It provides concept of Data warehouses, Data Marts, OLAP, Data Mining, Text Mining and Web Mining as tools for business intelligence.

1. Foundations of Business
Intelligence:
Data warehouses and Data Mining
Mr. Roshan Bhattarai
Kathmandu, Nepal

2. Data warehouse
• a separate database than operational database
• stores current and historical data of potential
interest to decision makers throughout the company
• information can be used across the enterprise for
management analysis and decision making, supports
reporting and query tools
• data may originate from sales, customer accounts,
website transactions, manufacturing, competitors,
regulatory body, market etc

3. Components of a Data Warehouse

4. Data Mart
• A data mart is a subset of data warehouse in which
summarized and highly focused portion of
organization’s data is placed in a separate database
• Smaller and decentralized warehouses
• Focuses on single subject area, so can be constructed
more rapidly and at lower cost than enterprise-wide
data warehouse
• Eg: Marketing and Sales data mart, Manufacturing
data mart etc

5. Tools for Business Intelligence
• Business Intelligence tools enable users to analyze data
to see new patterns, relationships and insights that are
useful for guiding decision making
• Principal tools include:
– Online Analytical Processing (OLAP)
– Data Mining
– Text Mining and Web Mining

6. 1. Online Analytical Processing (OLAP)
• Tool for multi-dimensional data analysis
• Enables user to view the same data in different ways
using multiple dimensions
• Supports manipulation and analysis of large volumes of
data from multiple perspectives
• Eg: Product vs Actual and Projected sales, Region vs
Actual and Projected sales etc

7. 2. Data Mining
• Provides insights into corporate data that cannot be
obtained with OLAP by finding hidden patterns and
relationships in larger databases
• Infer rules to predict future behavior of data
• Patterns and rules are used to guide decision making
and forecast the effect of those decisions
• The types of information obtainable from data mining
include:
a) Associations
– Occurrences linked to a single event
– Eg: Promotion vs Sales
After promotion, Purchase of coca-cola is increased to
80% (from 60%) of the time when pop corn is purchased

8. b) Sequences
– events linked over time
– Eg: if a house is purchased, an oven will be bought
within one month
c) Classification
– recognizes patterns that describes the group by
examining existing items that have been classified
d) Clustering
– no groups have been defined, data mining tool can
discover different grouping of data
e) Forecasts
– estimate future value of continuous variables by using
series of existing values

9. 3. Text mining and Web mining
• Unstructured data, most in the form of text files
• Believed to account for 80% of organization’s useful
information
• Email, memo, survey responses, service reports etc
• Text mining tools are used to analyze these data
• Discover hidden patterns and relationships from large
unstructured data sets

10. • Discovery and analysis of useful patterns and information
from www is Web mining
• Google trends and Google Insights for services services
• Track the popularity of various words and phrases used in
google search queries
• Web mining looks for patterns in data through content
mining (text, audio, video), structure mining (links in web
documents) and usage mining (user interaction data
recorded by web server).