to purchase an additional product
17
Cf. Williams, S./ Williams, N. (2007), p. 162.
18
Cf. McGonagle, J.J. (2007), p. 71.
19
Cf. Morcillo, P. (2003), URL: see list of references.
these needs, key intelligence topics (KITs) can be created, which point out the
topics of greatest interest for the achievement of the strategic goals of the
company.
20
Once the KITs are de?ned, the process continues with the collection phase, in
which the data has to be sourced and preprocessed. Here it is ?rst of all impor-
tant to match the available data sources with the KITs formulated in the plan-
ning phase.
21
This compliance with the intelligence needs is necessary to en-
sure that a correct decision can be taken based on the resourced data. Before
collecting the data, the source should thus furthermore always be checked for
its suitability.
22
For the collection itself, SMEs have a large variety of potential
sources at their disposal. They can i.e. source the data directly from interviews,
focus groups or executives. The most recommendable source, is probably,
however, the internet, as it has made research, i.e. on competitors, easy and
affordable. Here the SME has the option to connect with experts, customers
and suppliers or gather data from webpages, i.e. of competitors. While these
Illustration 2: The business intelligence process
Source: Own illustration based on Bose, R. (2007), p. 513; Guarda, T. et al. (w/o Y.), p. 187f.
De?ne
necessary
data
Collect data,
data pre-
processing,
data analysis
Data analysis,
Project results
Databases
Internal
Data
Sources
External
Data
Sources
Decision
Making
Re-initiation of Process
Data
Distribution
Feedback
Planning
Collection Dissemination Analysis Feedback
Business Intelligence
4
20
Cf. Krizan, L. (1999), URL: see list of references.
21
Cf. Bose, R. (2007), p. 513.
22
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
are all primary resources
23
, an SME can furthermore tap secondary resources
24
.
For this purpose search engines and online subscription databases have be-
come popular tools to collect data from commercial news organizations or
news ?ltering services.
25
Lastly the collected data needs to be re?ned and
structured for further analysis.
26
The next phase, the analysis phase, is the core stage and most critical part of
the BI process. It differs from the collection process in the way, that its purpose
is not to collect a set of data from diverse sources (i.e. internet, internal or ex-
ternal databases), but to illustrate the signi?cance of a prede?ned data set.
27
To
do so the re?ned data collected before is systematically examined, analyzed
and validated.
28
The main activity consists of mining the data
29
to identify pat-
terns and relationships for the extraction of actionable intelligence.
30
SMEs of-
ten encounter dif?culties performing this task, as they are not able to afford the
complex BI infrastructures of larger companies (see illustration 5). Nowadays,
however, there are many possible alternatives. Open source software for in-
stance is often available online at no cost and thus offers the SMEs an opportu-
nity to start mining data without committing to a large investment.
31
Once the intelligence has successfully been extracted, it needs to be dissemi-
nated. In this stage the extracted intelligence is reported back to management
through meetings, reports or dashboards, providing insight on the KITs. For this
purpose it is essential that the report is visualized an easy-to-understand format
so possible misunderstandings can be avoided.
32
Provided with the intelli-
gence on the KITs, the decision makers are then able to take action. Besides for
Business Intelligence
5
23
Primary sources are ?rst hand sources that provide direct evidence of a topic.
24
Secondary sources interpret or analyze primary sources and are thus one step remote from
the event.
25
Cf. Botha, A.P. (2007), p. 53.
26
Cf. Barnard, S. (w/o Y.), URL: see list of references.
27
Cf. Krizan, L. (1999), URL: see list of references.
28
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
29
data mining is a set of activities to identify hidden relationships in data.
30
Cf. Miller, S.H. (2001), URL: see list of references.
31
Cf. Chen, X. et al. (2007), p. 4.
32
Cf. Miller, S.H. (2001), URL: see list of references.
decision making, the intelligence may also serve for further analyses such as
competitor pro?ling, scenario planning or scenario analyses.
The process is concluded with the feedback of the executives. It includes an
assessment of the quality and accuracy of the intelligence as well as guidance
for the analyst of how the process can be improved in the future.
33
A BI process
thus has to be understood as iterative in the sense that it permanently repeats
upon completion and is always subject to improvement.
34
4. Recommendations
As many enterprises are opposed to change or new technology, the support
from top management is a critical factor for the success of BI. A dedicated
management which endorses the BI process ensures ?nancial resources and
effective project management.
35
It is thus the responsibility for those involved
in an intelligence program to convince the senior executives of its usefulness.
They should be encouraged to use the system actively instead of contemplat-
ing it just as another reactive management resource.
36
Besides its endorsement
for BI as a system, it is important that the top management considers the intel-
ligence program an iterative process rather than a one time project.
37
This con-
tinuity is essential, as strategic planning requires long-term intelligence and a
continuous basis of information.
Closely connected to continuity are also other critical success factors. A culture
of trust and cross-organizational collaboration, i.e. is vital for the effective
knowledge exchange and thus critical to transform knowledge of an individual
into organizational knowledge. Especially in SMEs, that rely mostly on tacit
knowledge, knowledge workers can only be replaced to a certain extent. A col-
Business Intelligence
6
33
Cf. Bose, R. (2007), p. 514.
34
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
35
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
36
Cf. Global Intelligence Alliance (2004), URL: see list of references.
37
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
laborative culture facilitates a healthy ?ow of information within the organiza-
tion, which is needed to make the collected and analyzed information about
customers, competition, market conditions, vendors, partners, products and
employees available at all levels.
38
A properly functioning BI process in this
sense also requires the utilization of tools and applications. They not only facili-
tate the collaboration by providing means to communicate the intelligence
within the organization, but furthermore encourage BI users to produce con-
tent themselves. In the end, it should, however, always be clear to an enterprise
that tools merely support the managing knowledge process and that BI can
consequently only be successful, if the knowledge and skills of the people are
used effectively.
39
Business Intelligence
7
38
Cf. Atre, S. (2003), URL: see list of references.
39
Cf. Global Intelligence Alliance (2004), URL: see list of references.
5. Attachments
Illustration 3: De?nition of Business Intelligence
Source: Own illustration.
Data Processing Data Sourcing Data Visualization
Data
Intelligence
Data Integration from
Departments and
External Sources
Intelligent Data
Analysis
Data
Dissemination
Internal
Data
Sources
External
Data
Sources
Business Intelligence
8
Source: Own illustration.
Illustration 4: The business intelligence value chain
Increasing
potential to
support
business
decisions
Data
Knowledge
Insight
Action
Information
Business Intelligence
9
Source: Own illustration based on Hannig, U. (2002), p. 6.
Illustration 5: The architecture of business intelligence
Internal Data Sources
(ERP Systems)
External Data Sources
(Internet)
Standardized
Reporting
Ad-hoc Re-
porting
Data
Mining
Meta-Data Bank
ETL-Tools
Extract
Transfer
- Convert
- Filter
- Aggregate
Load
OLAP Tools
Archive
Data Warehouse
Data Mart
Business Intelligence
10
6. Bibliography
Barnard, S. (w/o Y.): Business intelligence for SMEs, in:http://www.insideinfo.com.au/?les/business@20intelligence@[email protected]
df (accessed 09/25/2013).
Bose, R. (2007): Competitive intelligence process and tools for intelligence
analysis, in: Industrial Management and Data Systems, Vol. 108, No. 4, p. 510-
528.
Botha, A.P. (2007): Knowledge - living and working with it, Cape Town, South
Africa.
Canes, M. (2009): Business Intelligence for the SME, in:http://www.bluelinkerp.com/downloads/2009-10-15-Business-Intelligence-for-t
he-SME.pdf (online publication from 15/10/2009; accessed 09/23/2013).
Chen, X./ Ye, Y./ Williams, G./ Xu, X. (2007): A Survey of Open Source Data
Mining Systems, in: Washio, T./ Zhou, Z.H./ Huang, J.Z./ Hu, X./ Li, J./ Xie,
C./ He, J./ Zou, D./ Li, K.C./ Freire, M.M. (eds.) (2007): Emerging Technolo-
gies in Knowledge Discovery and Data Mining, Berlin; Heidelberg, Germany.
Frost, S. (w/o Y.): What Are the Bene?ts of a Competitive Intelligence Organi-
zation?, in:http://smallbusiness.chron.com/bene?ts-competitive-intelligence-organization-
33876.html (accessed 09/24/2013).
Global Intelligence Alliance (2004): Key Success Factors of Competitive In-
telligence, in:http://www.gcc-consulting.com/GIA+WhitePaper_KeySuccess
_2004-10-25[1].pdf (online publication from 04/2004; accessed 09/26/2013).
Guarda, T./ Santos, M./ Pinto, F./ Augusto, M./ Silva, C. (2013): Business In-
telligence as a Competitive Advantage in SMEs, in: International Journal of
Trade, Economics and Finance, Vol. 4, No. 4, p. 187-190.
Business Intelligence
11
Hannig, U. (2002): Knowledge Management und Business Intelligence, Berlin,
Germany et al.
Harper, D. (w/o. Y.): Surveying The Business Intelligence Space, in:http://modeladvisor.com/interesting_articles/business_inteligence.htm (ac-
cessed 09/05/2013).
Krizan, L. (1999): “Intelligence essentials for everyone”, Occasional Paper Se-
ries, No. 6, Joint Military Intelligence College, Washington, DC, in:http://www.scip.org/?les/Resources/Krizan-Intelligence-Essentials.pdf (online
publication from 06/1999; accessed 09/25/2013).
Kudyba, S./ Hoptroff, R. (2001): Data Mining and Business Intelligence - A
Guide to Productivity, London, UK.
Institute for Business Intelligence (w/o Y.): Verständnis von Business Intelli-
gence, in:http://www.competence-site.de/Institut-fuer-Business-Intelligence-IBI (ac-
cessed 09/23/2013).
McGonagle, J.J. (2007): An Examination of the !Classic! CI Model, in: Journal
of Competitive Intelligence and Management, Vol. 4, No. 2, p. 71-86.
Miller, S.H. (2001): Competitive Intelligence – An Overview, in:http://www.ventes-marketing.com/References/Intelligence concurrentielle/
Articles/CI%20Overview.pdf (accessed 09/25/2013).
Morcillo, P. (2003): Vigilancia e inteligencia competitiva: fundamentos e im-
plicaciones, in:http://www.madrimasd.org/revista/revista17/tribuna/tribuna1.asp (online pub-
lication from 06/2003; accessed 09/25/2013).
Olszak, C.M./ Ziemba, E. (2012): Critical Success Factors for Implementing
Business Intelligence Systems in Small and Medium Enterprises on the Exam-
ple of Upper Silesia, Poland, in: Interdisciplinary Journal of Information, Knowl-
edge, and Management, Vol. 7, No. 3, p. 129–150.
Business Intelligence
12
Sangar, A.B./ Iahad, N.B.A. (2013): Critical Factors That Affect The Success Of
Business Intelligence Systems (BIS) Implementation In An Organization, in: In-
ternational Journal of Scienti?c & Technology Research, Vol. 2, No. 2, p. 176-
180.
Steyl, J. (2009): Business Intelligence vs. Competitive Intelligence, in:http://it.toolbox.com/blogs/bi-ci/business-intelligence-vs-competitive-intellige
nce-32441 (online publication from: 06/22/2009; accessed on 09/23/2013).
Vedder, R.G./ Vanecek, M.T./ Guynes, C.S./ Cappel, J.J. (1999): CEO and
CIO perspectives on competitive intelligence, in: Communications of the ACM,
Vol. 42, No. 8, p. 109-116.
William, Y./ Koronios, A. (2010): Critical Success Factor for Business Intelli-
gence Systems, in: Journal of Computer Information System, Vol. 50, No. 3, p.
23-32.
Williams, S./ Williams, N. (2007): The Pro?t Impact of Business Intelligence,
San Francisco, USA.
Business Intelligence
13
doc_842741028.pdf
The amount of data enterprises daily have to cope with has, however, constantly increased in the last years.
Business Intelligence
Dynamic SME
B u s i n e s s I n t e l l i g e n c e
T h o m a s F e l i x K a r r a s c h
Business Intelligence
Agenda:
1. De?nition
2. Advantages
3. Implementation
4. Recommendations
5. Attachments
6. Bibliography
1
2
3
6
8
11
1. De?nition
In today´s business environment, the ability to create new knowledge and un-
derstand the market forces has become a crucial factor for the maintenance of
market share and pro?tability.
1
This requires the ability to permanently retrieve,
store, analyze and communicate data. The amount of data enterprises daily
have to cope with has, however, constantly increased in the last years. Espe-
cially the internet has contributed to a never ending ?ow of data input, that has
made it dif?cult for the management to capture the information relevant for
proper decision making. As a result enterprises have developed a business in-
telligence (BI) KMT, which facilitates the retrieval of information out of data.
2
There are many different de?nitions for BI. While some differentiate between BI
as the management for internal and CI for external data,
3
others de?ne it “as
the ability to extract actionable insight from the internal and external data
available to an organization, for the purpose of supporting decision making
and improving corporate performance.” (see illustration 3)
4
This thesis will fol-
low the second de?nition and thus refers to BI as the integration of strategies,
processes and techniques to generate actionable intelligence of dispersed and
inhomogeneous data of an enterprise, the market or competitors.
5
BI can be
hence understood as a supply chain process for data. It sources data from the
organizational functions of an enterprise and the exterior, i.e. competitor or
market data and converts it into actionable intelligence (see illustration 4). As a
result BI helps to organize oceans of scattered data and extract all the relevant
information needed by the management to take good business decisions.
6
With the increasingly turbulent economic environment, that demands enter-
prises to remain constantly updated on market movements, the role of BI in the
business world has changed over the last years. From a basic subordinated tool
Business Intelligence
1
1
Cf. Kudyba, S./ Hoptroff, R. (2001), p. 5.
2
Cf. Hannig, U. (2002), p. 3.
3
Cf. Steyl, J. (2009): URL: see list of references.
4
Canes, M. (2009): URL: see list of references.
5
Cf. Institute for Business Intelligence (w/o Y.), URL: see list of references.
6
Cf. Harper, D. (w/o. Y.), URL: see list of references.
for the analysis of data, BI has emerged as complete new management ap-
proach which is imperative in the decision making process.
7
2. Advantages
As many SMEs are merely owner-managed, decisions are mostly taken based
on static reports, simple spreadsheets or instinct. While this routine is practica-
ble if an SME is small and easily manageable, the method soon reaches its limit
with quick market changes or intensifying competition, which demand decision
making based on relevant and current information.
8
The BI tool facilitating the extraction of information from data and providing
intelligence needed for strategic decision making, serves this need.
9
Through
gaining information i.e. of a new product or service of a competitor, it enables
enterprises to track the move-
ments and changing strategies of
the competition. Based on this, an
enterprise is able to identify gaps
in the market, which it can use to
?ll customer needs.
10
By gaining
relevant information about the
competitor, BI furthermore allows
an enterprise to determine its
weaknesses and thus provides insight about the competitive position in the
market environment.
11
As a result, BI provides an SME with a good overview
Illustration 1: Business intelligence leads to the
discovery of new opportunities
Source: Original scene from the video.
Business Intelligence
2
7
Cf. Olszak, C.M./ Ziemba, E. (2012), p. 130.
8
Cf. Canes, M. (2009): URL: see list of references.
9
Cf. Guarda, T. et al. (2013), p. 187f.
10
Cf. Frost, S. (w/o Y.), URL: see list of references.
11
Cf. Bose, R. (2007), p. 511f.
over threats and opportunities in the market, based on which it can derive
measurements for the improvement of its operations (see illustration 1).
12
Another advantage of BI is the ability to forecast trends.
13
Analytical techniques
of BI identify relationships or unusual patterns in the obtained data and are
hence able to visualize information that has not been recognized before.
14
By
the discovery of these anomalies, an enterprise becomes aware of emerging
trends and is able to adjust its strategy accordingly. The ability to discover pat-
terns and relationships data also holds advantages for the customer service.
Through the analysis of i.e. sales data, BI assesses the demography as well as
habits of the customers and determines correlating products or services. As a
result, BI offers valuable opportunities for up-selling
15
and cross-selling prod-
ucts
16
. This allows an enterprise to streamline its marketing efforts, resulting in
an improved customer experience and better use of resources.
17
3. Implementation
Researchers have proposed many different approaches for the design of a BI
process. This thesis will focus on the most commonly applied structure, which
consists of ?ve distinct phases (see illustration 2).
18
The process starts with the planning phase, in which the objectives for the
process are set.
19
At this point it is important that the decision makers, for
whose demand the process is ultimately created, communicate their intelli-
gence needs, as these establish the foundation for the later phases. Based on
Business Intelligence
3
12
Cf. Vedder, R.G. et al. (1999), p. 110.
13
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
14
Cf. Guarda, T. et al. (2013), p. 188.
15
up-selling is a sales strategy in which the seller encourages a customer to purchase more ex-
pensive items or upgrades for a higher sales pro?t
16
cross-selling is the encouragement of an existing customer to purchase an additional product
17
Cf. Williams, S./ Williams, N. (2007), p. 162.
18
Cf. McGonagle, J.J. (2007), p. 71.
19
Cf. Morcillo, P. (2003), URL: see list of references.
these needs, key intelligence topics (KITs) can be created, which point out the
topics of greatest interest for the achievement of the strategic goals of the
company.
20
Once the KITs are de?ned, the process continues with the collection phase, in
which the data has to be sourced and preprocessed. Here it is ?rst of all impor-
tant to match the available data sources with the KITs formulated in the plan-
ning phase.
21
This compliance with the intelligence needs is necessary to en-
sure that a correct decision can be taken based on the resourced data. Before
collecting the data, the source should thus furthermore always be checked for
its suitability.
22
For the collection itself, SMEs have a large variety of potential
sources at their disposal. They can i.e. source the data directly from interviews,
focus groups or executives. The most recommendable source, is probably,
however, the internet, as it has made research, i.e. on competitors, easy and
affordable. Here the SME has the option to connect with experts, customers
and suppliers or gather data from webpages, i.e. of competitors. While these
Illustration 2: The business intelligence process
Source: Own illustration based on Bose, R. (2007), p. 513; Guarda, T. et al. (w/o Y.), p. 187f.
De?ne
necessary
data
Collect data,
data pre-
processing,
data analysis
Data analysis,
Project results
Databases
Internal
Data
Sources
External
Data
Sources
Decision
Making
Re-initiation of Process
Data
Distribution
Feedback
Planning
Collection Dissemination Analysis Feedback
Business Intelligence
4
20
Cf. Krizan, L. (1999), URL: see list of references.
21
Cf. Bose, R. (2007), p. 513.
22
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
are all primary resources
23
, an SME can furthermore tap secondary resources
24
.
For this purpose search engines and online subscription databases have be-
come popular tools to collect data from commercial news organizations or
news ?ltering services.
25
Lastly the collected data needs to be re?ned and
structured for further analysis.
26
The next phase, the analysis phase, is the core stage and most critical part of
the BI process. It differs from the collection process in the way, that its purpose
is not to collect a set of data from diverse sources (i.e. internet, internal or ex-
ternal databases), but to illustrate the signi?cance of a prede?ned data set.
27
To
do so the re?ned data collected before is systematically examined, analyzed
and validated.
28
The main activity consists of mining the data
29
to identify pat-
terns and relationships for the extraction of actionable intelligence.
30
SMEs of-
ten encounter dif?culties performing this task, as they are not able to afford the
complex BI infrastructures of larger companies (see illustration 5). Nowadays,
however, there are many possible alternatives. Open source software for in-
stance is often available online at no cost and thus offers the SMEs an opportu-
nity to start mining data without committing to a large investment.
31
Once the intelligence has successfully been extracted, it needs to be dissemi-
nated. In this stage the extracted intelligence is reported back to management
through meetings, reports or dashboards, providing insight on the KITs. For this
purpose it is essential that the report is visualized an easy-to-understand format
so possible misunderstandings can be avoided.
32
Provided with the intelli-
gence on the KITs, the decision makers are then able to take action. Besides for
Business Intelligence
5
23
Primary sources are ?rst hand sources that provide direct evidence of a topic.
24
Secondary sources interpret or analyze primary sources and are thus one step remote from
the event.
25
Cf. Botha, A.P. (2007), p. 53.
26
Cf. Barnard, S. (w/o Y.), URL: see list of references.
27
Cf. Krizan, L. (1999), URL: see list of references.
28
Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
29
data mining is a set of activities to identify hidden relationships in data.
30
Cf. Miller, S.H. (2001), URL: see list of references.
31
Cf. Chen, X. et al. (2007), p. 4.
32
Cf. Miller, S.H. (2001), URL: see list of references.
decision making, the intelligence may also serve for further analyses such as
competitor pro?ling, scenario planning or scenario analyses.
The process is concluded with the feedback of the executives. It includes an
assessment of the quality and accuracy of the intelligence as well as guidance
for the analyst of how the process can be improved in the future.
33
A BI process
thus has to be understood as iterative in the sense that it permanently repeats
upon completion and is always subject to improvement.
34
4. Recommendations
As many enterprises are opposed to change or new technology, the support
from top management is a critical factor for the success of BI. A dedicated
management which endorses the BI process ensures ?nancial resources and
effective project management.
35
It is thus the responsibility for those involved
in an intelligence program to convince the senior executives of its usefulness.
They should be encouraged to use the system actively instead of contemplat-
ing it just as another reactive management resource.
36
Besides its endorsement
for BI as a system, it is important that the top management considers the intel-
ligence program an iterative process rather than a one time project.
37
This con-
tinuity is essential, as strategic planning requires long-term intelligence and a
continuous basis of information.
Closely connected to continuity are also other critical success factors. A culture
of trust and cross-organizational collaboration, i.e. is vital for the effective
knowledge exchange and thus critical to transform knowledge of an individual
into organizational knowledge. Especially in SMEs, that rely mostly on tacit
knowledge, knowledge workers can only be replaced to a certain extent. A col-
Business Intelligence
6
33
Cf. Bose, R. (2007), p. 514.
34
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
35
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
36
Cf. Global Intelligence Alliance (2004), URL: see list of references.
37
Cf. William, Y./ Koronios, A. (2010), p. 23ff.
laborative culture facilitates a healthy ?ow of information within the organiza-
tion, which is needed to make the collected and analyzed information about
customers, competition, market conditions, vendors, partners, products and
employees available at all levels.
38
A properly functioning BI process in this
sense also requires the utilization of tools and applications. They not only facili-
tate the collaboration by providing means to communicate the intelligence
within the organization, but furthermore encourage BI users to produce con-
tent themselves. In the end, it should, however, always be clear to an enterprise
that tools merely support the managing knowledge process and that BI can
consequently only be successful, if the knowledge and skills of the people are
used effectively.
39
Business Intelligence
7
38
Cf. Atre, S. (2003), URL: see list of references.
39
Cf. Global Intelligence Alliance (2004), URL: see list of references.
5. Attachments
Illustration 3: De?nition of Business Intelligence
Source: Own illustration.
Data Processing Data Sourcing Data Visualization
Data
Intelligence
Data Integration from
Departments and
External Sources
Intelligent Data
Analysis
Data
Dissemination
Internal
Data
Sources
External
Data
Sources
Business Intelligence
8
Source: Own illustration.
Illustration 4: The business intelligence value chain
Increasing
potential to
support
business
decisions
Data
Knowledge
Insight
Action
Information
Business Intelligence
9
Source: Own illustration based on Hannig, U. (2002), p. 6.
Illustration 5: The architecture of business intelligence
Internal Data Sources
(ERP Systems)
External Data Sources
(Internet)
Standardized
Reporting
Ad-hoc Re-
porting
Data
Mining
Meta-Data Bank
ETL-Tools
Extract
Transfer
- Convert
- Filter
- Aggregate
Load
OLAP Tools
Archive
Data Warehouse
Data Mart
Business Intelligence
10
6. Bibliography
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df (accessed 09/25/2013).
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analysis, in: Industrial Management and Data Systems, Vol. 108, No. 4, p. 510-
528.
Botha, A.P. (2007): Knowledge - living and working with it, Cape Town, South
Africa.
Canes, M. (2009): Business Intelligence for the SME, in:http://www.bluelinkerp.com/downloads/2009-10-15-Business-Intelligence-for-t
he-SME.pdf (online publication from 15/10/2009; accessed 09/23/2013).
Chen, X./ Ye, Y./ Williams, G./ Xu, X. (2007): A Survey of Open Source Data
Mining Systems, in: Washio, T./ Zhou, Z.H./ Huang, J.Z./ Hu, X./ Li, J./ Xie,
C./ He, J./ Zou, D./ Li, K.C./ Freire, M.M. (eds.) (2007): Emerging Technolo-
gies in Knowledge Discovery and Data Mining, Berlin; Heidelberg, Germany.
Frost, S. (w/o Y.): What Are the Bene?ts of a Competitive Intelligence Organi-
zation?, in:http://smallbusiness.chron.com/bene?ts-competitive-intelligence-organization-
33876.html (accessed 09/24/2013).
Global Intelligence Alliance (2004): Key Success Factors of Competitive In-
telligence, in:http://www.gcc-consulting.com/GIA+WhitePaper_KeySuccess
_2004-10-25[1].pdf (online publication from 04/2004; accessed 09/26/2013).
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telligence as a Competitive Advantage in SMEs, in: International Journal of
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Hannig, U. (2002): Knowledge Management und Business Intelligence, Berlin,
Germany et al.
Harper, D. (w/o. Y.): Surveying The Business Intelligence Space, in:http://modeladvisor.com/interesting_articles/business_inteligence.htm (ac-
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Krizan, L. (1999): “Intelligence essentials for everyone”, Occasional Paper Se-
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Guide to Productivity, London, UK.
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Sangar, A.B./ Iahad, N.B.A. (2013): Critical Factors That Affect The Success Of
Business Intelligence Systems (BIS) Implementation In An Organization, in: In-
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doc_842741028.pdf