Business Intelligencefor Decision Making

Dr. V.V. NARENDRA KUMARProfessor and Vice Principal,

St. Mary’s Engineering College,Hyderabad.

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PREFACE

Business Intelligence has drawn considerable attention of both academiciansand practitioners. Business intelligence, as one of the fastest developing businessapplication areas, has potential role in good and quality decision making. Muchof the business intelligence techniques rely on data mining processes. Hence,data mining has a vital role to play in Business Intelligence.

This book presents various data mining techniques that are commonly usedin Business Intelligence for effective decision making in businesses. Datawarehouses also have a great role in storing large amounts of data from varioussources. This data is very useful in finding new and hidden patterns which helpthe business to flourish during the downturns.

Several examples have been presented for easy understanding of theconcepts in this book. The content of this book was taken from several websites,blogs, e-books, e-newspapers, etc. which contributed towards BusinessIntelligence and Decision Making.

This can serve as a supplementary reading, reference book for those whowant to gain some extra knowledge in Decision Making, Data Mining andBusiness Intelligence.

Hope the reader of the book finds the content useful.

I dedicate this book to Sri Venkateshwara, the Lord of Seven Hills.

Dr. V.V. NARENDRA KUMAR

CONTENTS

1 INTRODUCTION 1 - 4

2 BUSINESS DECISION MAKING 5 - 13

3 DECISION MAKING TOOLS AND TECHNIQUES 14 - 27

4 DECISION SUPPORT SYSTEMS AND EXPERT SYSTEMS 28 - 35

5 DATA WAREHOUSING 36 - 56

6 DATA MINING – AN OVERVIEW 57 - 71

7 DATA MINING TECHNIQUES 72 - 129

8 BUSINESS INTELLIGENCE vs. BUSINESS ANALYTICS (BI vs. BA) 130 - 140

9 CONCLUSION 141 - 146

(1)

HISTORY

In the year 1865, the term “Business Intelligence” was originally framed by Richard MillarDevens’ in the ‘Cyclopædia of Commercial and Business Anecdotes’. Later on in 1958, IBMresearcher Hans Peter Luhn used the term business intelligence. He employed the Webster’sdictionary definition of intelligence: “the ability to apprehend the interrelationships of presented factsin such a way as to guide action towards a desired goal”.

Business intelligence as it is understood today is said to have evolved from the decision supportsystems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated inthe computer-aided models created to assist with decision making and planning. From DSS, datawarehouses, Executive Information Systems, OLAP and business intelligence came into focusbeginning in the late 80s.

In 1988, an Italian-Dutch-French-English consortium organized an international meeting on theMultiway Data Analysis in Rome. The ultimate goal is to reduce the multiple dimensions down to oneor two (by detecting the patterns within the data) that can then be presented to human decision makers.

In 1989, Howard Dresner (later a Gartner Group analyst) proposed “business intelligence” as anumbrella term to describe “concepts and methods to improve business decision making by using fact-based support systems”. It was not until the late 1990s that this usage was widespread.

What came to be known as BI technologies evolved from earlier, often mainframe-basedanalytical systems, such as decision support systems and executive information systems. Businessintelligence is sometimes used interchangeably with business analytics; in other cases, businessanalytics is used either more narrowly to refer to advanced data analytics or more broadly to includeboth BI and advanced analytics.

BUSINESS INTELLIGENCE

Business intelligence (BI) is the set of techniques and tools which transforms raw data intomeaningful and useful information for business analysis purposes. Business intelligence (BI) presentsactionable information to help corporate executives, business managers and other end users makewell-versed business decisions. BI encompasses a variety of tools, applications and methodologiesthat enable organizations to collect data from various internal systems and external sources, organizeit for analysis, develop and execute queries against the data, and create reports, dashboards and data

INTRODUCTION1

Business Intelligence for Decision Making2

visualizations to make the analytical results available for better decision. Seldom, BI is also termed as“data surfacing”.

BI technologies are capable of handling large amounts of unstructured data to help identify,develop new strategies in business opportunities. BI allows for the easy interpretation of largevolumes of data thereby identifying new opportunities for implementing an effective strategy based oninsights. BI can provide can provide businesses competitive edge and long-term stability.

BI can be used to support a variety of business decisions ranging from operational to strategic.Basic operating decisions include product positioning or pricing whereas strategic business decisionsinclude priorities, goals and directions at the broadest level. In all cases, BI is most effective when itcombines data derived from the market in which a company operates (external data) with data fromcompany sources internal to the business such as financial and operations data (internal data). Thecombination of external and internal data provides a more complete picture which creates an“intelligence” that cannot be derived by any singular set of data

The potential benefits of business intelligence comprise of● Accelerating and improving decision making● Optimizing internal business processes● Increasing operational efficiency● Driving new revenues and● Gaining competitive advantages over business rivals

BI systems can also help companies identify various market trends and business problems. BItechnologies provide historical, current and predictive views of business operations.

Business intelligence technologies have various functions like:● Reporting● Online analytical processing● Analytics● Data mining● Process mining● Complex event processing● Business performance management● Benchmarking● Text mining● Predictive analytics and● Prescriptive analytics.

Business intelligence is made up of number of components like:● Multidimensional aggregation and allocation● Denormalization, tagging and standardization● Real time reporting with analytical alert● A method of interfacing with unstructured data sources● Group consolidation, budgeting and rolling forecasts● Statistical inference and probabilistic simulation

Introduction 3

● Key performance indicators optimization● Version control and process management● Open item management

Business Intelligence (BI) systems are software applications. They facilitate better understandingof organizational data and provide the information to the organizations for better decision making.Business intelligence systems mainly focus on reporting, querying, and analysis of the data, dashboard management, OLAP, Data mining, etc., in an enterprise data warehouse (EDW) and in also datamarts.

Business intelligence can be categorized into 5 applications (Figure 1.1), namely:1. Operational Reports: These displays data in a structured layout (i.e., rows and columns)

in the form of tables.2. Query and Analysis: These are interactive methods to query and present the data in an ad

hoc manner and to find information on an as-needed basis.3. Dashboard Management: These are graphical interfaces and real time methods which

provide guided analysis and monitors organizational metrics.4. On-line Analytical Processing (OLAP): This is the capability of manipulating and

analyzing data from various perspectives in a quick manner.5. Data Mining and Predictive Analytics: Utilizing statistics, algorithms, and sophisticated

data search capabilities to discover hidden patterns and relationships in data and projectfuture results.

Figure 1.1: Applications of Business Intelligence

BI data comprises of historical information, and new data gathered from source systems as it isgenerated. This enables BI analysis to support both strategic and tactical decision making processes.Initially, BI tools were used by data and other IT analysts who and produced reports with query resultsfor business users. Gradually, business executives and workers started using BI software.

Data Warehouse/Data Mart

DashboardManagement

Data Mining

Query andAnalysis

OperationalReports

OLAP

Business Intelligence for Decision Making4

Business intelligence combines a broad set of data analysis applications, including ad hocanalysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real timeBI, operational BI, cloud and software as a service BI, open source BI, collaborative BI and locationintelligence. BI technology also includes data visualization software for designing charts and otherinfographics, as well as tools for building BI dashboards (Figure 1.2) and performance scorecards thatdisplay visualized data on business metrics and key performance indicators in an easy-to-grasp way.BI applications can be bought separately from different vendors or as part of a unified BI platformfrom a single vendor.

Figure 1.2: An Oracle Business Intelligence Dash Board

Business intelligence data typically is stored in a data warehouse or smaller data marts that holdsubsets of a company’s information. In addition, Hadoop systems are increasingly being used withinBI architectures as repositories or landing pads for BI and analytics data, especially for unstructureddata, log files, sensor data and other types of big data. Before it’s used in BI applications, raw datafrom different source systems must be integrated, consolidated and cleansed using data integrationand data quality tools to ensure that users are analyzing accurate and consistent information.

In addition to BI managers, business intelligence teams generally include a mix of BI architects,BI developers, business analysts and data management professionals; business users often are alsoincluded to represent the business side and make sure its needs are met in the BI development process.To help with that, a growing number of organizations are replacing traditional waterfall developmentwith Agile BI and data warehousing approaches that use agile software development techniques tobreak up BI projects into small chunks and deliver new functionality to end-users on an incrementaland iterative basis. Doing so can enable companies to put BI features into use more quickly and torefine or modify development plans as business needs change or new requirements emerge and takepriority over earlier ones.