This document provides an overview of business intelligence. It defines business intelligence as decision support tools that enable interactive access, analysis, and manipulation of corporate information in real-time. The goals of BI are to support internal users and deliver critical business information to customers and suppliers. BI tools allow users to drill down and slice and dice transactional and operational data from sources like a data warehouse. The document then describes common BI components like ETL, data marts, star schemas, OLAP, and pivot tables.

1. Business Intelligence
Overview

2. What is Business Intelligence?
 “Business Intelligence (BI) applications are decision support tools that
enable real-time, interactive access, analysis and manipulation of
mission-critical corporate information.”
 The goals for Business intelligence projects are to provide information
used to:
 Support internal enterprise users in the assessment, enhancement and
optimization of organizational performance & operation.
 Deliver critical business information to end-users about value chain
constituencies such as customers and supply-chain partners.
 End-users can utilize the BI tools to "drill-down" and "slice and dice" to
gain a better understanding of transactional and operational information;
for example, Star Schema and OLAP are usually used for provide this
functionality.

3. Business Intelligence
Customer
Inventory Marketing
Data Mart OLAP
Credit
ETL Data Finance BI
tools Warehouse Data Mart
Sales
Reports
Distribution
Operation
Data Mart
External
Pivot Table

4. Business Intelligence Tools
Introduction
 Operational Data Source: Business Intelligence system collects
data from various sources including operation database, OLTP,
ERP, legacy apps, external database and etc.
 ETL tools (Extract, Transform, Load) are used to pull data from
source database, transform the data so that it is compatible with
the data warehouse and then load it into data warehouse.
 A Data Warehouse is a "Subject-Oriented, Integrated, Time-
Variant, Nonvolatile collection of data in support of decision
making". Data Warehouses tend to have these distinguishing
features: (1) Use a subject oriented dimensional data model; (2)
Contain publishable data from potentially multiple sources and; (3)
Contain integrated reporting tools.
 Data Warehouse usually adopts “Star Schema” data structure for
optimizing the performance of data analysis and reporting.

5. Star Schema
 A Star schema is optimized for data analysis as is typically required
in a Decision Support System.
 A star schema has a fact table surrounded by dimension tables

6. Data Mart
 A data mart is a repository of data gathered from operational
data and other sources that is designed to serve a particular
community of knowledge workers.
 The data may derive from an enterprise-wide database or data
warehouse or be more specialized.
 The emphasis of a data mart is on meeting the specific demands
of a particular group of knowledge users in terms of analysis,
content, presentation, and ease-of-use. Users of a data mart can
expect to have data presented in terms that are familiar.

7. On-Line Analytical Processing
(OLAP)
 Literally, On-Line Analytical Processing. Designates a category of applications
and technologies that allow the collection, storage, manipulation and
reproduction of multidimensional data, with the goal of analysis.
 Example: http://perso.wanadoo.fr/bernard.lupin/english

8. Pivot Table
 A pivot table is a great
reporting tool that
allows for “slicing and
dicing” data.

9. Analytical Software Market
Analytical Software Revenue
$6.00
$5.20
$5.00
$4.10
$4.00
(In Billions)
$3.20
$3.00 $2.50
$1.90
$2.00 $1.50
$1.00
$0.00
1998 1999 2000 2001 2002 2003
Year Revenue

10. OLAP Market

11. Strategic, Tactical & Functional
Benefits of Business Intelligence