Description
Evaluation of Business Intelligence Maturity Level in Albania Banking Systems
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
90
Evaluation of Business Intelligence Maturity Level in Albania Banking
Systems
Blerta Moçka
1*
, Gudar Beqiraj
2
and Daniel Leka
3
Faculty of Economy and Agribusiness, Agricultural University of Tirana, Albania; Kodër Kamëz, Tiranë, Albania
1
Academy of Sciences of Albania, Albania; Square Fan Noli, Tirane, Albania
2
Albanian Mobile Communications (AMC), Albania; St. Gjergj Legisi, Laprake, Tirane, Albania
3
Abstract
The banking service industry is changing rapidly. I t
is under the pressure of responding quickly to the
changing conditions and factors such as
globalization, deregulation, mergers and
acquisitions, competition from non-financial
institutions and technological innovation. Many
large banks in the world have been using Business
I ntelligence (BI ) computer software for some years
to help them gain competitive advantage. With the
introduction of cheaper and more generalized
products to the market place, BI is now in the reach
of smaller and medium sized companies. Guided by
these factors, the banking systems in Albania are
adopting Business I ntelligence (BI ) technologies,
Data Warehouse (DW) and systems that help the
decision making process. BI is the process of
transforming raw data into meaningful information
to enable more effective business insight and
decision-making. This study examine the key
components on improving BI operations and the
benefits gained from Albanian banking industry in
using Business I ntelligence. The detailed evaluation
in the maturity level of Albanian's banking systems
BI activities is the goal of this research paper.
Keywords
Business I ntelligence (BI ), Data Warehouse, Banking
systems and CMMI model.
1. Introduction
Nowadays, banks and financial institutions have over
the years compiled huge amounts of business and
customer data into large electronic repositories. They
have large volumes of detailed operational data, but
key business analysts and decision makers still
cannot get the answers they need to react quickly
*Author for correspondence
enough to the changing conditions given that the data
are spread across many departments in the
organization or are locked in a sluggish technology
environment.
Is the maintenance of the data an obligation, an asset
or just another regular task for them? This data will
always be a liability as long as it is locked inside the
repositories. This information is the most important
asset of any organization. To turn data into an asset,
its inherent value needs to be stored and analyzed.
There are two main purposes in the use of this asset:
? Data capturing for operational record
? Data analyzing for decision making
The first one, is optimized to process everyday
transaction quickly. It registers clients, take orders,
monitors the status of operational activities, and other
operations in bank. The employers of an operational
system turn the wheels of the organization.
Operational systems do not maintain history they are
used to put the data in and to reflect the most current
state of the organization.
The second one is where we get the data out to
evaluate the performance of the organization. For this
purpose Business Intelligence (BI) systems are used
to count the new client and compare them with last
week’s results, and ask what the new customers want
and what they are complaining about. It can help
banks to improve products, enhance customer
relationships, make better forecasts based on the past
trends, handle competition, manage risk, increase
operational efficiency. BI outputs give organizations
a better understanding of their present circumstances,
so that they may take the right course of action in the
future.
BI is a set of tools, technologies and solutions
designed for end users to efficiently extract useful
business information from a set of data. Institutions
that are interested in implementing efficient BI
capabilities require much more than just the
collection and storage of data. Banks and technology
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
91
vendors need to bring many moving pieces together.
This includes the right sourcing strategy, software
applications, technology tools, business processes,
collected data, company metrics,
incentives, corporate culture and project management
skills (Datamonitor, 2007 [5]).
2. Business Intelligence
In literature, BI is both defined as a process and a
product. The process is “composed of methods that
organizations use to develop useful information, or
intelligence, that can help organizations survive and
thrive” (Jourdan, Rainer, and Marshall, 2008 [10]).
The product is the information that allows
organizations to more accurately understand current
and predict future behaviors of “competitors,
suppliers, customers, technologies, acquisitions,
markets, products and services, and the general
business environment” (Vedder, Vanecek, Guynes,
and Cappel, 1999 [17]). Successful organizations
improve the value of their customer base by reducing
the rate of defections, increasing the longevity of the
relationship and enhancing the growth potential of
each customer (Kotler and Keller, 2009 [11]). BI can
be used to mine customer relationships (Phan and
Vogel, 2010 [16]), identify profitable customers and
facilitate retaining them by understanding individual
behaviors (Jaffri & Nadeem, 2004 [9]).
Organizations can gain a competitive advantage with
successful implementations of BI (Jaffri & Nadeem,
2004 [9]), by recognizing “their raw transactional
data as a valuable source of unique information”
(Gunnarsson, Walker, Walatka, & Swann, 2007 [7]).
Given that banks receive a vast amount of
information from different resources, the main
problem in taking the operational decision is to focus
on the right information. In today’s rapidly changing
business environment, organizational resourcefulness
depends on operational monitoring of how the
business is performing and mostly on the prediction
of the future outcomes which are critical for a
sustainable competitive position. Intelligence
becomes an asset only if it is used (Flud, 2003 [6]).
Implementing a BI system can help to identify the
causes and reasons of certain occurrences thus,
helping the business to make predictions, calculations
and analyses; so that the needed knowledge is
successfully extracted from the data and that the
proper decisions are made. BI consists of a wide
range of analytically software's that provide the
information necessary for every user of the business,
such as analyzers, managers and operators to make a
better decision. The information is in real-time and
supports reporting on every organizational level.
According to Blomme, traditionally, BI systems
provide a retrospective view of the business by
querying data warehouse which contain historical
data. On the contrary, contemporary BI-systems
analyze real-time event streams in memory (Blomme,
2010 [1]).
BI is implemented to give users access to information
in their systems in an automatic and efficient way.
The users need not to have any technical knowledge
of the underlying system because all the gathered
data are performed automatically by the BI systems
(Ritacco, 2003 [13]).
3. BI technology in banking
The financial service industry is influenced by some
factors such as globalization, integration, growing
competition, product and market innovations, re-
engineering of processes, and other trends. Financial
institutions must also manage risk and comply with
regulatory requirements such as Basel II accord and
IAS (Curko, 2007 [4]). To be successful, financial
institutions must (Howson, 2008 [8]):
? Monitor all aspects of client relations;
? Identify and retain the most profitable
customers;
? Attract new customers from competition;
? Correctly measure products’ and
organizational productivity;
? Recognize new markets and needs for new
products.
Efficient Business Intelligence connects business
with information technology (IT) so that the available
resources can be allocated with respect to their own
capabilities, as well as provides intelligent problem
solutions (Ranjan, 2008 [12]). Figure 1 describes the
BI environment, which integrates many of the
business processes (ERP, CRM, etc) into multiple
applications that serve the primary source of data.
Once the data are gathered and stored in e DW they
can be easily analyzed with the help of BI tools, such
as reports, OLAP, and data mining. These analytic
tools have the potential to provide actionable
information that can be turned into valuable
information on which the companies base their
decisions.
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
92
Fig 1: BI environment
ETL and Data
Integration
DW
Reporting Query OLAP
Data
Mining
CRM
Analytics
Financial
Analytics
Operational
Analytics
ERP SCM CRM
Custom
Apps.
External
Servers
Storage
M
e
t
a
d
a
t
a
M
a
n
a
g
e
m
e
n
t
S
e
c
u
r
i
t
y
Analytic
application
BI tools
DW
management
tools
Extraction,
Transformati
on, Load and
Data
Integration
tools
Transaction
Processing
Applications
Hardware
platform
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
93
With appropriate planning, banks can move their
operational database data into a data warehouse and
then further exploit that data with OLAP and data
mining techniques to create a strong Business
Intelligence solution and increased value for their
membership. (O’Brien, 2011 [15]).
4. BI Maturity model
To evaluate the application of BI solution for banking
systems in Albania one maturity model CMMI is
used (Capability Maturity model Integration) (CMMI
Product Team SEI, 2010 [3]). This maturity model
measure and track progress in the bank organization
that is using BI (Figure 2).
Figure 2: The maturity level (Carienge Mellou,
2005 [2])
At Initial, maturity level 1, processes are usually ad
hoc and chaotic. The organization usually does not
provide a stable environment to support processes.
At Managed, maturity level 2, work groups establish
the foundation for an organization to become an
effective service provider by institutionalizing
selected Project and Work Management, Support,
and Service Establishment and Delivery processes.
Work groups define a service strategy, create work
plans, and monitor and control the work to ensure the
service is delivered as planned.
At Defined, maturity level 3, service providers use
defined processes for managing the work. They
embed tenets of project and work management and
services of the best practices, such as service
continuity and incident resolution and prevention,
into the standard process set.
At Quantitatively Managed, maturity level 4, service
providers establish quantitative objectives for quality
and process performance and use them as criteria in
managing processes. Quantitative objectives are
based on the needs of the customer, end users,
organization, and process implemented.
At Optimizing, maturity level 5, an organization
continually improves its processes based on a
quantitative understanding of its business objectives
and performance needs. The organization uses a
quantitative approach to understand the variation
inherent in the process and the causes of process
outcomes.
Since BI is a process, we used CMMI model for
testing the improvement of banks in this process.
This process has three levels: acquiring the data,
analyzing the data, and taking action based on the
data. These levels are used as independent variables
and BI maturity level is considered as dependent
variable.
5. Analyses questionnaire and result
To collect the needed data we used e questionnaire. It
is valid and reliable. The model which is used for the
BI processes modeling is as shown in Tab. 1(Najmi,
M. Sepehri, M. & Hashemi, S. 2010 [14]).
Table 1: The sub processes of BI
Factors Construct
Acquire the data Data gathering
Extraction
Transformation
Data storage
Data warehouse
Analyze the data Reporting and dashboard
Online Analytical Processing
OLTP
Data Mining
Take action based
on the data
Business strategist
Therefore we just need to know what the maturing
level of these processes in Albanian Banking is. Each
main process and sub process is assessed according
to CMMI maturity levels. For each question at a
structured questionnaire, we used a scale from 0 to 5,
as defined by CMMI. Here are the results calculated
in MS Excel.
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
94
5.1 Process of data acquisition
At this phase Data are extracted from this
environment and stored in the data warehouse
describes in Figure 3.
Figure 3: Chart for the process of acquiring the
data
? Data gathering is the process of gathering
the data from different sources. The data in
Albanian banking are gathered through
ATM, POS, client, and the process is done
automatically and systematically.
? Data extraction is the process of receiving
data from the operational environment, like
transnational tables or NoSQL database for
moving data into the warehouse. In Albania
banking there is not any automatic process
or specific software, but they are very
interested in this process.
? Data transformation is the process of
converting data from different systems and
formats into one consistent format. In
Albania banking this transformation is done
through different applications, which are not
integrated as they should.
? Data cleaning is the process of removing
errors from the input stream. In banking this
process is done just for financial purpose
twice a year.
? Data storage contains the row data of the
data warehouse. This process needs to be
done automatic. It includes regular control,
backup and recovery, monitoring every 24
hour and is considered completely.
? Data warehouse is not a unique and
integrated in all Albanian banking industry.
This activity is done by different tools for
different applications
5.2 The process of analyzing the data
This phase regains data and presents them to the
decision maker (Figure 4).
Figure 4: Chart for the process of data analysis
? Reporting and dashboard tool need to be as
clear and straightforward as possible to get
the data to employees who may want it. In
Albanian banks, that is not any integrated
system for extracting any report from that.
There are systems in some banks that extract
cart switch transaction efficiency
information from that.
? OLAP (OnLine Analytical Processing)
permits the business person to present the
data in multiple dimensions at time. In
Albanian banks there are not specific
analytically processes, but analyses are done
by statistical tools or by manager according
to the reports.
? OLTP (OnLine Transaction Processing)
refers to processing and responding
immediately to the user request by the
system. An ATM (Automatic Teller
Machine) is an example of this. OLTP
systems are implemented in Albanian
banking as the main database and all the
processes are doing online on the data.
OLTP should be monitored all the time and
should be updated regularly.
? Data mining in Albanian banking industry is
not integrated as classified and estimated.
5.3 The process of taking action based on the
data
This phase is the main factor of BI process. Decision
making process takes information from BI tools and
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
95
defines some course of action. Decision maker of the
organization can be considered lower employees,
partners and customers. The figure below show the
entire questionnaires with the average value
calculated. For each phase is the output and the
maturity score.
Figure 5: BI process maturity in Albanian
banking
As it is shown all levels of the process are weighed
3.3 to 4.0 which mean that the BI process in Albanian
banking industry is between level three and four,
Defined and Quantitatively Managed, of CMMI.
6. Conclusions
According to the results from the questionnaires the
maturity level of BI in Albanian banking industry is
at level three and four. It means that we have some
defined processes in the implementation of BI for
doing BI and they are established quantitatively. BI
includes three main processes of:
? Acquiring the data is at level four. It
means that there are some measured and
controlled processes for that. In more details
we found out that data gathering, extraction,
transformation and storing are better
managed and applied. But data warehouse
process is at level three, which means it is
defined but it still needs to be more
quantitatively managed.
? Analyzing the data, is between level three
and four. It means that sub processes are
well defined and becoming better managed.
The worse sub process is data mining.
? Taking action based on data is at level
three. The banks are interested in analyzing
and taking actions based on the analyses of
the data. Practice processes are established
in these banks.
In Albania there is only one public bank and the
others are private. The research shows the maturity
level of BI sub processes in the banking industry. A
picture of each sub process is given in figure 6, where
we can understand the worst and the best BI sub
processes.
Figure 6: BI maturity level based on its sub
processes
Considering that this research has started many years
ago, we can definitely emphases that Albanian banks
are embracing BI solution for their needs, but still
there isn't a full integration BI framework in banks IT
infrastructure. As clearly described during this
article, the goal was to provide a snapshoot of the
current Business Intelligence usage in Albanian
banking systems. The only suggestion that we could
give for each bank is to improve the data warehouse
as the key sub processes of BI, doing so they can
improve their maturity level of BI. This opens new
opportunity to present BI solution to Albanian
banking systems from BI vendors.
References
[1] Blomme, J. (2010), “The new normal in Business
Intelligence”
http://www.slideshare.net/johblom/the-new-
normal-in-business-intelligence (Accessed
17.04.2014).
[2] Carienge Mellou (2005), “Capability Maturity
Model Integration (CMMI) Overview”
http://elsmar.com/pdf_files/cmmi-
overview05.pdf .
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
96
[3] CMMI Product Team, (2010) “CMMI for
Services, Version 1.3”, Software Engineering
Institute,
http://www.sei.cmu.edu/reports/10tr034.pdf .
[4] Curko, K., Pejic Bach, M. and Radonic, G (2007)
“Business Intelligence and Business Process
Management in Banking Operations”, 29th
International Conference on Information
Technology Interfaces.
[5] Datamonitor "Business Intelligence in Retail
Banking (Review Report) Business Intelligence
across the enterprise." (2007) DMTC2167,
Datamonitor. (Accessed on 13.05.2014).
[6] Fuld, L. (2003). "Be Prepared," Harvard Business
Review, 3: 20-21.
[7] Gunnarsson, C. L., Walker, M. M., Walatka, V.,
and Swann, K.(2007) “Lessons learned: A case
study using data mining in the newspaper
industry.”
[8] Howson, C. (2008). Techno Babble: Components
of Business Intelligence Architecture.
http://www.b-eye-network.com/view/7105
(accessed 17.02.2013).
[9] Jaffri, S. A. H., & Nadeem, M. (2004)
“Application of business intelligence in banks”.
[10] Jourdan, Z., Rainer, R. K., & Marshall, T. E.
(2008). “Business intelligence: An analysis of the
literature. Information Systems Management”.
[11] Kotler, P., and Keller, K. L. (2009). Marketing
management (13th ed.). Upper Saddle River N.J.:
Pearson Prentice Hall.
[12] Ranjan, J. (2008) "Business justification with
business intelligence", The Journal of
Information and Knowledge Management
Systems, Vol. 38, No. 4, pg. 461-475.
[13] Ritacco, M. and Carver, A. (2003) “The Business
Value of Business Intelligence”,
www.businessobject.com (accessed on
13.01.2014).
[14] Najmi, M. Sepehri, M. & Hashemi, S. "The
evaluation of Business Intelligence maturity level
in Iranian banking industry", 10.29.2010, Pg.
466-470, IW&EM, IEEE 17th International.
[15] O’Brien, E. (2011). “Using Business Intelligence
to Leverage Operational Data in Support of
Membership and Asset Growth in Credit
Unions.”
[16] Phan, D. D., and Vogel, D. R. (2010). A model of
customer relationship management and business
intelligence systems for catalogue and online
retailers. Information & Management.
[17] Vedder, R. G., Vanecek, M. T., Guynes, C. S., &
Cappel, J. J. (1999). CEO and CIO perspectives
on competitive intelligence. Communications of
the ACM.
Blerta MOÇKA graduated on
Computer Science at the Faculty of
Natural Sciences where she obtained
her MSc in 2009. Joined the PhD
program March 2012. Interested in the
field of Business Intelligence, Database
and Programming. Current position
Lecturer at Faculty of Economics and
Agribusiness, Agricultural University of Tirana and part
time lecturer at Faculty of Natural Sciences.
Email: [email protected]
Gudar BEQIRAJ is Vice President of
the Academy of Sciences of Albania.
His research activity includes
development of different algorithms
and programs applied in Albania in
domains such as geology, geophysics,
agriculture, medicine, and other.
Publications: Books in the field of
informatics and its application in geophysics, many
scientific articles, two books on the sport.
Daniel LEKA graduated on MSc
Information Technology at the
Pyrotechnical University of Tirana.
Current position Smart Metering (SM)
Operation Coordinator at SM Pilot for
Albanian Distributor Electrical Energy,
AMC. His research activity includes
Cloud Computing, Virtualization,
Business Analysis, Data Analytics, Open Source,
Management and Quality asurancces.
doc_685328107.pdf
Evaluation of Business Intelligence Maturity Level in Albania Banking Systems
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
90
Evaluation of Business Intelligence Maturity Level in Albania Banking
Systems
Blerta Moçka
1*
, Gudar Beqiraj
2
and Daniel Leka
3
Faculty of Economy and Agribusiness, Agricultural University of Tirana, Albania; Kodër Kamëz, Tiranë, Albania
1
Academy of Sciences of Albania, Albania; Square Fan Noli, Tirane, Albania
2
Albanian Mobile Communications (AMC), Albania; St. Gjergj Legisi, Laprake, Tirane, Albania
3
Abstract
The banking service industry is changing rapidly. I t
is under the pressure of responding quickly to the
changing conditions and factors such as
globalization, deregulation, mergers and
acquisitions, competition from non-financial
institutions and technological innovation. Many
large banks in the world have been using Business
I ntelligence (BI ) computer software for some years
to help them gain competitive advantage. With the
introduction of cheaper and more generalized
products to the market place, BI is now in the reach
of smaller and medium sized companies. Guided by
these factors, the banking systems in Albania are
adopting Business I ntelligence (BI ) technologies,
Data Warehouse (DW) and systems that help the
decision making process. BI is the process of
transforming raw data into meaningful information
to enable more effective business insight and
decision-making. This study examine the key
components on improving BI operations and the
benefits gained from Albanian banking industry in
using Business I ntelligence. The detailed evaluation
in the maturity level of Albanian's banking systems
BI activities is the goal of this research paper.
Keywords
Business I ntelligence (BI ), Data Warehouse, Banking
systems and CMMI model.
1. Introduction
Nowadays, banks and financial institutions have over
the years compiled huge amounts of business and
customer data into large electronic repositories. They
have large volumes of detailed operational data, but
key business analysts and decision makers still
cannot get the answers they need to react quickly
*Author for correspondence
enough to the changing conditions given that the data
are spread across many departments in the
organization or are locked in a sluggish technology
environment.
Is the maintenance of the data an obligation, an asset
or just another regular task for them? This data will
always be a liability as long as it is locked inside the
repositories. This information is the most important
asset of any organization. To turn data into an asset,
its inherent value needs to be stored and analyzed.
There are two main purposes in the use of this asset:
? Data capturing for operational record
? Data analyzing for decision making
The first one, is optimized to process everyday
transaction quickly. It registers clients, take orders,
monitors the status of operational activities, and other
operations in bank. The employers of an operational
system turn the wheels of the organization.
Operational systems do not maintain history they are
used to put the data in and to reflect the most current
state of the organization.
The second one is where we get the data out to
evaluate the performance of the organization. For this
purpose Business Intelligence (BI) systems are used
to count the new client and compare them with last
week’s results, and ask what the new customers want
and what they are complaining about. It can help
banks to improve products, enhance customer
relationships, make better forecasts based on the past
trends, handle competition, manage risk, increase
operational efficiency. BI outputs give organizations
a better understanding of their present circumstances,
so that they may take the right course of action in the
future.
BI is a set of tools, technologies and solutions
designed for end users to efficiently extract useful
business information from a set of data. Institutions
that are interested in implementing efficient BI
capabilities require much more than just the
collection and storage of data. Banks and technology
I nternational J ournal of Advanced Technology and Engineering Exploration
I SSN (Print): 2394-5443 I SSN (Online): 2394-7454
Volume-2 I ssue-7 J une-2015
91
vendors need to bring many moving pieces together.
This includes the right sourcing strategy, software
applications, technology tools, business processes,
collected data, company metrics,
incentives, corporate culture and project management
skills (Datamonitor, 2007 [5]).
2. Business Intelligence
In literature, BI is both defined as a process and a
product. The process is “composed of methods that
organizations use to develop useful information, or
intelligence, that can help organizations survive and
thrive” (Jourdan, Rainer, and Marshall, 2008 [10]).
The product is the information that allows
organizations to more accurately understand current
and predict future behaviors of “competitors,
suppliers, customers, technologies, acquisitions,
markets, products and services, and the general
business environment” (Vedder, Vanecek, Guynes,
and Cappel, 1999 [17]). Successful organizations
improve the value of their customer base by reducing
the rate of defections, increasing the longevity of the
relationship and enhancing the growth potential of
each customer (Kotler and Keller, 2009 [11]). BI can
be used to mine customer relationships (Phan and
Vogel, 2010 [16]), identify profitable customers and
facilitate retaining them by understanding individual
behaviors (Jaffri & Nadeem, 2004 [9]).
Organizations can gain a competitive advantage with
successful implementations of BI (Jaffri & Nadeem,
2004 [9]), by recognizing “their raw transactional
data as a valuable source of unique information”
(Gunnarsson, Walker, Walatka, & Swann, 2007 [7]).
Given that banks receive a vast amount of
information from different resources, the main
problem in taking the operational decision is to focus
on the right information. In today’s rapidly changing
business environment, organizational resourcefulness
depends on operational monitoring of how the
business is performing and mostly on the prediction
of the future outcomes which are critical for a
sustainable competitive position. Intelligence
becomes an asset only if it is used (Flud, 2003 [6]).
Implementing a BI system can help to identify the
causes and reasons of certain occurrences thus,
helping the business to make predictions, calculations
and analyses; so that the needed knowledge is
successfully extracted from the data and that the
proper decisions are made. BI consists of a wide
range of analytically software's that provide the
information necessary for every user of the business,
such as analyzers, managers and operators to make a
better decision. The information is in real-time and
supports reporting on every organizational level.
According to Blomme, traditionally, BI systems
provide a retrospective view of the business by
querying data warehouse which contain historical
data. On the contrary, contemporary BI-systems
analyze real-time event streams in memory (Blomme,
2010 [1]).
BI is implemented to give users access to information
in their systems in an automatic and efficient way.
The users need not to have any technical knowledge
of the underlying system because all the gathered
data are performed automatically by the BI systems
(Ritacco, 2003 [13]).
3. BI technology in banking
The financial service industry is influenced by some
factors such as globalization, integration, growing
competition, product and market innovations, re-
engineering of processes, and other trends. Financial
institutions must also manage risk and comply with
regulatory requirements such as Basel II accord and
IAS (Curko, 2007 [4]). To be successful, financial
institutions must (Howson, 2008 [8]):
? Monitor all aspects of client relations;
? Identify and retain the most profitable
customers;
? Attract new customers from competition;
? Correctly measure products’ and
organizational productivity;
? Recognize new markets and needs for new
products.
Efficient Business Intelligence connects business
with information technology (IT) so that the available
resources can be allocated with respect to their own
capabilities, as well as provides intelligent problem
solutions (Ranjan, 2008 [12]). Figure 1 describes the
BI environment, which integrates many of the
business processes (ERP, CRM, etc) into multiple
applications that serve the primary source of data.
Once the data are gathered and stored in e DW they
can be easily analyzed with the help of BI tools, such
as reports, OLAP, and data mining. These analytic
tools have the potential to provide actionable
information that can be turned into valuable
information on which the companies base their
decisions.
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Fig 1: BI environment
ETL and Data
Integration
DW
Reporting Query OLAP
Data
Mining
CRM
Analytics
Financial
Analytics
Operational
Analytics
ERP SCM CRM
Custom
Apps.
External
Servers
Storage
M
e
t
a
d
a
t
a
M
a
n
a
g
e
m
e
n
t
S
e
c
u
r
i
t
y
Analytic
application
BI tools
DW
management
tools
Extraction,
Transformati
on, Load and
Data
Integration
tools
Transaction
Processing
Applications
Hardware
platform
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With appropriate planning, banks can move their
operational database data into a data warehouse and
then further exploit that data with OLAP and data
mining techniques to create a strong Business
Intelligence solution and increased value for their
membership. (O’Brien, 2011 [15]).
4. BI Maturity model
To evaluate the application of BI solution for banking
systems in Albania one maturity model CMMI is
used (Capability Maturity model Integration) (CMMI
Product Team SEI, 2010 [3]). This maturity model
measure and track progress in the bank organization
that is using BI (Figure 2).
Figure 2: The maturity level (Carienge Mellou,
2005 [2])
At Initial, maturity level 1, processes are usually ad
hoc and chaotic. The organization usually does not
provide a stable environment to support processes.
At Managed, maturity level 2, work groups establish
the foundation for an organization to become an
effective service provider by institutionalizing
selected Project and Work Management, Support,
and Service Establishment and Delivery processes.
Work groups define a service strategy, create work
plans, and monitor and control the work to ensure the
service is delivered as planned.
At Defined, maturity level 3, service providers use
defined processes for managing the work. They
embed tenets of project and work management and
services of the best practices, such as service
continuity and incident resolution and prevention,
into the standard process set.
At Quantitatively Managed, maturity level 4, service
providers establish quantitative objectives for quality
and process performance and use them as criteria in
managing processes. Quantitative objectives are
based on the needs of the customer, end users,
organization, and process implemented.
At Optimizing, maturity level 5, an organization
continually improves its processes based on a
quantitative understanding of its business objectives
and performance needs. The organization uses a
quantitative approach to understand the variation
inherent in the process and the causes of process
outcomes.
Since BI is a process, we used CMMI model for
testing the improvement of banks in this process.
This process has three levels: acquiring the data,
analyzing the data, and taking action based on the
data. These levels are used as independent variables
and BI maturity level is considered as dependent
variable.
5. Analyses questionnaire and result
To collect the needed data we used e questionnaire. It
is valid and reliable. The model which is used for the
BI processes modeling is as shown in Tab. 1(Najmi,
M. Sepehri, M. & Hashemi, S. 2010 [14]).
Table 1: The sub processes of BI
Factors Construct
Acquire the data Data gathering
Extraction
Transformation
Data storage
Data warehouse
Analyze the data Reporting and dashboard
Online Analytical Processing
OLTP
Data Mining
Take action based
on the data
Business strategist
Therefore we just need to know what the maturing
level of these processes in Albanian Banking is. Each
main process and sub process is assessed according
to CMMI maturity levels. For each question at a
structured questionnaire, we used a scale from 0 to 5,
as defined by CMMI. Here are the results calculated
in MS Excel.
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5.1 Process of data acquisition
At this phase Data are extracted from this
environment and stored in the data warehouse
describes in Figure 3.
Figure 3: Chart for the process of acquiring the
data
? Data gathering is the process of gathering
the data from different sources. The data in
Albanian banking are gathered through
ATM, POS, client, and the process is done
automatically and systematically.
? Data extraction is the process of receiving
data from the operational environment, like
transnational tables or NoSQL database for
moving data into the warehouse. In Albania
banking there is not any automatic process
or specific software, but they are very
interested in this process.
? Data transformation is the process of
converting data from different systems and
formats into one consistent format. In
Albania banking this transformation is done
through different applications, which are not
integrated as they should.
? Data cleaning is the process of removing
errors from the input stream. In banking this
process is done just for financial purpose
twice a year.
? Data storage contains the row data of the
data warehouse. This process needs to be
done automatic. It includes regular control,
backup and recovery, monitoring every 24
hour and is considered completely.
? Data warehouse is not a unique and
integrated in all Albanian banking industry.
This activity is done by different tools for
different applications
5.2 The process of analyzing the data
This phase regains data and presents them to the
decision maker (Figure 4).
Figure 4: Chart for the process of data analysis
? Reporting and dashboard tool need to be as
clear and straightforward as possible to get
the data to employees who may want it. In
Albanian banks, that is not any integrated
system for extracting any report from that.
There are systems in some banks that extract
cart switch transaction efficiency
information from that.
? OLAP (OnLine Analytical Processing)
permits the business person to present the
data in multiple dimensions at time. In
Albanian banks there are not specific
analytically processes, but analyses are done
by statistical tools or by manager according
to the reports.
? OLTP (OnLine Transaction Processing)
refers to processing and responding
immediately to the user request by the
system. An ATM (Automatic Teller
Machine) is an example of this. OLTP
systems are implemented in Albanian
banking as the main database and all the
processes are doing online on the data.
OLTP should be monitored all the time and
should be updated regularly.
? Data mining in Albanian banking industry is
not integrated as classified and estimated.
5.3 The process of taking action based on the
data
This phase is the main factor of BI process. Decision
making process takes information from BI tools and
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defines some course of action. Decision maker of the
organization can be considered lower employees,
partners and customers. The figure below show the
entire questionnaires with the average value
calculated. For each phase is the output and the
maturity score.
Figure 5: BI process maturity in Albanian
banking
As it is shown all levels of the process are weighed
3.3 to 4.0 which mean that the BI process in Albanian
banking industry is between level three and four,
Defined and Quantitatively Managed, of CMMI.
6. Conclusions
According to the results from the questionnaires the
maturity level of BI in Albanian banking industry is
at level three and four. It means that we have some
defined processes in the implementation of BI for
doing BI and they are established quantitatively. BI
includes three main processes of:
? Acquiring the data is at level four. It
means that there are some measured and
controlled processes for that. In more details
we found out that data gathering, extraction,
transformation and storing are better
managed and applied. But data warehouse
process is at level three, which means it is
defined but it still needs to be more
quantitatively managed.
? Analyzing the data, is between level three
and four. It means that sub processes are
well defined and becoming better managed.
The worse sub process is data mining.
? Taking action based on data is at level
three. The banks are interested in analyzing
and taking actions based on the analyses of
the data. Practice processes are established
in these banks.
In Albania there is only one public bank and the
others are private. The research shows the maturity
level of BI sub processes in the banking industry. A
picture of each sub process is given in figure 6, where
we can understand the worst and the best BI sub
processes.
Figure 6: BI maturity level based on its sub
processes
Considering that this research has started many years
ago, we can definitely emphases that Albanian banks
are embracing BI solution for their needs, but still
there isn't a full integration BI framework in banks IT
infrastructure. As clearly described during this
article, the goal was to provide a snapshoot of the
current Business Intelligence usage in Albanian
banking systems. The only suggestion that we could
give for each bank is to improve the data warehouse
as the key sub processes of BI, doing so they can
improve their maturity level of BI. This opens new
opportunity to present BI solution to Albanian
banking systems from BI vendors.
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Volume-2 I ssue-7 J une-2015
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Blerta MOÇKA graduated on
Computer Science at the Faculty of
Natural Sciences where she obtained
her MSc in 2009. Joined the PhD
program March 2012. Interested in the
field of Business Intelligence, Database
and Programming. Current position
Lecturer at Faculty of Economics and
Agribusiness, Agricultural University of Tirana and part
time lecturer at Faculty of Natural Sciences.
Email: [email protected]
Gudar BEQIRAJ is Vice President of
the Academy of Sciences of Albania.
His research activity includes
development of different algorithms
and programs applied in Albania in
domains such as geology, geophysics,
agriculture, medicine, and other.
Publications: Books in the field of
informatics and its application in geophysics, many
scientific articles, two books on the sport.
Daniel LEKA graduated on MSc
Information Technology at the
Pyrotechnical University of Tirana.
Current position Smart Metering (SM)
Operation Coordinator at SM Pilot for
Albanian Distributor Electrical Energy,
AMC. His research activity includes
Cloud Computing, Virtualization,
Business Analysis, Data Analytics, Open Source,
Management and Quality asurancces.
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