Information systems and management in business Chapter 8 Business Intelligence (BI)

Information systems and management in business

Chapter 8Business Intelligence (BI)

8.1 Introduction

Overview Businesses and organizations gather large volumes

of business data via their operational systems The data is typically kept in relational databases or

large data warehouses In practice this data is left in their relevant

databases, archived or discarded when it has no further operational value

Traditional management and executive information systems are not geared to analyzing the data in a manner which is capable of discovering business value that lies hidden within such large volume of data

BI systems are geared to fit this role

8.2 Business Intelligence (BI) Vs Knowledge Management

knowledge management (KM) The process and strategies with which an

organization creates, capture, store, use, distribute, and share its intellectual assets is a concept that is typically referred to as knowledge management (KM) [32 and 33]

BI definition The process of accessing and analyzing vast

volumes of business that is created by operational systems and stored in various relational databases, data warehouses or data marts using complex analytical tools or technologies in order to enhance the effectiveness of the business decision making process


The BI Triangle Three key areas (BI triangle) need to

carefully evaluated and managed in order to create an effective and beneficial BI environment

The business value of BI BI technologies Business intelligence issues of concern


The BI Triangle The business value of BI

Business intelligence has the potential to add value to businesses in a number of key business areas some of which include

Competitiveness Responsiveness Customer’s satisfaction & experience Creating business opportunities


The BI Triangle BI technologies Primarily there are two key business

intelligence technologies Data analysis

Data mining OLAP

Data technologies


The BI Triangle Business intelligence issues of concern

A number of issues need to be taken into consideration and appropriately evaluated prior to embarking on a business intelligence project

Direct Vs Indirect Data Feed Silo Vs Centralized BI Approach Context Empowerment

8.3 Business Intelligence Key Data Technologies

BI key data technologies Online Transaction processing (OLTP) Data warehouses Data marts


Online Transaction processing (OLTP) overview Operational data is gathered using various

operational systems FSIS, TPSs, ERP, CRM, SCM, etc..

The process of storing, retrieving and manipulating operational data using various operational information systems is known as online transaction processing (OLTP)

Accuracy and speed are critical factors for OLTP OLTP are typically designed with performance -

transactions speed in mind OLTP associated databases employ a process known

as normalization for structuring transactional data in order to deliver on the speed and accuracy goals


Data Warehouses What is a Data Warehouse?

A data warehouse is basically a centralized repository of a business’s or an enterprise’s various operational data such as finance, HR, inventory and so forth [40 and 44]

Data in a data warehouse is read only and none volatile (historical) where as in operational systems (OLTP systems), it is current and regularly changing [41 and 56]


Data warehouses advantages The ability to facilitate data analysis

and reporting a way from operational systems

Data centralization Unified and a comprehensive view of the

business or the organization The ability to employ data modeling

techniques and servers technologies Optimized for speeding up reporting and

data querying


What is ETL? Short for extraction, loading and

transformation A critical part of a data warehouse

architecture ELT is a process which involves

extracting data from operational systems and loading it into a data warehouse


Data Marts A data mart is typically a very small

type of data warehouse which is used to keep transactional data of a particular business function, operation or a geographic location as opposed to keeping an entire organizational data [36,

37 and 38]

8.4 Business Intelligence Categories

There are three categories of business intelligence [23] Strategic

Used by executive and senior managers Historical data sourced from operational systems Months – decision latency

Tactical Used by middle managers Historical data sourced from operational systems Days, weeks or months – decision latency

Operational Used by front line workers such as call center agents and

sales executive Fresh and real or near real time data Few seconds, minutes or hours– decision latency

8.6 Key Business Intelligence Technologies

What is Data Mining? Generally data mining is defined as

searching and analyzing large volumes of data in order to identify patterns and relationships and to find useful information [48, 49, 50 and 51]


Data Mining Scope Generally, the data mining analysis process falls into

a number of categories [21, 26, 27] Examples

Classification Analyzing the data in order to identify predictive

information Regression

Similar to classification except that it is limited to working with continuous quantitative data [21]

Association Analyze the data in order to discover hidden patterns or

correlation that exists in the data Clustering

Entities that have similar characteristics are grouped together


The Data Mining Process Four steps process

Analysis request Request processing

Data mining application Typically involve some data modeling

based on statistical or machine learning techniques

Data retrieval OLTP, data warehouses, data marts

Analysis presentation


Data Mining Techniques (Algorithms) overview When we talk about data mining algorithms we are

basically referring to the statistical and machine learning techniques that are used to perform the data analysis which discover information in the data or make prediction from the data

There are a number of techniques which data mining employ for its predictive (classification or regression) or descriptive analysis (clustering or association) of the data

Artificial neural networks, decision trees, nearest neighbor method and rule induction [3, 9 and 26]


Data Mining In Practice The data mining vendors provide solutions

(products) that often incorporate a number of different analytical techniques

A single product may have the capability to perform classification, regression, association as well as clustering using various algorithms such as neural networks, CART and nearest neighbors [21, 26]

This feature is essential for building users confidence with using the generated data model


Online Analytical Processing Concept overview What is OLAP?

Generally, OLAP may be simply defined as a category of software applications or technologies which are designed to support the decision making process through providing a visual, speedy, interactive and a multi perspective (dimensions) view of the dataxx


OLAP Process Architecture Multidimensional data modelling and storage is

a key component of the OLAP process architecture

An OLAP server is at the centre of architecture Performs all the data manipulation,

computation and analysis required in order to satisfy all analysis queries received from its clients

OLAP Clients – the third component of the architecture

Typically present the analysis output in a multidimensional dimensional highly visual presentational formats


OLAP Activities There are a number of activities which an OLAP

client could deploy in order to analyze a multidimensional data structure with OLAP [57]

Slice and dice Slicing and dicing is basically about the ability to

break up large data into slices that could then be broken further into smaller chunks (dicing) in order to get a further insight into it

Drill down Analyzing the data from a hierarchal

perspective

8.7 Customer Relationship Management (CRM)

Customer relationship management definition A business philosophy or a strategy

that is focused in understanding and anticipating customer’s needs in order to create a strong and a profitable relationship


The Business Value of CRM CRM places the customer at the centre of its

architecture Having a business strategy which puts the

customer at the center of this is likely to positively affect the profitability and the competitive position of the business

Providing a service that understands, anticipates and satisfy customer’s needs is an enabler to the process of retaining existing customers and potentially attracting new ones


How to realize the CRM benefits A great deal of planning and careful

budgeting Appropriate training Enterprise wide early involvement Choosing the appropriate implementation

process A through understanding of need for

customization and the potential problems that may be associated with it.

A high degree of commitment and support from the top of the business management hierarchy

Chapter 8 Problems Solving Skills Development

Visit the book’s Web site www.halaeducation.com & select module 8

Perform Chapter 8 associated skills development through their respective skills development exercises link

Information systems and management in business Chapter 8 Business Intelligence (BI)

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