Abstract. Business Intelligence is something which is used in many business
oriented applications. What is different between Decision Support Systems and
Business Intelligence? In this paper we would like to describe in detail just
what Business Intelligence is and make clear the difference between Business
Intelligence and Decision Support System by examining their definitions and their
components. Moreover, the study case is also represented as an introduction to
show how it is used in Enterprise applications. The BI is used for Cau Giay District
People’s Committee Information System as OLAP cube in many dimension to
serve many users making business decisions.
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JOURNAL OF SCIENCE OF HNUE
FIT., 2013, Vol. 58, pp. 79-87
This paper is available online at
BUSINESS INTELLIGENT: A COMBINATION
OF USE DATA MINING TOOLS IN BUSINESS PROCESS
Nguyen Thi Thu Thuy∗ and Hoang Quoc Minh
Economic Information System Faculty, Vietnam University of Commerce
∗E-mail: nguyentthuthuy@gmail.com
Abstract. Business Intelligence is something which is used in many business
oriented applications. What is different between Decision Support Systems and
Business Intelligence? In this paper we would like to describe in detail just
what Business Intelligence is and make clear the difference between Business
Intelligence and Decision Support System by examining their definitions and their
components. Moreover, the study case is also represented as an introduction to
show how it is used in Enterprise applications. The BI is used for Cau Giay District
People’s Committee Information System as OLAP cube in many dimension to
serve many users making business decisions.
Keywords: Business Intelligence (BI), Decision Support System (DSS), Data
Mining, Business Decision.
1. Introduction
Data Mining is a powerful tool that is used in business, bioinformatics, healthcare,
etc. It is used, for the most part, to generate alternative models for decision making. Even
though data mining has been successfully used in the formulation of various business
processes as well as to transfer innovations from academic research into the business
world, the gap between the problems that the research community works on and real-world
problems is significant. With Business Intelligence (BI), data mining tools are used in
the process of making business decisions. In 1989, Howard Dresner [10] used the term
“Business Intelligence” to describe systems that help decision makers understand the state
of their company’s world. Since then, there has been a steady growth in the use of both BI
solutions and their adoption and usage. While large organizations were quick to embrace
BI, with the availability of online SAAS solutions and Cloud, small and medium size
businesses have also embraced business intelligence as a key differentiator [10].
Demand for Business Intelligence (BI) applications continues to grow even at a
time when demand for most information technology (IT) products is soft [8], [9]. Business
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Nguyen Thi Thu Thuy, Hoang Quoc Minh
Intelligence systems combine operational data with analytical tools, such as Data Mining
tools, to present complex and competitive information to planners and decision makers.
Business Intelligence is used to understand the capabilities available in the firm; the
state of the art, trends, and future directions in the markets, the technologies and the
regulatory environment in which the firm competes; and the actions of competitors and
the implications of these actions. The emergence of the data warehouse as a repository,
advances in data cleansing, increased capabilities of hardware and software and the
emergence of the web architecture all combine to create a richer business intelligence
environment than was available previously. Although business intelligence systems are
widely used in industry, research in these systems is limited. An explanation of the nature
of BI and applications in possible use of BI by the Cau Giay District People’s Committee
is also proposed.
2. Content
2.1. Business Intelligence
2.1.1. What is Business Intelligence?
Business intelligence is a relatively new term in information technology and the
meaning of business intelligence differs from context to context. The term was first used
by Gartner and popularized by analyst Howard Dresner [10]. It describes the process of
turning data into information and then into knowledge. The claim is that such intelligence
is more useful to the user as it passes through each step. BI describes a set of concepts
and methods to improve business decision making by using fact based support systems.
Gartners’s definition of business intelligence includes all the ways in which an enterprise
can explore, access and analyze information in the data warehouse to develop insights that
lead to improved, informed decisions.
According to whatis.com “Business Intelligence (BI) is a broad category of
applications and technologies for gathering, storing, analyzing, and providing access to
data to help Enterprise users make better business decisions”. “Business intelligence (BI)
is also defined as the ability of an organization to take all its capabilities and convert
them into knowledge. This produces large amounts of information which can lead to
the development of new opportunities for the organization. When these opportunities
are identified and a strategy has been effectively implemented, they can provide an
organization with a competitive advantage in the market and stability in the long run
(within its industry)” [1], [7].
It can be seen that BI is defined as a decision support system (DSS). However,
many Vietnamese people do not understand that BI is another name for DSS when used
in business. So, what is difference between the two? Let go back to the definition of DSS:
According to Whatis.com: “A decision support system (DSS) is a computer program
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Business intelligent: a combination of use data mining tools in business process
application that analyzes business data and presents it so that users can make business
decisions more easily.” Also: “A decision support system (DSS) is a computer-based
information system that supports business or organizational decision-making activities.”
(Wikipedia). According to [6], DSS dates back to the late 1960s and was used in
theory developments in the 1970s and, in the early and mid 1980s, it was used in the
implementation of financial planning systems, spreadsheet-based DSS and Group DSS.
Data warehouses, Executive Information Systems, OLAP and Business Intelligence all
evolved in the late 1980s and early 1990s. The field of computerized decision support is
expanding to use new technologies and to create new applications. Therefore, BI can be
seen as a part of a field in the DSS area. We can see BI is an aspect of DSS in Figure 1
below. It is clear that BI is a combination of the use Data Mining tools and Text Mining in
Data Warehouse to extract knowledge needed for business decision making. Looking at it
another way, we can understand that BI is a set of methodologies, processes, architectures,
and technologies that transform raw data into meaningful and useful information that
can be used to enable more effective strategic, tactical, and operational insights and
decision-making [3].
Figure 1. Components of BI and DSS [6]
2.1.2. Business Intelligence Frame Work
BI can use both structured and semi-structured data in its processes. For structure
data, BI can withdraw data from a data warehouse. Normally, this data is extracted from
the information system via internet browser technologies. Figure 2 will show the BI
framework for both structured and semi-structure data.
To create business intelligence information, the integrated data are searched for,
analyzed and delivered to the decision maker. For structured data we can use tools such
as Enterprise Resource Planning (ERP) systems, data-mining tools and on-line analytical
processing tools (OLAP). However, at the moment, a different and less sophisticated set
of analytic tools is currently required to deal with semi-structured data (such as email,
web pages, reports, etc.). In this paper, the case study of BI iinvolves only structured data.
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Nguyen Thi Thu Thuy, Hoang Quoc Minh
Details about BI using semi-structured data can be seen in [4].
Figure 2. Business Intelligence Data Framework [5]
The architecture for Structured Data can be seen in Figure 3. Information from
alternative systems such as ERP, CRM, etc can be stored in Data Warehouse. Only BI
needed data will be saved in Data Mart. Via Network Distribution, output can take several
forms including exception reports, routine reports and responses to specific request. The
outputs are sent whenever they need to be required on demand.
Figure 3. Typical BI Architecture for Structured Data [5]
2.1.3. Business Intelligence for the Cau Giay District People’s Committee
In Vietnam, many companies have deployed information systems such as ERP and
CRM. In these systems most of the data is ready for analysis. Most company reports show
only specific activities, such as accounting reports and executive operations, of the various
offices of the company. The head of the company or the management board does not
have enough information to make a decision because this information is not there in the
company’s report. The lack of analysis tools which could combine data from alternative
reports sources is one reason why the above issue exists.
In the Cau Giay District People’s Committee’s information system, such is the case.
A general and inclusive online summary report can not be provided because there are
available only alternative reports from individual offices, generated by each of those
offices. In this paper we would like to propose a BI framework that can be used in the
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Business intelligent: a combination of use data mining tools in business process
Cau Giay District People’s Committee’s information system and would result in a vast
improvement.
The Cau Giay District People’s Committee can now work with 404 administrative
procedures (258 district procedures and 146 wards procedures). People in the district can
download all 404 level 2 administrative forms of alternative procedures from a website.
The number of administration procedures of level 3, people will find forms for only 8
procedures on the website. The software for this, installed in 2006, is SQL server 2000.
The disadvantage of this system is that handles only the manual reports generated by the
office. Thus, individual officers generally need to refer to additional paper documents.
The result is that data will appear in an officer’s report that is not in either the database
or a system report. OLAP technology can produce a report automatically for managers
who go online with data taken from the system. Therefore, the reports using OLAP would
be more accurate than manually derived reports. Moreover, OLAP reports would save the
time by scanning data from all databases because it is built based on the OLAP database
in the servers.
In this paper, the BI is represented in the use of MS Visual Studio in building
an OLAP cube for analysis data in a One-Door database. This database is in the Cau
Giay District People’s Committee Information System. The framework for the One-Door
database can be seen in Figure 4.
Figure 4. BI Framework for One-Door Database
The Cau Giay District People’s Committee Information System contains many
databases, the largest being the One-Door database which includes 90 tables. There are
an additional 68 relational tables, and others that have no relevance. The raw data (papers
and excel files) is taken from the many offices in the Committee and the many Communes
in the District. Committee Officers clean data subjectively and input that into the system.
For example, system default values will be used to provide data that is missing in a form.
To understand the use of BI in the analysis of business data we need to be clear on
the definition of OLAP cube in the BI context. It can be understood as an array of data that
has 0 or many dimensions where each dimension is a table that attributes to the analysis
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Nguyen Thi Thu Thuy, Hoang Quoc Minh
of business data in the search for business intelligence in online analytical processing (see
Figure 6 for an example of creating alternative dimensions for a One-Door database).
From this definition, it is clear that the analytical business data obtained via online
processing differs greatly from the that obtained in traditional analysis using alternative
known tools such as MS Excel or SPSS. The advantage here is that we can represent data
in many dimension to extract the needed data for business processes. More detail about
OLAP cube for BI can be seen in [2].
Figure 5. Table list and some table relationships in One-Door database
Figure 6. Select dimension for One-Door Cube
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Business intelligent: a combination of use data mining tools in business process
Figure 7. Report example
• Introduction to the One-Door database: This database serves the following
functions: Declare and mange the list location and administrative procedures of the
Cau Giay District People’s Committee offices; Receive administrative documents from
citizens; Set the time to process and return documents received; Delivery of documents
to the appropriate office; Update documents; Notify offices when deadlines to return
documents draws near; Generate a quarterly work reports for each offices.
• Database structure: This includes 90 tables to serve about 404 administration
procedures (see in Figure 5).
Figure 8. Cube ND_ThuTuc example
• Design cube, Dimension table and Fact table: The cube is designed by using
BIDS tools in MS visual Studio. See Table 1 for Fact Tables.
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Nguyen Thi Thu Thuy, Hoang Quoc Minh
The dimension for the One-Door Cube can be seen in Figure 6. The cube can
be deployed to analyze service within the server. A detailed report will be built with
data taken from the One-Door Cube. In Figure 7, the report shows the summary of
administration procedures, grouped by professional area, that is to be delivered to other
administrative levels when there are complaints from citizens. This data is taken from the
alternative dimensions in the selected Fact Tables in the One-Door Cube (see Figure 8) in
the server.
Table 1. List of Fact Tables
Table Name Table Name
DM_CanBoThuTuc DM_CanBoThuTucND
DM_SoNganh DM_SoNganhHinh
DM_ThuTuc DM_ThuTucHoSo
MC_EventsHoSo MC_NhanDuLieu
MC_NhanDuLieuTra MC_TheoNhomYeuCau
MC_YeuCauChuyenDi MC_YeuCauGiaoPhong
ND_QuyetDinhChungNhan ND_QuyetDinhChungNhanBienDong
ND_ThuTucCotThem ND_ThuTucCotThemCm
ND_YeuCauHoSoGomCo ND_YeuCauLog
DM_MucTaiLieuFile DM_PhuongXaHinh
DM_SoNganhThuTuc DM_TaiKhoan
MC_EventsAttach MC_EventsConcurrent
MC_NhanDuLieuNhan MC_NhanDuLieuPhuongXa
MC_TruyenDuLieu MC_YeuCauAttach
MC_YeuCauTra ND_QuyetDinhAttach
ND_Taikhoan ND_TheoNhomYeuCau
ND_ThuTucHoSo ND_YeuCauCotThem
ND_YeuCauTra
3. Conclusions and Further Works
The BI definition seems to be a detailed definition of Data–driven Decision Support
System (DSS) as is commonly used in Business Information Systems. When there is
a clear understanding of this definition, researchers can build accurate and adaptive
application models for their business organizations. This paper attempts to clarify the
differences between DSS and OLAP BI. Moreover, the application of the One-Door cube
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Business intelligent: a combination of use data mining tools in business process
built for the Cau Giay District People’s Committee Information System is an example
of the use of OLAP BI in the real world. We hope that Data-driven DSS or BI can be
deployed in many company applications to help businesses make challenging decisions.
Additional work on the Cau Giay District People’s Committee Information System will
deal with semi-structure data such as administration procedures via emails from citizens
or reports from alternative offices.
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[4] Moss, L.T. and S. Atre, 2003. Business Intelligence Roadmap: The Complete Project
Lifecycle.
[5] Negash, S, 2004. "Business Intelligence". Communications of the Association of
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[6] Power, D.J., 2011. A Brief History of decision support system. Available at
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[8] Soejarto, A., 2003. “Tough Times Call for Business Intelligence Services,
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[10] website:
[11] website: www.caugiay.hanoi.gov.vn
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