Decision Support Systems
Lecture II
Business Intelligence
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2.1 A Preview of the content
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A centralized repository of data, usually the data warehouse (DW).
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An end-user set of tools that can be used to create reports and queries from data and
information and to analyze the data, information, and reports. This set of tools is referred to as BA and also
includes data visualization.
•
To find non-obvious relationships among large amounts of data, We can use a statisticalmathematical approach known as data mining. For text, We can use text mining, and for Web data, we can
use Web mining; all of these are described. The statistical tools and the methodology for mining, including
neural computing.
•
To gain a competitive edge, companies need to be innovative and to excel. In order to find out
how well a company is performing today, Where it wants to go, and how to get there, we can use a
methodology referred to as BPM . It includes setting up goals as metrics and standards and monitoring and
measuring performance by using the BI methodology.
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2.2 The origins and drivers of business intelligence (BI)
Table 2.1 Business Value of BI Analytical Application
Analytic Application
Business Question
Business Value
Customer segmentation
What market segments do
my customers fall into, and
what are their characteristics?
Personalize Customer
relationships for higher
satisfaction and retention.
Propensity to buy
Which customers are most likely to
respond to my promotion?
Target customers based on
their need to increase their loyalty
to your product line.
Also, increase campaign
profitability by focusing on
the most likely to buy.
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2.2 The origins and drivers of business intelligence (BI)
Table 2.1 Business Value of BI Analytical Application
Analytic Application
Business Question
Business Value
Customer profitability
What is the lifetime profitability of my
customer?
Make individual business
interaction decisions on the
overall profitability
of customers.
Fraud detection
How can I tell which transactions are
likely to be fraudulent?
Quickly determine fraud and
take immediate action to
minimize cost.
Customer attrition
Which customer is at risk of leaving?
Prevent loss of high-value
customers and let go of
lower-value customers.
Channel optimization
What is the best channel to reach my
customer in each segment?
Interact with customers
based on their preference
and your need to manage cost.
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2.3 THE GENERAL PROCESS OF INWELLIGENCE CREATION AND USE
Managers and executives need BI solutions to better manage the business. Enterprises that fail
to properly implement these solutions put themselves at a competitive disadvantage. To be successful in
today’s business environment, enterprises must:
•
Assess their readiness for meeting the challenges posed by these new business
realities
•
Take a holistic approach to BI functionality
•
Leverage best practices and anticipate hidden costs
Gartner, Inc. (2004), suggested the following key questions as a framework for BI
analysis:
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How can enterprises maximize then BI investments?
•
What BI functionality do enterprises need, and what are they using today?
•
What are some of the hidden costs associated with BI initiatives?
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2.3 THE GENERAL PROCESS OF INWELLIGENCE CREATION AND USE
An example of how BI helped a company deal with the Sarbanes-Oxley Act of 2002 is
provided next (extracted from Gartner, Inc., 2004).
Example
The Sarbanes-Oxley Act of 2002 mandates drove one firm to implement a new financial
performance management system, capable of meeting the new requirements to:
•
Perform flawless analysis and compilation of thousands of transactions and
journal entries.
•
Balance more access to data with the need to control access to sensitive insider
information.
•
Deliver reports to the SEC in less time.
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2.3 THE GENERAL PROCESS OF INWELLIGENCE CREATION AND USE
The company deployed a BI infrastructure and applications that met these challenges.
Within the overarching goal of achieving financial-reporting compliance, these objectives included the
following:
•
Get "more eyes on the data" and key performance indicators and build in strict
security controls
•
Provide live reports that allow people to drill down to the lowest level of
transaction detail
•
Put a spotlight on the accounting treatment of material components
•
Proactively scour the financial databases for anomalies, using variance triggers
•
Gather all financial data into a cohesive database
An implementation tightly linked to these objectives provided the company with a financial
performance management system that enables analysis to complement accounting and budgeting applications
for flexible reporting, free-form investigation, and automated data analysis.
The BI infrastructure and
applications support large numbers and types of users and uses automatic data mining for anomaly detection.
It can proactively alert specific individuals whenever an anomaly is detected.
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5.3 THE GENERAL PROCESS OF INWELLIGENCE CREATION AND USE
A CYCLICAL PROCESS
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Process of Intelligence
Creation
and Use
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2.3 THE GENERAL PROCESS OF INWELLIGENCE CREATION AND USE
INTELLIGENCE CREATION AND USE AND Bl GOVERNANCE
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For each potential BI project in the portfolio, it is important to use return on investments (ROI)
and total cost of ownership (TCO) measures to estimate the cost-benefit ratio.
•
This means that each project must be examined through costing associated with the general
process phases as well as costs of maintaining the application for the business user.
•
In addition, the benefits estimations need to involve end-user examinations of decision-making
impacts, including measures reflecting benefits such as cash flow acceleration.
•
Some organizations refer to the project prioritization process as a form of BI governance (see
Matney and Larson, 2004; and Online File WS.2).
A typical set of issues for the BI governance team is to address the following:
•
Creating categories of projects (e.g., investment, business opportunity, strategic, mandatory)
•
Defining criteria for project selection
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Determining and setting a framework for managing project risk
•
Managing and leveraging project interdependencies
•
Continually monitoring and adjusting the composition of the portfolio
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
TRANSACTION PROCESSING VERSUS ANALYTIC PROCESSING
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Enterprise software system are designed as transaction processing tools, and today the main job
is to optimize an informed decision-making process for users at all levels of the organizational hierarchy.
•
Recent trends seem to indicate that access to key operational data is no longer the purview of
executives alone.
•
Many executives of manufacturing and service companies today are allowing (and even
encouraging) low-level managers, supervisors, and analysts on the shop floor and in distribution centers
access to operational performance data, to enable better and more timely decision making by those
employees.
•
The transaction processing systems involved with these transaction constantly handle updates to
what we might call operational databases. Example, an ATM withdrawal transaction needs to reduce the
bank balance accordingly, a bank deposit adds to an account, and a grocery store purchase is likely reflected
in the store’s calculation of total sales for the day and should reflect an appropriate reduction in the store’s
inventory for the items we bought.
•
These online transaction processing (OLTP) systems handle a company’s ongoing business.
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We will provide a more technical informed definition of DW; at this point, it suffices to say
that DWs are intended to work withFree
informational
data used Templates
for online analytical processing (OLAP)
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system.
2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
TRANSACTION PROCESSING VERSUS ANALYTIC PROCESSING
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Most Operational data in enterprise resource planning (ERP) system ---and in their
complementing siblings, such as supply-chain management (SCM) or customer relationship management
(CRM), are stored in what is referred to as OLTP system, which are computer-processing systems in which
the computer responds immediately to user requests.
•
The very design that makes an OLTP system efficient for transaction processing makes it
inefficient for end-user ad hoc reports, queries, and analyses.
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
THE INFORMATION FACTORY VIEW
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The term warehouse is associated with the concept of a factory.
•
The information factory View sees BI/DW as a central, critical component of any business
organization (corporate or government), and today it is moving more and more toward the Web
environment (see Inmon, 2005).
•
As with a physical factory, with an information factory, there are inputs (e.g., data sources,
acquisition), storage (e.g., DW, data marts), processing of inputs (e.g., analysis, data mining), and outputs
(e.g., data delivery, BI applications).
•
An information factory is connected to other internal information systems, such as ERP,
CRM, and e-commerce, as Well as to external information systems, usually via the Internet or an extranet.
The information factory concept is illustrated in Figure S.2.
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
DATA WAREHOUSING AND BUSINESS INTELLIGENCE
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A DW is a collection of data, designed to support management decision making. DWs
contain a wide variety of data that present a coherent picture of business conditions at a single point in
time. The theory was to create a database infrastructure that is always online and that contains all the
information from the OLTP systems, including historical data, but reorganized and structured in such a
way that it is fast and efficient for querying, analysis, and decision support.
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
TERADATA ADVANCED ANALYTICS METHODOLOGY
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Teradata, a division of NCR (teradata.com), created a methodology for BI that is illustrated
in Figure S.3. As shown in the figure, BI applications (upper-left side) are supported by advanced
analytics techniques and tools (left side). The methodology is shown on the right side as a cyclical process
that circles the enterprise DW. The process includes steps such as business understanding and data
understanding (right side of figure).
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
TERADATA ADVANCED ANALYTICS METHODOLOGY
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The Corporate Information Factory
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2.4 THE MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE
TERADATA ADVANCED ANALYTICS METHODOLOGY
Teradata Advanced Analysis Methodology
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2.5 TOWARD COMPETITIVE INTELLIGENCE AND ADVANTAGE
STRATEGIC IMPERATIVE
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BI projects have shown significant business value to organizations (see Williams and
Williams, 2003).
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First, in many markets, “barriers to entry of a new competitor to an industry” (see Porter,
1985) are being significantly diminished.
•
This means that an organization that has a strong position within its industry could easily
face new competitors because the costs and other constraints to becoming a player in the market have
decreased.
•
This is due to increasing globalization, as companies from around the world are challenging
major players in industries such as automobile manufacturing, electronics, textile manufacturing, and
computer software development, just to name a few.
•
Furthermore, the ability to deliver goods throughout the world through readily available
supply-chain channels such as FedEx, UPS, and DHL, as well as e-commerce, is making it easier for
potential competitors to get products and services to more customers almost anywhere.
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2.5 TOWARD COMPETITIVE INTELLIGENCE AND ADVANTAGE
COMPETITIVE INTELLGENCE
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Although CI often involves more than the BI initiatives used in most organizations, there
are some important overlaps.
•
One difference between CT and BI is that CI implies tracking what competitors are doing
by gathering sources of materials on their recent and in-process activities.
•
In BI initiatives, some outside sources of data are included in the analysis process, but they
are often available from third-party vendors.
•
Members of the Society of Competitive Intelligence Professionals (scip.org) view BI as an
emerging aspect of their overall charter, which typically includes more generic competitor analysis.
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2.5 TOWARD COMPETITIVE INTELLIGENCE AND ADVANTAGE
CEMPETITIVE STRATEGY IN AN INDUSTRY
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Competitor analysis is a component of industry analysis, which serves as a basis for
strategic planning processes.
•
Competitive intelligence in this context would imply that companies need to know if some
major new means of producing/generating/delivering a service or product would result in significantly
lower costs, thereby shifting the competitive landscape.
•
BI applications in this context might include scrutinizing quality metrics associated with
specific production processes, analyzing raw materials from various suppliers to assess defect rates,
tracking costs of goods sold as a percentage of rut volume, and so on.
•
In addition, BI applications can generate business rules can actually be integrated with
business processes.
•
Another competitive strategy is to focus on a particular market niche, perhaps through some
form of product or service differentiation.
•
This means that a specific market segment that has a special preference, perhaps for upscale
goods and services, would be the target of the strategy.
•
For example, having a hotel room prepared according to a business traveler’s common
wishes, with the appropriate newspaper
delivered
in the morning,
might be a way to build loyalty to serve
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a lucrative market niche.
2.5 TOWARD COMPETITIVE INTELLIGENCE AND ADVANTAGE
SUSTAINING COMPETITIVE ADVANTAGE
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Organizations do this through building brand and customer loyalty, for example, through
BI applications for product differentiation/market niche strategies.
•
Most strategic analysts agree that low-cost leadership may not yield a sustainable
advantage unless the low cost can be sustained.
•
For this reason, BI projects and DW are becoming increasingly important weapons in
sustaining competitive advantage across industries; the type of BI projects might vary based on
strategy, and, in particular, the BI governance team might prioritize potential projects based on their
ability to offer sustained competitive advantage.
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2.6 THE TYPICAL DATA WAREHOUSE AND BUSINESS INTELLIGENCE
USER COMMUNITY
Which personnel in an organization would be the most likely to make use of BI? One of
the most important aspects of a successful DW/BI initiative is that it must be of benefit to the
enterprise as a whole. This implies that there are likely to be a host of users in the enterprise, many of
whom should be involved from the outset of a DW investment decision. Not surprisingly, there are
likely to be users who focus at the strategic level and those who are more oriented to the tactical level.
An appropriate framework for describing user communities is to discuss the following categories:
farmers, tourists, operators, explorers, and miners (suggested by Imhoff and Pettit, 2004). The details
are provided in Online File WS.3.
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2.6 THE TYPICAL DATA WAREHOUSE AND BUSINESS INTELLIGENCE
USER COMMUNITY
Table 2.2 Matching User Types and Functionality to Maximum Value
Types of User
Functionality
IT
Power User
Executives
Functionality
Managements
Occasional
Information
Consumers
Extranet:
Partners
and Customers
Number of Users
Few
Dozens
Dozens
Dozens to
hundreds
Hundreds to
thousands
Hundreds to
thousands
BI tools and functions Developer ,
administrator,
metadata , security
, data management
Ad hoc query,
OLAP reports ,
data mining ,
advanced analysis
Dashboard ,
scorecard , reports
CPM
Report ,
spreadsheets ,
OLAP view ,
business activity
monitoring(BAM)
, corporate
performance
management
(CPM)
Reports ,
spreadsheets
Reports
Strategic value
High
High
Medium
Low
High
---------------------------------------------------------------------------------------Intranet-------------------------------------------------------------------------------------
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Extranet
2.6 THE TYPICAL DATA WAREHOUSE AND BUSINESS INTELLIGENCE
USER COMMUNITY
Gartner, Inc.(2004), distinguishes six similar types of users. Table 2.2 shows these
different users, how many these are, what BI tools they use, and the strategic value of their usage.
The various classes of DW and BI users that exits in an organization can help to guide
how the DW is structured and the types of BI tools and other supporting soft-ware needed. Members of
each group are excellent sources of information on assessing the costs and benefits of specific BI
project when a DW is in place.
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
Gartner, Inc.(2004), prepared a comprehensive report regarding the implementation of
BI and its relationships to other enterprise systems, such as ERP and CPM; this report also provides
interesting case studies. The report covers the following major topic:
-BI trends and technologies
-Effective BI approaches for today’s business world
-Organizing for BI success
-Best practices for defining effective business metrics
-Building an agile infrastructure for strategic BI
-The benefits of effective data quality and metadata management
-Management costs and enhancing value of DW and BI
-Business trends and best practices in managing corporate performance
-The BMP road map
-Key trends in corporate governance and compliance management
-Using business activity monitoring to gain a real-time edge
-Getting the most out of ERP through BI
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
-The role of analytics in successful CRM strategies
-Web analytics: From software to service model
-Driving workplace productivity with portals and enterprise suites
If a company’s strategy is properly aligned with the reasons for a DW and BI initiatives,
if the company’s IS organization is or can be made capable of playing its role in such a project, and if
the requisite user community is in place and has the proper motivation, it is wise to start BI and
establish a BI competency center (BICC) within the company. What can a company’s BICC achieve?
Following are several potential achievements, as exemplified by France
-The center can demonstrate how BI is clearly linked to strategy and execution of
strategy.
-The center can serve to encourage interaction between the potential business user
communities and the IS organization.
-The center can serve as a repository and disseminator of best BI practices between and
among the different lines of business.
-Standards of excellence in BI practices can be advocated and encouraged throughout the
company.
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
-The IS organization can learn a great deal through interaction with the user
communities, such as knowledge about the variety of types of analytical tools needed.
-The business user community and IS organization can better understand why the DW
platform must be flexible enough to provide for changing business requirements.
-The BICC can help important stakeholders, such as high-level executives, see how BI
can ply an important role
For more on BICC, see Gartner, Inc. (2004)
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
France Telecom Business Intelligence
In a short period of time, France Telecom transitioned from being France's only telecom
provider to one of several in an industry that was becoming deregulated. With new competitors rapidly
entering the telecom market, France Telecom’s executives knew they had to use their information
systems as a major strategic weapon. They initiated a repositioning under the banner of becoming the
“Net Company.” This required substantial rethinking to foster information systems standardization
throughout the company and its subsidiaries. To this end, they set out to migrate all applications to a
technical architecture more suitable for Web-based capabilities. In the past, the company had been
organized on a regional basis, with each regional business unit management its own IT budget. This
led to the coexistence of a myriad of dissimilar technologies, applications software versions, and so on.
Standardization was a first step in laying an infrastructure foundation for a major DW
and BI initiative. The company established a four-person team to facilitate its BICC. The BICC was
charged with overseeing the data warehousing implementation, ensuing that different business unit and
BI teams share best practices, and maintaining consistency in all BI-related projects. The BICC was
responsible for several import task. First, the BICC was charged with providing consulting and
development services, including offering advice to project managements on development strategies
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with respect to design,
2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
France Telecom Business Intelligence
auditing, installation, and so on. Second, the BICC provided support for project
managers, architects , designers, developers, and operators via a hotline and help desk. Support also
included an intranet Web site to provide BI tool advice, tips, methodology advice, and installation
documentation. Next, the BICC was the designated negotiator for the company with BI vendors. The
BICC centralized the opening of all case new software versions; and tracking, distribution, and
maintenance of license arrestment. Finally, he BICC helped support end users with tools to help them
become more autonomous, including an intranet site dedicated to user support, online training, and
interaction help. The intranet site was also used to disseminate information on BI success stories (i.e.,
project that resulted in superior performance in line with the company’s strategy and objectives). To
that end, the site was a source of information for executives on the state of the BI initiative in
delivering business value.
In summary, France Telecom’s BICC was developed to help the organization manage its
portfolio of BI projects, standardize analytical approaches across the enterprise, train and educate end
users, help power users, provide knowledge management through the sharing of best practices, and
handle all associated vendor relations and support.
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
France Telecom Business Intelligence
France Telecom’s service to more than 91 million customers in 220 countries on 5
continents has been significantly enhanced through its DW and ongoing BI projects. As an example of
executive leadership’s support for the BICC and its initiatives, the director of operations for Customer
Relations Information Systems stated, “To win new customers and develop loyalty, we now base our
action on a business intelligence process in which BI plays a key role of retrieving and analyzing data
in our corporate resources. Today, the company has 130,000 PCs, nearly half of which run BI
software.”
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
ATTAINING REAL-TIM, ON-DEMAND BI
The demand for instant, on-demand access to dispersed information has grown as the
need to close the gap between the operational data and strategic objective has become more pressing.
As a result, a category of product called real-time BI applications have emerged. The introduction or
new data-generating technologies, such as RFID, is accelerating this growth and the subsequent need
for real-time BI. Traditional BI systems use a large volume of static data that have been extracted,
cleansed, and loaded into a DW to produce reports and analyses. However, he need is not just
reporting because users need business monitoring, performance analysis , and an understanding of why
things are happening. These can alert users, virtually in real-time, about changes in data or the
availability of relevant report, alerts, and notifications regarding events and emerging trends in Web,
e-mail, or instant messaging (IM) applications. In addition, business applications can be programmed
to act on what these real-time BI systems discover. For example, an SCM application might
automatically place an order for more “widgets” when real-time inventory falls below a certain
threshold, or a CRM application might automatically trigger a customer service representative and
credit control clerk to check a customer who has placed an online order larger than $10,000
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2.7 SUCCESSFUL BUSINESS INTELLIGENCE IMPLEMENTTATION
ATTAINING REAL-TIM, ON-DEMAND BI
The first approach to real-time BI user the DW model of traditional BI system. In this
case, products from innovative BI platform providers such as Ascential (ascential.com) or Informatica
(informatiea.com) provide a service-oriented, near-real-time solution that populates the DW much
faster than the typical nightly extraction, transformation, and load (ETL) batch update does. The
second approach to real-time BI is commonly called business activity monitoring . This appriach is
used by pure-play BAM and hybrid BAM middleware provider such as Savvion (savvion.com),
Integration Software(itteration.com), Vitria(vitria.com), webMethods(webmethods.com),
Quantive(quantive.com) , and Tibco (tibco.com), It bypasses the DW entirely and user web services or
other monitoring means to discover key business evens. These software monitors (or intelligent agens)
can be placed on a separate server in the network or on the transactional application databases
themselves, and they can use event- and process-based approaches to proactively and intelligently
measure and monitor operational processes. Also, the use of the Web facilitates real-time BI, see
Thompson and Jakovljevic (2005)
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2.8 STRUCTURE AND COMPONENTS OF BUSINESS INELLIGENCE
THE DATA WAREHOUSE
Data flow from operational systems (e.g., CRM , ERP) to a DW, which is a special
database or repository of data that has been prepared to support decision-making applications ranging
from this for
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The Major Components of Business Intelligence
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2.8 STRUCTURE AND COMPONENTS OF BUSINESS INELLIGENCE
THE DATA WAREHOUSE
simple reporting and querying to complex optimization. The DW is constructed with
methodologies, mainly metadata and ETL. Also described there are data marts, which are repositories
of a particular subject or department (e.g., marketing).
BUSINESS ANALYTICS
Many software tools allow users to create on-demand reports and queries and to conduct
analysis of data. They appear originally under the name online analytical processing (OLAP). For
example, users can analyze different dimensions of multidimensional data , such as time series and
trend analysis views. Hence, business users can quickly and easily identify performance trends by
using time-phased information analysis and graphing capabilities of products that support more
sophisticated data analysis and have full calculated field capabilities. To conduct BA, the user needs
interactivity software that is called middleware to access the DW. It is considered infrastructure, and it
is a user interface to the system.
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2.8 STRUCTURE AND COMPONENTS OF BUSINESS INELLIGENCE
DATA MINING
Data mining is a class of database information analysis that looks for hidden patterns in a
group of data that can be used o predict future behavior. For example, I can help retail companies find
customers who have common interests. However, the term is commonly misused to describe software
that presents data in new ways because true data mining software does not just change he presentation
but actually discovers previously unknown relationships among the data; this knowledge is than
applied to achieving specific business goals. These tools are used to replace or enhance human
intelligence by scanning through massive storehouses of data to discover meaningful new correlations,
pattern recognition technologies and advanced statistics.
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2.8 STRUCTURE AND COMPONENTS OF BUSINESS INELLIGENCE
BUSINESS PERFORMANCE MANAGEMENT
The final component of the BI process is BPM/CPM. This component is based on the
balanced scorecard methodology, which is a framework for defining, implementing if managing and
managing an enterprise’s business strategy by linking objectives with factual measures. In other
words, it is a way to link top-level metrics, such as the financial information created by the chief
financial officer (CFO), with actual performance all the way down the corporate pecking order. BPM
uses BI analysis reporting and queries. The objective of BPM is to optimize the overall performance
of an organization.
Currently, most BI suites enable the application of balanced Scorecards by providing the
ability to readily compare business performance with established targets. Such BI suites also provide
a platform for sharing performance targets and results across an enterprise, allowing management to
understand how the business is performing at a glance.
The BPM includes usually dashboards, which provide a comprehensive, at-a-glance, view
of corporate performance with graphical presentations resembling an automobile dashboard.
Dashboards show performance measures, trends, and exceptions, and they integrate information from
multiple business areas. The centerpiece of any dashboard design is captured metrics and performance
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reflecting the health
of the business.
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2.9 BUSINESS INTELLIGENCE TODAY AND TOMORROW
In today’s highly competitive business, the quality and timeliness of business information for an
organization is not just a choice between profit and loss; it may be a question of survival or
bankruptcy. No business organization can deny the inevitable benefits of BI. Recent industry analyst
reports show that in the coming years, millions of people will use BI visual tools and analytics every
day (see Baum, 2006).
Today’s organizations are deriving more value from BI by extending actionable
information to many types of employees, maximizing the use of existing data assets. Producers,
retailers, governments, special agencies, and others use visualization tools, including dashboards.
More and more industry-specific analytical tools will flood the market to do almost any kind of
analysis and help to make informed decision making from the top level to the user level.
A potential trend involving BI is its possible merger with artificial intelligence (AI). AI
has been used in business applications since the 1980s, and it is widely used for complex problemsolving and decision support techniques in real-time business application (see Chapters 12-14). It will
not be long before AI applications are merged with BI, bringing in a new era in business. To enable this
integration, BI vendors are integration (EII). See Thompson and Jakovljevic (2005).
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2.9 BUSINESS INTELLIGENCE TODAY AND TOMORROW
BI is spreading its wings to cover small, medium, and large companies. Large BI
players are for large enterprises, and small, niche players service midsize and small companies.
Analytics tools are also penetrating the market for very specialized functions, which will help some
companies to go just for BA instead of full DW-based BI implementation.
BI takes advantage of already developed and installed components of IT technologies,
helping companies leverage their current IT investments and use valuable data stored in legacy and
transactional systems. For many large-size companies that have already spent millions of dollars
building DW and data marts, now is the right time to build BI as the next step to get the full benefit of
their investment, which will directly affect ROI. However, although some components of BI, such
as the DW, may change (e.g., data may be stored online), the need for conducting BI in our rapidly
changing business environment will increase, making BI a necessity. For more on the future of BI
see Lal (2005).
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