Role of Business Intelligence

In Knowledge Management

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Shakthi Fernando

BSc. Financial Management (Special)-Undergraduate

Sabaragamuwa University of Sri Lanka

2014 April

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Abstract

This study is fundamentally based on the most common components of a Business

Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining,

which comfort the decision making function. It further describe about the role of each

component in a Business Intelligence System and how Business Intelligence Systems

can be used for better business decision making at each level of management.

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Content

Page

Introduction 04

Knowledge Management and Business Intelligence 05

Data warehouses 06

ETL tools 07

On-Line Analytical Processing techniques 08

Data mining 09

Role of components in a Business Intelligence System 10

Role of Business Intelligence in each level of management 12

References 14

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Introduction

There is no any controversy that the knowledge has become one of most important

features in businesses. A business cannot be developed and cannot even survive

without a proper torrent of knowledge. As well as businesses should manage it well in

order to get the maximum results out of knowledge. As a result of that Knowledge

Management has been identified as one of the fastest growing areas of software

investment. Knowledge in an organization which cannot be disseminated and

communicated with others is considered as useless. When it is shared throughout the

organization it becomes useful to the organization. Knowledge can be disseminated

within an organization via Google sites, social networking, e-mail and etc.

It is said that the major source of wealth is the production and distribution of

information and knowledge since we live in an information economy. It has been

calculated that 55 percent of the United States labor force consists of knowledge and

information workers, and 60 percent of the gross domestic product of the United

States are from the knowledge and information sectors (Laudon, 2011, pp. 417).

Businesses have identified the importance of Knowledge Management. They have

recognized that mostly the value of the firm depends on its ability to gain and manage

knowledge. It has found that a substantial part of a stock market value of a company

is related to firm’s intangible assets, which consists knowledge as an important

component.

When it comes to Knowledge Management, Business Intelligence is playing a vital

role. It says Business Intelligence is used to recognize the capabilities of the firm,

trends, patterns, technologies, future directions in the market and the regulatory

environment in which the organization operates. Same as that it is used to identify the

actions of competitors. Business Intelligence basically supports to the Knowledge

Management function. In fact it is ultimately backing to the decision making function,

which is known as one of the most important function in an organization.

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Knowledge Management and Business

Intelligence

Knowledge Management can be elaborated as a systematic process of finding,

selecting, organizing, distilling and presenting information in a way that improves the

organization’s understanding in a specific area of interest. Knowledge Management

comforts an organization to gain the understanding from its own experiences. Certain

activities of Knowledge Management focus on acquiring, storing and utilizing

knowledge in an organization for strategic planning, dynamic learning, problem

solving and decision making.

Knowledge Management technologies support to create, store, retrieve, distribute and

analyze structured and unstructured information. Those Knowledge Management

technologies enhance capabilities of helping to solve current problems, answer

questions and realize new opportunities. There is a vast amount of data in larger firms

including business documents, spreadsheets, databases, e-mails, reports, technical

journals, news and press articles, web documents and contracts. Knowledge

Management technologies are used to search, organize and extract value from these

information sources and focus on development activities.

Business Intelligence focuses on the similar purpose, but from a different point of

view. Business intelligence systems are used for intelligent exploration, integration,

aggregation, and analysis of data originated from various information sources. It

concerns with decision making using data warehousing and On-line analytical

processing techniques. Data warehousing collects relevant data, where it is organized.

Then it can support to decision making objectives. Same as that Business Intelligence

uses data mining and ETL tools for making the decision making function into a more

efficient one.

It is identified data warehouses, ETL tools, OLAP techniques and data mining as most

common components of a Business Intelligence System. At the same time it highly

supports to Business Intelligence System to comfort the decision making process.

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Data warehouses

A data warehouse is identified as a database, which stores current and historical data

of the organization. Then it can be analyzed to comfort decision making process. Data

warehouses are filled with data that has been extracted from distributed databases. It

has data which has been originated from various core operational transaction systems

such as manufacturing, sales, customer accounts as well as internet based website

transactions. The data warehouses are filled with information from different

operational databases so that the information can be used all over the organization

with the purpose of decision making and management analysis.

New data warehouses are frequently being filled with business data in order to verify

that the updated data is available for decision making function. As a result of that

decisions which are taken by management are efficient, since those are based on

historical as well as up to minute updated data. In some organizations, they have

created smaller, decentralized warehouses, called data marts rather than a centralized,

enterprise-wide data warehouse which serves the entire entity. Data mart is a sub set

of a data warehouse, where the summarized or most important portion of entity’s data

is placed.

The idea of data warehouses is practically used by Catalina Marketing, which is

recognized as a global marketing firm for major consumer packaged goods,

companies and retailers. It operates an enormous data warehouse, which includes

purchase history of three years. It is considered as the largest loyalty database in the

world. In that case customers’ buying preferences are determined by analyzing the

database of customer purchase histories. When a purchase is done, it is instantly

analyzed with the customer’s buying history in the data warehouse (Laudon, 2011, pp.

222-223).

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Same as that the United States Internal Revenue Service (IRS) maintains a data

warehouse, which amalgamates tax payer data, in different systems. It includes

personal information of tax payers as well as tax returns. In that case the warehouse

integrates the data of tax payers from many sources and it makes analyzing part much

easier. It provides a descriptive image of tax payers. Then Internal Revenue Service

(IRS) can identify tax payers, who are most likely to cheat on their income (Laudon,

2011, pp. 223).

ETL tools

ETL tools are responsible for extracting the data from source systems. It transforms

data to a common format from different formats. Then those are loaded into the data

warehouses. Earlier ETL designs and implementation was identified as a supporting

task for the data warehouse. It was not considered as a part of the business

intelligence, but a subset of the data warehousing. But now it has become one of the

most common components of a Business Intelligence System.

ETL solutions have been divided into three stages that find, convert data from various

sources and transfer the results into a data warehouse. The extraction stage involves

with obtaining access to data, which is originated from different sources. The

transformation stage involves with transforming the extracted data. It is considered as

the most complex stage of the ETL process. The load stage involves with loading the

data warehouses with data.

Simply the task of ETL tools is to fulfill the requirements of a business intelligence

system. It extracts data which is in different formats, convert them and load them into

the appropriate data warehouse. The use of ETL tools is identified as the most

efficient way to process data with frequency and timing varying according to the

requirements of businesses. Same as that ETL tools can access to a large volume of

data.

Eventually companies need of reporting data is rapidly increasing. On the other hand

the complexity and volume of the data is rocketing. In practical business world

Walmart Company is handling more than one million transactions in every hour

regarding sales, products, inventory and customers by using ETL tools.

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On-Line Analytical Processing (OLAP)

techniques

The utilization of On-Line Analytical Processing is originated with the difficulties

arisen, when data analysis on databases are implemented that are frequently being

updated during transactions via other information systems. Basically On-Line

Analytical Processing is analyzing complex data on a database that are constantly

being updated along with traditional data.

On-Line Analytical Processing can be identified as amelioration of earlier single

dimensional analysis tools that allowed users to analyze data from only one point of

view at a time. It provides a multi-dimensional tool to users. On-Line Analytical

Processing enables users to analyze data from different perspectives and explore it

with the intention of discovering hidden information. Simply On-Line Analytical

Processing backs multi-dimensional data analysis, enabling users to view the same

data in different ways. At the same time On-Line Analytical Processing enables users

to exact online answers even when the data are stored in large databases.

On-Line Analytical Processing allows user access, analysis and modeling of business

problems and sharing of information which is stored in data warehouses. On-Line

Analytical Processing offers techniques for analyzing and drilling data. As well as it

offers tools those are particularly used for generating interactive reports. Basically

On-Line Analytical Processing tools use data mining techniques and statistical

methods to create fast and readable reports that are used for forecasting and strategic

decision making process.

As an example, a leading outsource collection agency for government debts in the

United States has saved $200K in accounting software expenses by using On-Line

Analytical Processing technology. Even it has supported ABC to sell some equity for

$167,000,000 in just nine months.

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Data Mining

Data mining is utilized for further discoveries. Data mining techniques are used to

identify relationships and rules. The data mining process includes discovering various

patterns, regularities, rules and generalizations in data resources. Data mining

provides erudition into corporate data that cannot be obtained with On-Line

Analytical Processing by recognizing hidden patterns and relationships in large

databases and rules from them to predict future behavior. The patterns and rules are

used to direct decision making and forecast the consequences of those decisions.

There are several fundamental strategies for data mining. The most frequently used

strategies are classification, prediction, estimation, time series analysis and market

basket analysis. These strategies are aligned with the requirements of an organization

and support to decision making function by breaking through varies regularities,

patterns, rules and generalizations in data resources. As an example, market basket

analysis can be used in a business to model retail sales. Same as that classification can

be used to classify unstructured data. The types of information which can be obtained

from data mining include associations, sequences, classifications, clusters, and

forecasts.

As a practical example for data mining, Harrah’s Entertainment, the second largest

gambling company in industry, uses data mining to recognize its most profitable

customers with the intention of generating more revenue from them. The company

analyzes data about its customers when they play casinos or use hotels. Harrah’s

marketing department uses this information to build a detailed gambling profile. Then

data mining enables the company to identify the favorite gaming experiences of a

regular customer, hid preferences for room accommodations, restaurants and

entertainment. This information conduct management when decisions are made about

how to amplify the most profitable customers, encourage them to spend more and

attract more customers with high revenue generating potential. Harrah’s profits have

been increased with the use of business intelligence and it has become a core piece of

the firm’s business strategy (Laudon, 2011, pp. 226).

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Role of components in a Business

Intelligence System

Business Intelligence Systems chiefly extract information with the intention of

supporting managers to solve their structured and unstructured issues. Each

component of a Business Intelligence System can be used to extract information by

acquiring information, searching and gathering information, analyzing information

and delivery of information. Same as that Business Intelligence Systems eliminates

communication barriers that exist at the different organizational levels within the

entity by analyzing historical data. Those analysis enable decision makers to evaluate

former activities and direct future actions.

Basically each component of a Business Intelligence System fulfill some tasks in

order to comfort the decision making process. Simply ETL tools are acquiring and

searching information, data warehouses also acquire information. Both On-Line

Analytical Processing techniques and data mining components are analyzing and

delivering the information.

Acquiring information

It has been more difficult to acquire information, since modern organizations use

more distributed information systems to store their business data. This action is

revealing the business issue. It uses ETL tools in order to direct the processes to find

out the needed information and into which data warehouse that the information should

be stored.

Searching information

The newly loaded data which are in high quality are mined using data mining

techniques and processes after the data are extracted from operational databases. This

action is basically implemented at different quality levels of data. Lower quality data

are searched by using ETL tools. The data, which are in higher quality are being

loaded into a data warehouse.

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Analyzing information

Managers have to create data models in order to understand and address business

issues. Managers can analyze information from multiple dimensions by data

preprocessing and applying On-Line Analytical Processing and data mining

techniques. As an example, information derived through analysis is directly affected

to decisions related to forecasting sales, promotional campaigns and financial results.

It can be used in fraud detection in some cases.

On-Line Analytical Processing summarizes data and makes predictions based on

historical data. On the other hand data mining reveals hidden patterns in data. Data

mining operates at a detailed level instead of a summary level. In real words, data

mining predicts while On-Line Analytical Processing forecasts. Data mining and On-

Line Analytical Processing can be used to analyze financial data, marketing data,

customer data, production data, wage related data, personal data, logistical data, etc.

Delivery of information

Data mining can be also used to deliver information within an entity. Data mining in a

Business Intelligence System is not only interpreting and evaluating results generated

from the analysis implemented on data stored in data warehouses. Even it can display

reports that enable decision makers to discover various patterns, regularities and

generalizations. On the other hand On-Line Analytical Processing creates quick

reports by utilizing simple data mining techniques by summarizing data. Same as that

data mining provides a descriptive report, while On-Line Analytical Processing

provides a summary of information. Management will get reports that are

inappropriate for the decision making function and reports which are included too

much information without a well defined delivery.

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Role of Business Intelligence in each

level of management

Simply a Business Intelligence System is collecting, treating and disseminating

information with the intention of reducing the uncertainty of decision making function

in an organization. In this case, organizations need real time data in order to make

decisions. A Business Intelligence System enables users to make decisions by using

real time data by monitoring competition and carrying out continuous analysis of

various data. The Business Intelligence System collects data from various data

sources like operational databases and customer databases, transforms into some

formats. Finally it loads the newly formatted data into data warehouses that are

available to all three level of decision making, operational, tactical and strategic.

Each level of management use different On-Line Analytical Processing techniques

and data mining processes to analyze data and report information that is highly

relevant to them. The information generated from the Business Intelligence System is

being used in all decision making processes. Chiefly decisions at the strategic level

are to set objectives and directed to the tactical level of the organization. At the

tactical level, information is mined from the Business Intelligence System in order to

develop tactics to realize the strategic objectives. On the other hand it pushes

decisions to the operational level of the organization. Different levels of the

organization use information for different purposes. Information provides input to

senior managers at strategic and tactical levels, while information provides input to

lower level managers at the operational level.

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Operational level decisions

Decisions which are made at the operational level related to the continuous

operations. These decisions are basically based on up to date information. Simply

operational level decisions are considered as decisions, which allow an organization

to run its day today activities. Data that is identified from the operational level by

Business Intelligence System is analyzed and combined with other information in

order to support the strategic planning process. As an example, operational level

decisions provide analysis of products, analysis of employees, analysis of regions, etc.

Tactical level decisions

Decisions which are made at the tactical level are related to planning and it is based

on real time data. It is forecasting to direct the future actions of sales, finance,

marketing, as well as capital management. Tactical decisions are used to comfort

strategic decisions. As an example, it forecasts demand for a given product or service,

makes decisions related to marketing, sales, finance, capital management, etc.

Strategic level decisions

Strategic level decisions set objectives as well as ensure that those are attained.

Business Intelligence Systems provide information in order to support to strategic

decisions which are related to the development of future results based on historical

results, profitability and effectiveness of distribution channels. Simply strategic

decisions use Business Information Systems in order to make forecasts based on

historic data, combine them with current performance and estimate how conditions

will be performed in the future. As an example, decisions are made at the strategic

level by using the Business Intelligence System such as whether to enter into a new

market, whether to launch a new product, etc.

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References

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Business Intelligence & Knowledge Management. IBM Systems Journal.

Herschel, R.T. (2005). KM & Business Intelligence: The importance of integration.

Workshop on Knowledge Management & organizational memories,

Edinburgh, Scotland.

Laudon, K.C., & Laudon, J.P. (2011). Management Information Systems: Managing

the digital firm (12th

ed.). Prentice Hall.

Lloyd, J. (2011). Identifying key components of Business Intelligence Systems & their

role in managerial decision making. University of Oregon, Eugene.

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R. (n.d.). Bringing business objects into Extract-Transform-Load (ETL)

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Shehzard, R., & Khan, M.N.A. (2013). Integrating Knowledge Management with

Business Intelligence processes for enhanced organizational learning.

International Journal of Software Engineering & its Applications.