1. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET)
& TECHNOLOGY January- February (2013), © IAEME
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 1, January- February (2013), pp. 01-07
IJCET
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME
www.jifactor.com
IMPACT OF BUSINESS INTELLIGENCE TOOLS IN
EXECUTIVE INFORMATION SYSTEMS
1
Mrs.R.Sharmila
Research Scholar, Manonmaniam Sundaranar University, Tirunelveli
Asst.Prof, Department of Applied Science
Er. Perumal Manimekalai College of Engineering, Hosur.
e-mail: sharmi.saravanan@yahoo.in
2
Dr.A.Subramani
Research Supervisor, Manonmaniam Sundaranar University, Tirunelveli
Prof & Head, Department of MCA,
KSR College of Engineering, Tiruchengode.
e-mail: subramani.appavu@gmail.com
ABSTRACT
The Executive Information System (EIS) are designed to facilitate and support the
information and strategic decision making needs of senior executives with the help of
innovative tools and techniques. These technologies are known as Business Intelligence (BI)
Tools. Business Intelligence in general deals with bringing the right information at the right
time to the right people in the right format. The goal of the BI systems is to pull data from all
internal systems and external sources to present a single version of the truth. This paper
discusses about the importance and architecture of BI tools in EIS.
Keywords: EIS, BI tools, Data Warehousing, OLAP tools, Dashboards, Reporting, etc.
INTRODUCTION
“Business Intelligence”, the term Coined by Gartnerin (1989) defined as using
information effectively to make better decisions. BI is the core component of a company’s IT
framework. The BI architecture consists of a Data Warehouse server which consolidates data
from several operational databases, and serves a variety of front-end querying, reporting, and
analytic tools. The back-end of the architecture is a data integration pipeline for populating
the data warehouse by extracting data from distributed and usually heterogeneous operational
sources; cleansing, integrating and transforming the data; and loading it into the data
warehouse. Business intelligence tools require software applications capable of supporting:
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
• Business intelligence solution design
• Analytic business intelligence use
• Business intelligence reporting and
• Decision support requirements
Executive Information System (EIS) is one of the most potent forms of computing,
that serves the information needs of the top executives. Through EIS, the BI tools can
identify problems and present trends that are of vital importance to the organization. EIS
represents one of the most sophisticated applications of computer technology. Deploying EIS
involves many risks: system design, data quality, and technology obsolescence.
Finding Information needs is the very crucial stage in the design of Business
Intelligence Tools and it is carried out with the support of Structured Interviews like IBM's
Business System Planning (BSP), Critical Success Factors (CSF) and Ends/Means (E/M)
Analysis. The key general categories of business intelligence tools are: Spreadsheets,
Reporting and querying software (tools that extract, sort, summarize, and present selected
data ), OLAP: Online analytical processing , Digital dashboards, Data mining, Data
warehousing, Decision engineering, Process mining, Business performance management and
Local information systems. Eclipse BIRT Project, RapidMiner, SpagoBI, R and KNIME are
some of the open-source free products whereas Jaspersoft (Reporting, Dashboards, Data
Analysis, and Data Integration) , Palo (OLAP database: OLAP Server, Worksheet Server and
ETL Server) and Pentaho (Reporting, analysis, dashboard, data mining and workflow
capabilities) are some of the open source commercial products.
MOTIVATION AND BACKGROUND
Business Intelligence Tools promotes managerial learning and provides manager’s
access to the data delivers trends and assists in measuring performance in a timely manner.
Instead of a small number of analysts spending 100% of their time analyzing data, all
managers and professionals should spend 10% time using BI software.
The Executive Information System developed by Executive information committee of
Pennsylvania University in 1998 provided a view of the Admission information. Paul Frech,
president of Lockheed-Georgia, monitors employee contributions to company-sponsored
programs (United Way, blood drives) as a surrogate measure of employee morale (Houdeshel
and Watson 1987). C. Robert Kidder, CEO of Duracell, found that productivity problems
were due to salespeople in Germany wasting time calling on small stores and took corrective
action (Main 1989). Vandenbosch and Huff (1992) from the University of Western Ontario
found that Canadian firms using an EIS achieved better business results if their EIS promoted
managerial learning.
Potentially valuable content is frequently trapped in organizational silos, lost in transit
from one system to another, bypassed by inadequately tuned data collection systems, or
presented in user-unfriendly formats. Although wired with layers of information-gathering
technology, organizations still find it difficult to deliver the right data to the right people. At
the heart of these difficulties are inadequate executive information systems, supposedly
designed to help top management easily access pertinent internal and external data for
managing a company
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3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
Jack Rockart’s (1979, 1982) field research stimulated the development of executive
information systems (EIS) and executive support systems (ESS). These systems evolved from
single user, model-driven decision support systems and from the development of new
relational database products. The first EIS generally used pre-defined information displays
maintained by analysts for senior executives. For example, in the Fall of 1978, Lockheed-
Georgia began development of an EIS called Management Information and Decision Support
(MIDS) system (cf., Houdeshel and Watson, 1987).
EIS were specifically tailored to an executive's information needs. So there was a
targeted user group. Managers using EIS were able to access data about specific issues and
problems as well as read aggregated reports. Executive Information Systems, business
intelligence and data warehousing technologies are converging in the marketplace. Twenty
years ago, EIS used proprietary databases that required many staff people to update, maintain
and create. This approach was very expensive and remains hard to justify. Organizing
external data may however be best done in a dedicated database. Today executives need both
structured and unstructured external data. Realistically external data becomes obsolete
quickly and IS/IT staff aren't the appropriate maintainers for such data. Data warehouses,
business intelligence technologies, the Web and OLAP have made Executive Information
Systems potentially more powerful and more practical.
Modern EIS should report key results to managers. Performance measures in an EIS
must be easy to understand and collect. Wherever possible, data should be collected as part of
routine work processes. An EIS should not add substantially to the workload of managers or
staff. EIS should create value. According to Wikipedia, an EIS is commonly considered as a
specialized form of a Decision Support System (DSS). We need information systems that are
easy for senior executives to use. Modern EIS should provide timely delivery of secure,
sensitive decision relevant company information; present information in a context that helps
executives understand what is important and what is happening; provide filters and drill-
down to reduce data overload; assist in tracking events, finding reports and monitoring
results; and finally, a modern EIS should increase the efficiency and effectiveness of
executive decision makers.
ARCHITECTURE
BI architecture was designed for strategic decision making, where a small number of
expert users analyze historical data to prepare reports or build models, and decision making
cycles last weeks or months. The architecture requires the data to be stored in a Data
warehouse server. The data warehouse is described in terms of four inter‐related dimensions
as shown in Fig.1:
1. Applications (or the business intelligence layer).
2. Data.
3. Technology and security.
4. Support—processes and organization
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4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
Fig. 1 Data Warehouse components
Umeshwar Dayal et. al (2009) has proposed the architecture as an information supply
chain as shown in Fig. 2. Data Warehousing is a complex systems integration problem. A
full-blown Data Warehousing System may encompass the data warehouse, various data
marts (department, function, or application-specific DSSs, using Relational,
Multidimensional (MOLAP), or Column-based Servers), One or more Operational Data
Stores (ODSs) and One or more Data Staging Areas.
Data from various heterogeneous sources like database files, flat files, raw files such
as online transaction processing processing (OLTP) systems is extracted, cleansed,
integrated, transformed and loaded into the Data Warehouse system. The first Data
Warehousing Systems architecture begins with Extraction, Transformation, Migration, and
Loading (ETML) process, Establishes the data warehouse first, along with centralized
metadata repository, Data Marts are constituted from extracted and summarized data
warehouse data and metadata. Organizations stores subsets of data in Data Marts for easier
accessing. These processes are done by the BI such as ETL (Extract – Transform – Load)
tools such as Informatics, Oracle’s Warehouse Builder, PowerCenter, Carleton, Sybase
Adaptive server and so on.
Fig.2 Architecture of Business Intelligence system
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5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
For the information in the data warehouse to be valuable, it needs to be delivered in way that
makes it useful to campus personnel in doing their jobs. This is the job of business
intelligence applications. The warehouse includes a variety of these tools for reporting (or
information delivery, including variants such as dashboards and alerts), Query and Modeling,
Planning and Forecasting.
REPORTING
Data is useless if all it does is it in the data warehouse. As a result, the presentation
layer is of very high importance. Reporting applications deliver information in a form which
is useful to users. In their simplest form—fixed and printed reports. They imply a partnership
between users who need information and specialists who help design reports, displays and
graphics to deliver the required information. The best reports provide just the information a
user needs for a specific purpose, delivered in a way which makes the information usable and
actionable. Systems such as BAIRS and Cal‐Profiles focus on reporting. Most of the OLAP
tools allow users to call up pre-defined reports or create adhoc reports. SAP Business
Objects, MicroStrategy, IBM Cognos, Actuate, Jaspersoft, Pentaho, etc. are some of the tools
used for reporting.
DASHBOARDS AND ALERTS
In the interest of delivering just the information most useful to support decision‐making or
action, it will be useful to complement conventional reports with information dashboards and
alerts. Dashboards communicate information with quickly‐comprehended graphics. Typically
dashboards are used to report on established performance indicators, measured at predefined
intervals. Properly planned, dashboards make it easy for personnel managing a complex
process, such as undergraduate admissions, for example, to quickly assess current state and
progress against goals. Likewise, sometimes reporting will be confined to exception
conditions. Exceptions may deliver periodically as reports. More often, though, they will be
delivered as alert messages—emails or similar notifications. For example, departmental
administrators might get alerts when course enrollments exceed critical thresholds. Or a grant
administrator might get an email when grant expenditures exceed plan for a given period.
QUERY AND ANALYSIS
People who have analytical skills and jobs requiring analysis will need the ability to explore
the information in the warehouse. Available Data Warehousing query languages like MDX
and Oracle OLAP support only numeric data and moreover require skilled DW developers to
design queries. OLAP is well established in traditional business intelligence. To ensure
premium performance, it is supported in InetSoft software by a special XMLA data source
type. InetSoft provides an integrated OLAP front-end that allows business users to tap into
this rich source of data. The ability to leverage a data warehouse investment, high
performance, the ability to mashup OLAP data with other sources are the advantages of using
OLAP based analysis. InetSoft's rich ad hoc analysis options empower business users of all
skill levels by providing the OLAP server tools necessary to access insightful information.
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6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
CASE STUDY
Pepsi Co.
CONCLUSION
Information Systems are software products intended to store and handle data in an
organization. They must meet the informational needs of all levels of management -
operational, middle and top and they must be designed accordingly. Ann EIS should be
designed to allow managers who are not trained to use query languages and advanced
technologies, a fast, easy, and understandable way to navigate into data and identify trends
and patterns. Developing EIS systems involves time, high-costs and human resources, efforts
and an EIS must be capable to provide in real time representative information to the
executive management.
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6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME
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BIOGRAPHY
Mrs. R.Sharmila is currently working as a Assistant Professor and
Head, Department of Applied Science, Er.Perumal Manimekalai
College of Engineering, Hosur and as a Research Scholar in
Manonmaniam Sundaranar University, Tirunelveli. She received her
M.Sc and MCA degrees from Periyar University, Salem and M.Phil.,
from Manonmaniam Sundaranar University, Tirunelveli .She
published 10 technical papers in National Conference and 1
International conference. Her area of research is Data Mining.
Dr. A. Subramani is currently working as a Professor and Head,
Department of Computer Applications, K.S.R. College of
Engineering, Tiruchengode and as a Research Guide in various
Universities. He received his Ph.D. Degree in Computer
Applications from Anna University, Chennai. He is a Reviewer of 10
National/International Journals. He is in the editorial board of 6
International/National Journals. He is an Associate Editor of Journal
of Computer Applications. He has published more than 30 technical
papers at various International, National Journals and Conference proceedings. His areas
of research include High Speed Networks, Routing Algorithm, Soft computing, Wireless
Communications, Mobile Ad-hoc Networks.
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