MIS ASSIGNMENT

Submitted to

P. Ganeshan

Concept Report on BUSINESS INTELLIGENCE

Saumya Ranjan Sahoo

Roll No. FPM1404

Doctoral Student

Entrepreneurship Development Institute of India

Submitted By

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Contents Abbreviation ................................................................................................................................................. 2

1. Introduction .......................................................................................................................................... 3

2. Component and features of Business Intelligence ............................................................................... 6

2.1 Business Intelligence Architecture ................................................................................................ 6

2.2 Changing Business Environments and Computerized Decision Support ...................................... 8

2.3 NEED FOR BUSINESS INTELLIGENCE ............................................................................................ 10

2.4 DESIGNING AND IMPLEMENTING A BUSINESS INTELLIGENCE ................................................... 11

3. TOOLS AND TECHNIQUES.................................................................................................................... 12

3.1 Business Intelligence Tools ......................................................................................................... 12

3.2 Business Intelligence Techniques................................................................................................ 16

4. ROLES, FUNCTION AND BENEFITS OF BUSINESS INTELLIGENCE ......................................................... 19

5. INDUSTRY EXAMPLES: USE OF BUSINESS INTELLIGENCE .................................................................... 22

5.1 CASE 1: Make My Trip ................................................................................................................. 22

5.2 CASE 2: Ortho Max ...................................................................................................................... 23

5.3 CASE 3: Little Rock India School .................................................................................................. 25

5.4 CASE 4: Ananda Bazar Patrika ..................................................................................................... 26

References .................................................................................................................................................. 30

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Abbreviation

BI Business Intelligence

DM Data Mining

IT Information Technology

ERP Enterprise Resource Planning

CRM Customer Relationship Management

SCM Supply Chain Management

DBMS Database management systems

DDBMS Distributed database management systems

QRA Query, Reporting and Analysis

OLAP On-line analytical processing

SaaS Software as a Service

EDW Enterprise Data Warehouse

VAS Value-Added Services

BW Business Warehouse

CMS Content Management System

ECHI External Call History Interface

CRM Customer Relationship Management

IVR Interactive Voice Response

VCP Value Chain Planning

AMS Active Management System

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1. Introduction

Many firms have invested heavily in Information technology to help them manage their

business more effectively and gain a competitive edge. Over the last three decade, large

amounts of critical business data have increasingly being stored electronically and this

volume is expected to continue to grow considerably in the near future. Despite this wealth

of data, many companies have not been able to fully capitalize on its value. This is because

information that is implicit in the data is not easy to discern. Firms in number of industries

including retail, finance, healthcare, insurance etc. routinely maintain enormous amount of

data about the activities and preferences of their consumers. Implicit with this data are

patterns that reveal the typical behaviors of these consumers behaviors that can help firms

fine-tune their marketing strategies, reduce their risks and effectively improve their business

strategy. Advances in fields of business intelligence (BI) and data mining (DM) are helping

business managers use their data more effectively and obtain insightful information, which

can give them a competitive edge. Both BI and DM software enables managers to discover

previously undetected facts present in their business-critical data, which may consume many

gigabytes or terabytes of storage, may reside in files or various DBMS-managed databases,

and may be stored on a variety of operating system platform. Accuracy, efficiency, and an

open architecture are important requirements of such data-mining software.

Competitive business pressures and a desire to use the existing IT investments have led

many firms to explore the benefits of the BI and DM technology. This technology is designed

to help managers and entrepreneurs discover hidden patterns in their data patterns, which

can help them understand purchasing behavior of their key customers, detect likely credit

card or insurance fraud, predict probable changes in financial markets and create BI (Jaiswal

& Mittal, 2011).

Even in firms operation, BI systems combine operational data with analytical tools to

present complex and competitive information to planners and decision makers. The objective

is to improve the timeliness and quality of inputs to the decision process. BI is used to

understand the capabilities available in the firm; the state of the art, trends, and future

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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 (Negash,

2004).

Business intelligence (BI) has two basic different meanings related to the use of the term

intelligence. The primary, less frequently, is the human intelligence capacity applied in

business affairs/activities. Intelligence of Business is a new field of the investigation of the

application of human cognitive faculties and artificial intelligence technologies to the

management and decision support in different business problems. The second relates to the

intelligence as information valued for its currency and relevance. It is expert information,

knowledge and technologies efficient in the management of organizational and individual

business. Therefore, in this sense, business intelligence is a broad category of applications

and technologies for gathering, providing access to, and analyzing data for the purpose of

helping enterprise users make better business decisions. The term implies having a

comprehensive knowledge of all of the factors that affect the business. 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 (Negash, 2004). It is

imperative that firms have an in depth knowledge about factors such as the customers,

competitors, business partners, economic environment, and internal operations to make

effective and good quality business decisions. Business intelligence enables firms to make

these kinds of decisions (RANJAN, 2009).

By implementing intelligent information system ranging from Enterprise Resource

Planning (ERP) to Customer Relationship Management (CRM), Supply Chain Management

(SCM) and e-commerce application, many organizations have taken a big step towards

automating business processes. Business Analytics software enables organization to monitor,

capture and analyze the vast amount of data generated by various applications and provide

management and even employees at all levels, with tools necessary to optimize these

processes through strategic and tactical decisions (Jaiswal & Mittal, 2011).

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In simpler terms, Business Intelligence refers to a set of notions, methods and practices

which enable a firm to take informed business decisions. For this purpose, firm uses a variety

of tools, including query and reporting tools, analytical processing tools, data mining and

decision support systems (Jaiswal & Mittal, 2011)

The Fig.1 presents an understanding of BI. A BI system in other words is a combination of

data warehousing and decision support systems. The figure also reveals how data from

disparate sources can be extracted and stored to be retrieved for analysis. The basic BI

functions and reports are shown in fig 1.

Figure 1 Basic Understanding of Business Intelligence

The primary activities include gathering, preparing and analyzing data. The data itself

must be of high quality. The various sources of data is collected, transformed, cleansed,

loaded and stored in a warehouse. The relevant data is for a specific business area that is

extracted from the data warehouse. A BI organization fully exploits data at every phase of the

BI architecture as it progresses through various levels of informational metamorphosis. The

raw data is born in operational environments, where transactional data pours in from every

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source and every corner of the enterprise. Therefore, that is the business intelligent

organization vision: A natural flow of data, from genesis to action. In addition, at each step in

the flow, the data is fully exploited to ensure the increase of information value for the

enterprise. The challenge for BI, of course, is to build any organizations vision

2. Component and features of Business Intelligence

2.1 Business Intelligence Architecture

A business intelligence architecture is a framework for organizing the data, information

management and technology components that are used to build business intelligence

systems for reporting and data analytics. The underlying BI architecture plays an important

role in business intelligence projects because it affects development and implementation

decisions (Jaiswal & Mittal, 2011). A successful BI architecture, as seen in figure 2 has four

parts:

1. Information architecture

2. Data architecture

3. Technical architecture

4. Product architecture

Figure 2 Business Intelligence Architecture

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INFORMATION ARCHITECTURE

Information management architectural components are used to transform raw transaction data

into a consistent and coherent set of information that is suitable for BI uses. For example, this

part of a BI architecture typically includes data integration, data cleansing and the creation of

data dimensions and business rules that conform to the architectural guidelines. It may also

define structures for data warehousing or for a data federation approach that aggregates

information in virtual databases instead of physical data warehouses or data marts.

DATA ARCHITECTURE

The data components of a BI architecture include the data sources that corporate executives and

other end users need to access and analyze to meet their business requirements. Important

criteria in the source selection process include data currency, data quality and the level of detail

in the data. Both structured and unstructured data may be required as part of a BI architecture,

as well as information from both internal and external sources.

TECHNICAL ARCHITECTURE

The technology components are used to present information to business users and enable them

to analyze the data. This includes the BI software suite or BI tools to be used within an

organization as well as the supporting IT infrastructure i.e., hardware, database software and

networking devices. There are various types of BI applications that can be built into an

architecture: reporting, ad hoc query, data mining and data visualization tools, plus online

analytical processing (OLAP) software, business intelligence dashboards and performance

scorecards.

PRODUCT ARCHITECTURE

The product architecture includes the BI software, which are a combination of data-capturing

tools, analysis-and-reporting tools, data warehousing tools, and data-mining tools. Some to the

BI and DM software available in the market are Intelligent Miner by IBM, Enterprise Miner by

SAS, Oracle data mining by Oracle, and SPSS data mining by SPSS.

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2.2 Changing Business Environments and Computerized Decision Support

In the 1990s, the database management systems (DBMS) world was facing a crisis. DBMS

vendors such as IBM, Digital Equipment, Oracle, Ingres, and others had spent much of the latter

part of the 1980s trying to develop distributed versions of their respective core database

products. With the explosion of personal computers and minicomputers during the 1980s,

corporate data assets were increasingly dispersed among hundreds or even thousands of

different platforms throughout the enterprise. The idea behind a distributed DBMS (DDBMS)

product was that a single enterprise-wide data management layer would provide various types

of transparency services (e.g., location transparency, platform transparency, and data format

transparency) and treat these physically dispersed stores of data as if they were really a single,

logically centralized, and homogeneous database. For example, a single query could be executed

against the DDBMS layer that would, using its own directory and metadata (a database term for

data about data) information, determine that three different databases would need to be

accessed at execution time to merge and organize the requested information and present the

combined results back to the user or requesting application.

Without going into a lot of detail, DDBMS technology failed, and organizations entered the

1990s facing an ever-worsening islands of data problem. Data management strategists began

looking at alternatives to the failed DDBMS approach to dealing with this situation, and the idea

of data warehousing was born. Basically, data warehousing took a something old, something

new approach to the islands of data problem: if it was too difficult to reach out at execution

time to many different distributed, heterogeneous stores of data throughout the enterprise, why

not preload (e.g., copy) selected groups of data from different databases and file systems into a

single new database, where that content would be consolidated, cleansed, and staged, ready

for use? The something old portion of this approach is that most organizations were doing

something like this already in the form of extract files, in which they would extract data from

their legacy systems and move that data into a flat file for simple querying or generation of

standard reports.

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Data warehousing took off, though, for a couple of reasons:

1. Whereas DDBMS technology had been thought of as a solution for both transactional and

informational/analytical applications, organizations who built and deployed data

warehouses typically focused their usage on the informational/analytical side to generate

reports, analyze trends, and so on. Eventually, the term business intelligence came to

represent the spectrum Background: A Look Back at the 1990s of different analytically

focused usage and interaction models for an underlying data warehouse.

2. Instead of flat files, data warehouses were typically built on top of either a relational

database (taking advantage of the maturation and increasing acceptance of RDBMSs as

successors to earlier pointer-based, relatively inflexible database models) or a new

generation of proprietary dimensional database products (e.g., IRIs Express or Arbors

Essbase) that were specially architected for data analysis instead of transaction

processing. While many data warehousing professionals became caught up in the

relational versus proprietary database wars of the mid-1990s, the reality was that both

were vast improvements over flat extract files, helping to facilitate the growth and

acceptance of data warehousing. (Simon & Snaffer, 2010)

In this rapidly changing world consumers are now demanding quicker more efficient service

from businesses. To stay competitive companies must meet or exceed the expectations of

consumers. Companies will have to rely more heavily on their business intelligence systems to

stay ahead of trends and future events. Business intelligence users are beginning to demand Real

time Business Intelligence] or near real time analysis relating to their business, particularly in

frontline operations. They will come to expect up to date and fresh information in the same

fashion as they monitor stock quotes online. Monthly and even weekly analysis will not suffice.

In the not too distant future companies will become dependent on real time business information

in much the same fashion as people come to expect to get information on the internet in just one

or two clicks.

Also in the near future business information will become more democratized where end users

from throughout the organization will be able to view information on their particular segment to

see how it's performing.

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So, in the future, the capability requirements of business intelligence will increase in the same

way that consumer expectations increase. It is therefore imperative that companies increase at

the same pace or even faster to stay competitive. Once such blueprint of business intelligence

strategy adopted by several firms is shown in figure 2.

Figure 3 The Business Pressures - Responses - Support Model

2.3 NEED FOR BUSINESS INTELLIGENCE

Business Intelligence enables organizations to make well informed business decisions and

thus can be the source of competitive advantages. This is especially true when firms are able to

extrapolate information from indicators in the external environment and make accurate

forecasts about future trends or economic conditions. Once business intelligence is gathered

effectively and used proactively then the firms can make decisions that benefit the firms.

The ultimate objective of business intelligence is to improve the timeliness and quality of

information. Timely and good quality information is like having a crystal ball that can give an

indication of what's the best course to take. Business intelligence reveals:

1. The position of the firm as in comparison to its competitors

2. Changes in customer behavior and spending patterns

3. The capabilities of the firm in business domain.

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4. Market conditions, future trends, demographic and economic information in the business

domain

5. The social, regulatory, and political environment in the business domain.

6. What the other firms in the market are doing?

Businesses realize that in this very competitive, fast paced and ever-changing business

environment, a key competitive quantity is how quickly they respond and adapt to change.

Business intelligence enables them to use information gathered to quickly and constantly

respond to changes.

2.4 DESIGNING AND IMPLEMENTING A BUSINESS INTELLIGENCE

When implementing a BI programme one might like to pose a number of questions and take

a number of resultant decisions, such as:

Goal Alignment queries: The first step determines the short and medium-term purposes of the

programme. What strategic goal(s) of the organization will the programme address? What

organizational mission/vision does it relate to? A crafted hypothesis needs to detail how this

initiative will eventually improve results / performance (i.e. a strategy map).

Baseline queries: Current information-gathering competency needs assessing. Does the

organization have the capability of monitoring important sources of information? What data does

the organization collect and how does it store that data? What are the statistical parameters of

this data, e.g. how much random variation does it contain? Does the organization measure this?

Cost and risk queries: The financial consequences of a new BI initiative should be estimated. It is

necessary to assess the cost of the present operations and the increase in costs associated with

the BI initiative? What is the risk that the initiative will fail? This risk assessment should be

converted into a financial metric and included in the planning.

Customer and Stakeholder queries: Determine who will benefit from the initiative and who will

pay. Who has a stake in the current procedure? What kinds of customers/stakeholders will

benefit directly from this initiative? Who will benefit indirectly? What are the quantitative /

qualitative benefits? Is the specified initiative the best way to increase satisfaction for all kinds

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of customers, or is there a better way? How will customers' benefits be monitored? What about

employees, shareholders, distribution channel members?

Metrics-related queries: These information requirements must be operationalized into clearly

defined metrics. One must decide what metrics to use for each piece of information being

gathered. Are these the best metrics? How do we know that? How many metrics need to be

tracked? If this is a large number (it usually is), what kind of system can be used to track them?

Are the metrics standardized, so they can be benchmarked against performance in other

organizations? What are the industry standard metrics available?

Measurement Methodology-related queries: One should establish a methodology or a

procedure to determine the best (or acceptable) way of measuring the required metrics. What

methods will be used, and how frequently will the organization collect data? Do industry

standards exist for this? Is this the best way to do the measurements? How do we know that?

Results-related queries: Someone should monitor the BI programme to ensure that objectives

are being met. Adjustments in the programme may be necessary. The programme should be

tested for accuracy, reliability, and validity. How can one demonstrate that the BI initiative (rather

than other factors) contributed to a change in results? How much of the change was probably

random?

3. TOOLS AND TECHNIQUES

3.1 Business Intelligence Tools

Business intelligence tools are a type of application software designed to retrieve, analyze,

transform and report data for business intelligence. The tools generally read data that have been

previously stored, often, though not necessarily, in a data warehouse or data mart. Each vendor

typically defines Business Intelligence their own way, and markets tools to do BI the way that

they see it.

Business intelligence includes tools in various categories, including the following:

AQL - Associative Query Logic

Scorecarding

Business Performance Management and Performance Measurement

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Business Planning

Business Process Re-engineering

Competitive Analysis

Customer Relationship Management (CRM) and Marketing

Data mining (DM), Data Farming, and Data warehouses

Decision Support Systems (DSS) and Forecasting

Document warehouses and Document Management

Enterprise Management systems

Executive Information Systems (EIS)

Finance and Budgeting

Human Resources

Knowledge Management

Mapping, Information visualization, and Dash boarding

Management Information Systems (MIS)

Geographic Information Systems (GIS)

Online Analytical Processing (OLAP) and multidimensional analysis

Real time business intelligence

Statistics and Technical Data Analysis

Supply Chain Management/Demand Chain Management

Systems intelligence

Trend Analysis

User/End-user Query and Reporting

Web Personalization and Web Mining

Text mining

BI often uses Key performance indicators (KPIs) to assess the present state of business and to

prescribe a course of action. More and more organizations have started to make more data

available more promptly. The term business intelligence represents the tools and systems that

play a key role in the strategic planning process of the corporation. These systems allow a

company to gather, store, access and analyze corporate data to aid in decision-making. Generally

these systems will illustrate business intelligence in the areas of customer profiling, customer

support, market research, market segmentation, product profitability, statistical analysis, and

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inventory and distribution analysis to name a few. Most companies collect a large amount

of data from their business operations. To keep track of that information, a business and would

need to use a wide range of software programs tools discussed as under:

1) End-user Query, Reporting and Analysis (QRA)

These tools include query, reporting, and multidimensional analysis or on-line analytical

processing tools. Query and reporting tools are designed specifically to support ad hoc data

access and report building by even the most novice users. QRA tools provide a multidimensional

data management environment and are typically used for interactive manipulation of data based

on various aggregations.

2) OLAP (On-line analytical processing)

It refers to the way in which business users can slice and dice their way through data using

sophisticated tools that allow for the navigation of dimensions such as time or hierarchies. Online

Analytical Processing or OLAP provides multidimensional, summarized views of business data and

is used for reporting, analysis, modeling and planning for optimizing the business. OLAP

techniques and tools can be used to work with data warehouses or data marts designed for

sophisticated enterprise intelligence systems. These systems process queries required to

discover trends and analyze critical factors. Reporting software generates aggregated views of

data to keep the management informed about the state of their business. Other BI tools are used

to store and analyze data, such as data mining and data warehouses; decision support systems

and forecasting; document warehouses and document management; knowledge management;

mapping, information visualization, and dash boarding; management information systems,

geographic information systems; Trend Analysis; Software as a Service (SaaS).

3) Advanced Analytics

It is referred to as data mining, forecasting or predictive analytics, this takes advantage of

statistical analysis techniques to predict or provide certainty measures on facts.

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4) Corporate Performance Management (Portals, Scorecards, Dashboards)

This general category usually provides a container for several pieces to plug into so that the

aggregate tells a story. For example, a balanced scorecard that displays portlets for financial

metrics combined with say organizational learning and growth metrics.

5) Real time BI

It allows for the real time distribution of metrics through email, messaging systems and/or

interactive displays.

6) Data Warehouse and data marts

The data warehouse is the significant component of business intelligence. It is subject

oriented, integrated. The data warehouse supports the physical propagation of data by handling

the numerous enterprise records for integration, cleansing, aggregation and query tasks. It can

also contain the operational data which can be defined as an updateable set of integrated data

used for enterprise wide tactical decision-making of a particular subject area. It contains live data,

not snapshots, and retains minimal history. Data sources can be operational databases, historical

data, external data for example, from market research companies or from the Internet), or

information from the already existing data warehouse environment. The data sources can be

relational databases or any other data structure that supports the line of business applications.

They also can reside on many different platforms and can contain structured information, such

as tables or spreadsheets, or unstructured information, such as plaintext files or pictures and

other multimedia information. A data mart as described by (Inmon, 1999) is a collection of

subject areas organized for decision support based on the needs of a given department. Finance

has their data mart, marketing has theirs, and sales have theirs and so on. And the data mart for

marketing only faintly resembles anyone else's data mart. Perhaps most importantly, (Inmon,

1999) the individual departments own the hardware, software, data and programs that

constitute the data mart. Each department has its own interpretation of what a data mart should

look like and each department's data mart is peculiar to and specific to its own needs. Similar to

data warehouses, data marts contain operational data that helps business experts to strategize

based on analyses of past trends and experiences. The key difference is that the creation of a

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data mart is predicated on a specific, predefined need for a certain grouping and configuration

of select data. There can be multiple data marts inside an enterprise. A data mart can support a

particular business function, business process or business unit. A data mart as described by

(Inmon, 1999)is a collection of subject areas organized for decision support based on the needs

of a given department. Finance has their data mart, marketing has theirs, and sales have theirs

and so on. And the data mart for marketing only faintly resembles anyone else's data mart. BI

tools are widely accepted as a new middleware between transactional applications and decision

support applications, thereby decoupling systems tailored to an efficient handling of business

transactions from systems tailored to an efficient support of business decisions. The capabilities

of BI include decision support, online analytical processing, statistical analysis, forecasting, and

data mining. The following are the major components that constitute BI.

7) Data Sources

Data sources can be operational databases, historical data, external data for example, from

market research companies or from the Internet), or information from the already existing data

warehouse environment. The data sources can be relational databases or any other data

structure that supports the line of business applications. They also can reside on many different

platforms and can contain structured information, such as tables or spreadsheets, or

unstructured information, such as plaintext files or pictures and other multimedia information.

3.2 Business Intelligence Techniques

Any new-form organization now a days experience is the value chain, which is set of primary

secondary activities that create value for customers. (Denison, 1999) examines several critical

activities related to value chain. Without effective BI to target process-oriented organizations for

supporting, this is not possible. (Davenport, 1993) describes various issues on re-engineering in

business process innovation.

According to (Adelman, Larissa, & Barbusinski, 2015), BI is a term that encompasses a broad

range of analytical software and solutions for gathering, consolidating, analyzing and providing

access to information in a way that is supposed to let an enterprise's users make better business

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decisions. (Malhotra, 2000) describes BI that facilitates the connections in the new-form

organization, bringing real-time information to centralized repositories and support analytics

that can be exploited at every horizontal and vertical level within and outside the firm. BI

describes the result of in-depth analysis of detailed business data, including database and

application technologies, as well as analysis practices. BI is technically much broader, potentially

encompassing knowledge management, enterprise resource planning, decision support systems

and data mining (Gangadharan & Sundaravalli, 2004).

(Nguyen, Schiefer, & Min, 2005) Introduced an enhanced BI architecture that covers the

complete process to sense, interpret, predict, automate and respond to business environments

and thereby aims to decrease the reaction time needed for business decisions. (Nguyen, Schiefer,

& Min, 2005) proposed an event-driven IT infrastructure to operate BI applications which enable

real-time analytics across corporate business processes, notifies the business of actionable

recommendations or automatically triggers business operations, and effectively closing the gap

between Business Intelligence systems and business processes.

(Andhreas & Josef, 2005) suggest an architecture for enhanced Business Intelligence that

aims to increase the value of Business Intelligence by reducing action time and interlinking

business processes into decision making. Businesses no longer want what has happened but they

want to know the underlying reasons. Rather than knowing how many blankets were sold in

December, businesses want to understand how many were sold in china during a storm. BI

provides unified integrated view of business activities. A retailer knows how many blankets were

sold in December across India and therefore make better purchasing and stock management

decision for the upcoming year. Enterprises are building business intelligence systems that

support business analysis and decision making to help them better understand their operations

and compete in the marketplace. Innovation in data storage technology is now significantly

outpacing progress in computer processing power, heralding a new era for real-time BI. As a

result, some software vendors with superior tools offer a complete suite of analytic BI

applications, tools and data models that enable organizations to tap into the virtual treasure

trove of information. The tools provide easy access to corporate and enterprise wide data and

convert that data into useful and actionable information that is consistent across the

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organizationone coherent version of the truth. Companies still fee that BI has technology

related complexities and usable only by technically savvy specialists. They also feel that BI is

expensive. BI takes a long time to yield correct analysis. The firms want these analyses in real

time for short-term projects. The tradition BI may not do this but a real time BI environment

certainly comes into rescue. Data is finally treated as the corporate resource in a new discipline.

Any operational system (including ERP and CRM) and any decision support application (including

data warehouses and data marts) are BI, if and only if they were developed under the umbrella

and methodology of a strategic cross-organizational initiative (Gangadharan & Sundaravalli,

2004). Traditional BI systems consist of a back-end database, a front-end user interface, software

that processes the information to produce the business intelligence itself, and a reporting system.

The capabilities of BI include decision support, online analytical processing, statistical analysis,

forecasting, and data mining. Several varied sectors like manufacturers, electronic commence

businesses, telecommunication providers, airlines, retailers, health systems, financial services,

bioinformatics and hotels use BI for customer support, market research, segmenting, product

profitability, inventory and distribution analysis, statistical analysis, multi-dimensional reports,

detecting fraud detection etc. Business Intelligence and data mining is a field that is heavily

influenced by traditional statistical techniques, and most data-mining methods will reveal a

strong foundation of statistical and data analysis methods. Some of the traditional data-mining

techniques include classification, clustering, outlier analysis, sequential patterns, time series

analysis, prediction, regression, link analysis (associations), and multidimensional methods

including online analytical processing (OLAP). These can then be categorized into a series of data-

mining techniques, which are classified and illustrated in Table 1 (Goebel & Gruenwald, 1999).

Table 1 Current BI Techniques

TECHNIQUE

DESCRIPTION

Predictive modeling

Predict value for a specific data item attribute.

Characterization and descriptive data mining

Data distribution, dispersion and exception.

Association, correlation, causality analysis

(Link Analysis)

Identify relationships between attributes.

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Classification

Determine to which class a data item belongs.

Clustering and outlier analysis

Partition a set into classes, whereby items

with similar characteristics are grouped

together.

Temporal and sequential patterns analysis

Trend and deviation, sequential patterns,

periodicity.

OLAP (On-line Analytical Processing)

OLAP tools enable users to analyze different

dimensions of multidimensional data. For

example, it provides time series and trend

analysis views.

Model Visualization

Making discovered knowledge easily

understood using charts, plots, histograms,

and other visual means.

Exploratory Data Analysis (EDA)

Explores a data set without a strong

dependence on assumptions or models; goal

is to identify patterns in an exploratory

manner.

In addition, the entire broad field of data mining includes not only a discussion of

statistical techniques, but also various related technologies and techniques, including data

warehousing, and many software packages and languages that have been developed for the

purpose of mining data. Some of these packages and languages include: DBMiner, IBM Intelligent

Miner, SAS Enterprise Miner, SGI MineSet, Clementine, MS/SQLServer 2000, DBMiner,

BlueMartini, MineIt, DigiMine, and MS OLEDB for Data Mining (Goebel & Gruenwald, 1999).

4. ROLES, FUNCTION AND BENEFITS OF BUSINESS INTELLIGENCE

BI provides many benefits to companies utilizing it. It can eliminate a lot of the guesswork

within an organization, enhance communication among departments while coordinating

activities, and enable companies to respond quickly to changes in financial conditions, customer

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preferences, and supply chain operations. BI improves the overall performance of the company

using it.

Information is often regarded as the second most important resource a company has (a

company's most valuable assets are its people). So when a company can make decisions based

on timely and accurate information, the company can improve its performance. BI also expedites

decision-making, as acting quickly and correctly on information before competing businesses do

can often result in competitively superior performance. It can also improve customer experience,

allowing for the timely and appropriate response to customer problems and priorities.

The firms have recognized the importance of business intelligence for the masses has arrived.

Some of them are listed below (RANJAN, 2009).

1. With BI superior tools, now employees can also easily convert their business knowledge via

the analytical intelligence to solve many business issues, like increase response rates from

direct mail, telephone, e-mail, and Internet delivered marketing campaigns.

2. With BI, firms can identify their most profitable customers and the underlying reasons for

those customers loyalty, as well as identify future customers with comparable if not greater

potential.

3. Analyze click-stream data to improve e-commerce strategies.

4. Quickly detect warranty-reported problems to minimize the impact of product design

deficiencies.

5. Discover money-laundering criminal activities.

6. Analyze potential growth customer profitability and reduce risk exposure through more

accurate financial credit scoring of their customers.

7. Determine what combinations of products and service lines customers are likely to purchase

and when.

8. Analyze clinical trials for experimental drugs.

9. Set more profitable rates for insurance premiums.

10. Reduce equipment downtime by applying predictive maintenance.

11. Determine with attrition and churn analysis why customers leave for competitors and/or

become the customers.

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12. Detect and deter fraudulent behavior, such as from usage spikes when credit or phone cards

are stolen.

13. Identify promising new molecular drug compounds.

Customers are the most critical aspect to a company's success. Without them a company

cannot exist. So it is very important that firms have information on their preferences. Firms must

quickly adapt to their changing demands. Business Intelligence enables firms to gather

information on the trends in the marketplace and come up with innovative products or services

in anticipation of customer's changing demands.

Competitors can be a huge hurdle on firms way to success. Their objectives are the same as

firms and that is to maximize profits and customer satisfaction. In order to be successful firms

must stay one step ahead of the competitors. In business we don't want to play the catch up

game because we would have lost valuable market share. Business Intelligence tells what actions

our competitors are taking, so one can make better informed decisions.

Business intelligence provides organizational data in such a way that the organizational

knowledge filters can easily associate with this data and turn it into information for the

organization. Persons involved in business intelligence processes may use application software

and other technologies to gather, store, analyze, and provide access to data, and present that

data in a simple, useful manner. The software aids in Business performance management, and

aims to help people make "better" business decisions by making accurate, current, and relevant

information available to them when they need it. Some businesses use data warehouses because

they are a logical collection of information gathered from various operational databases for the

purpose of creating business intelligence.

In order for BI system to work effectively there must be some technical constraints in place.

BI technical requirements have to address the following issues (RANJAN, 2009):

Security and specified user access to the warehouse

Data volume (capacity)

How long data will be stored (data retention)

Benchmark and performance targets

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People working in business intelligence have developed tools that ease the work, especially

when the intelligence task involves gathering and analyzing large quantities of unstructured data.

Each vendor typically defines Business Intelligence their own way, and markets tools to do BI the

way that they see it.

5. INDUSTRY EXAMPLES: USE OF BUSINESS INTELLIGENCE

5.1 CASE 1: Make My Trip

BACKGROUND

One of Indias largest on-line travel portal for flight Bookings, hotel Bookings, tour packages, and

other related bookings for Indian & International Markets. It serves retail as well as corporate

customers by providing end-to-end Travel Solutions. Make my trip has a captive call center

operations for addressing customer queries and complaints

NEED FOR BI

Contact center environment calls for an agile decision making system where near real time

information on key metrics is extremely critical. The contact center team was looking at a data

analysis platform that can help them consolidate and analyze customer calls data & other related

information coming from multiple systems. In the older reporting system used by Make my Trip,

the business logics used for classifying customer calls under different categories were becoming

difficult to be handled. With the need for trend analysis on historical customer calls data and the

capability of slicing & dicing the same, the conventional reports were short-lived and need for a

flexible BI Tool was imminent.

CHALLENGES FACED AT MAKE MY TRIP

Increasing call volumes, complex calculation logic and relevant tagging of customer calls from the

source systems was turning out to a major challenge in the excel based reporting system that

customer was using. Dependence on MIS teams for data collation and report generation was

time-consuming activity and left relatively lesser time for business users for data analysis.

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Management team had limited access to consolidated business views across business functions

and whatever was available was mostly static. The challenges were bigger for analysis scenarios

where data has to be merged from multiple systems such as CMS, ECHI, CRM, IVR etc.

BUSINESS INTELLIGENCE SOLUTION

The BI Solution implemented at Make my Trip was an integrated system consolidating data from

the Contact Center Application, CRM System and few excel spreadsheets. Implementation of

Customer Repeat Calls Analysis by tagging calls from the calls data and classifying them under

different buckets was successfully achieved in the BI System. The BI solution at Make my Trip had

a SAP business intelligence comprising of analytics and dashboard interface. Custom Analytical

Reports providing insights into different aspects of customer calls analysis across dimensions

such as Agent, Customer Name, Skillset, Customer Segment etc. were developed and assigned to

different users based on their roles & responsibilities.

BENEFITS DELIVERED

MIS Team and business users at Make My Trip were able to save a substantial amount of time

and gain insights into their data on few clicks.

SOURCE: (ProGen International, 2015)

5.2 CASE 2: Ortho Max

BACKGROUND

Ortho Max engages in the development, manufacture, and sale of medical technologies. The new

unit located at Vadodara has been equipped with all modern manufacturing facilities. The

Manufacturing activities of orthopaedic instruments are done here. With business competition

increasing, Ortho Max was keen to install business intelligence software to improve their

competence in the market.

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CHALLENGES FACED AT ORTHO MAX PLANT

Unable to leverage full functionality of their existing VCP solutions due to combination of

configuration, data and process issues

Operations are heavily impacted reducing productivity

80% of items are managed on exception basis

BUSINESS INTELLIGENCE SOLUTION

Two day workshop was conducted to understand the current challenges in plant. The approach

was focused upon improving the current capabilities by VCP AMS. Data group were mapped

through usage of Advanced Planning Command Center. Oracle Analytics, dashboards, decision

support systems and online analytical processing were used to recommend improvements to the

sales and operations planning process.

Figure 4 Business Intelligence model adopted at Ortho Max

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BENEFITS DELIVERED

Able to leverage existing investment and get rapid benefits through optimizing current VCP

solution based process

Simplified application maintenance

Achieved increased operational efficiency through less disruptions to operations processes

Lays the foundation to provide solutions that can scale the sales and operations process for

increase revenue growth

SOURCE: (Laha, 2011)

5.3 CASE 3: Little Rock India School

BACKGROUND

Little Rock India School is one of the leading residential school in India. Located in Karnataka,

more than 3000 residential student are doing their school here. National recognition to Little

Rock came in the form of the Computer Literacy Excellence Award of the Ministry of Information

Technology, government of India. Little Rock was adjudged the best school in computer

education in Karnataka State. The school authorities were keen to develop an online student

information systems to inform the parents of student at distant location about the progress of

their student.

CHALLENGES FACED AT LITTLE ROCK INDIA SCHOOL

More than 3000 students are doing their schooling in Little Rock India School. In order to bring

about improvements in the grade and marks of the students, the school management decided to

move all the report cards frequently to their parents so that each parent can review their wards

progress in terms of academics and have a close watch of their ward. The decision was greatly

appreciated by all the parents and the challenge before the school management was to manage

the huge administration work involved as all the reports which includes unit tests, quarterly tests,

half yearly, annual year tests had to be printed and posted.

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BUSINESS INTELLIGENCE SOLUTION

A web application was developed using CRM management where all the reports can upload to one

single location on a web-server by the school management team as soon as the results are

published. Each parent will have a login to this system and they will have the provision to view

their wards results as well as their previous results summary. This will help them to analyze closely

the progress of their wards academics.

BENEFITS DELIVERED

On successfully implementing this system, now students are more cautious on their academic

performances and parents are more aware of their wards progress. The school management was

able to cut great costs connected with administration and paper works and organizing parent-

teachers meetings very frequently. Now, the results of small unit tests as well are easily

accessible for every parent as they have their own login to this application and are able to analyze

and compare their wards scores with the other students in the same class. The school is now

benefited, gained great recognition compared to other schools in the same locality and the

parents are much happier as they have less meetings and great visibility on their wards.

SOURCE: (Sesame Technology, 2015)

5.4 CASE 4: Ananda Bazar Patrika

BACKGROUND

Ananda Bazar Patrika is an Indian Bengali language newspaper founded in 1922 by ABP group.

According to an Indian readership survey, it is the only major Bengali newspaper in India and has

an average issue readership of 5.8 million.Along with daily newspaper it also publishes the

periodicals, books. The company is operated through different sales areas and print locations.

ABP group has evolved into a media corporation that has eleven premier publications, three 24-

hour national TV news channels, one leading book publishing business as well as mobile and

internet properties

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CHALLENGES FACED AT ANANDA BAZAR PATRIKA

In the subscription part of the business, customer relationship management was the biggest

challenge for ABP. Along with that there were major challenges that ABP were facing of

maintenance and the roll out for the implemented data.

Maintenance of the CRM, ECC application

Several integration issues from CRM to ECC MSD (Media Sales and Distribution) application.

Issues in day to day activities.

Handling marketing / service related issues in CRM.

Roll out for the existing implementations

Lacked understanding of the existing CRM, ECC applications led to difficulties in roll out for

other locations in ABP.

Skills, knowledge and capabilities

Lacked understanding of the solution which led to problems in permanent and timely

resolution of issues.

Critical business financial month end closures

Delayed processes and resolution of issues that had a direct impact on business reporting

along with closing their financial periods in agreed timeframe.

Lack for clarity in processes

Communication with other IT teams were not clear and responsive

Taking ownership of issues

Passing issues to other teams without clear analysis and not following up until full

resolution.

A failing CRM system which was hard to integrate with subscription, retail and finance.

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BUSINESS INTELLIGENCE SOLUTION

ABP has adopted industry specific solutions over media and customer relationship management

with CRM module. Sales, service, and marketing are three major areas which are implemented

in CRM which has seamless integration with SAP ECC.

SAP CRM marketing - marketing planning, campaign management, lead management,

market analytics,

SAP CRM sales - telesales, enterprise sales, opportunity management, customer order

management, commission and incentives, sales planning and analytics.

SAP CRM Service service request, service order, complaint management, service planning

and analytics.

IS-Media application in SAP is configured as per ABP requirements with a complete solution, best

practices, detail audit trail logs and seamless integration with other ERP modules. Renewal

subscription B2B solution was the key solution provided with important processes such as;

accepting advance payments, liability account update, revenue account update and customer

refunds. Along with this, several roll outs were performed as per the ABP's business requirement

and have been working in very stable manner.

BENEFITS DELIVERED

End user satisfaction and confidence with effective SAP business suit to manage customer

relationship.

SAP CRM has provided extensive marketing / service / sales functionalities which enabled

ABP management to analyze the information proactively and increased strategic decision

making.

SAP CRM is helping an organization to stay connected to customers in all aspects as it is very

user friendly, easily customizable and fully integrated.

Reduced time for month end processing to allow the business to close quickly and efficiently.

Strong and seamless integration in SAP ERP modules reduced dependency among users.

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Less paper work.

SOURCE: (Invenio Business Solution, 2015)

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