1. Business Intelligence
M. M. JUNAID
AIMS

2. Introduction
Business intelligence (BI) is defined as the ability for an
organization to take all its capabilities and convert them
into knowledge, ultimately, getting the right information to
the right people, at the right time, via the right channel.
This produces large amounts of information which can lead
to the development of new opportunities for the
organization. When these opportunities have been
identified and a strategy has been effectively
implemented, they can provide an organization with a
competitive advantage in the market, and stability in the
long run (within its industry).

3. What is Business Intelligence
 Collecting and refining information from
many sources
 Analyzing and presenting the information in
useful ways
 So people can make better business decisions

4. Business Intelligence
BI technologies provide historical, current and
predictive views of business operations.
Common functions of business intelligence
technologies are reporting, online analytical
processing, analytics, data mining, process mining,
complex event processing, business performance
management, benchmarking, text mining, predictive
analytics and prescriptive analytics.

5. Business Intelligence
 BI technologies provide historical, current and
predictive views of business operations.
 Common functions of business intelligence
technologies are reporting, online analytical
processing, analytics, data mining, process mining,
complex event processing, business performance
management, benchmarking, text mining, predictive
analytics and prescriptive analytics.
 BI is a umbrella term that include architectures,
tools, databases, analytical tools, applications and
methodologies.

6. Components of BI
 Data Repository (i.e. Data Warehouse)
 Business Analytics (Querying, Reporting, Analyis &
Visualization Tools etc)
 Data Minning
 Business Performance Measur

9. Data Mining

Data Mining is a process that uses statistical,
mathematical, artificial intelligence, Machine learning
techniques to extract and identify useful information
and subsequent knowledge from large databases.
Data mining is the process of finding mathematical
patterns from usually large set of data.
These patterns can be rules, affinity, correlations,
trends and prediction models.

10. Statistics Machine Learning
Data Mining
Database
systems

11. Steps in data Mining

12. Steps in Data Mining

13. Data Mining Tasks

•Association
•Sequence
•Classification
•Clustering
•Forecasting
•Regression

14. • Types of information obtainable from data mining
• Associations: Occurrences linked to single event
• Sequences: Events linked over time
• Classifications: Patterns describing a group an item belongs to
• Clusters: Discovering as yet unclassified groupings
• Forecasting: Uses series of values to forecast future values
• Regression : Predict a value of a given continuous valued variable
based on the values of other variables

15.  Direct Marketing
identify which prospects should be included in a mailing list
 Market segmentation
identify common characteristics of customers who buy same products
 Customer churn
Predict which customers are likely to leave your company for
competitor
 Market Basket Analysis
Identify what products are likely to be bought together
 Insurance Claims Analysis
discover patterns of fraudulent transactions
compare current transactions against those patterns

16. Association
 Given a set of records each of which contain some
number of items from a given collection
 Produce dependency rules which will predict occurrence of an
item based on occurrences of other items
TID ITems
1 Pencil, Eraser, Sharpener
2 Scale, Pencil,
Rules discover
3 Scale, Eraser, pouch, Sharpener {sharpener} --> {eraser}
{pouch ,sharpener} --> {scale)
4 Scale, Pencil, pouch, sharpener
5 Eraser, scale , sharpner

17. Classification
 Given a collection of records (training set )
 Each record contains a set of attributes, one of the attributes is
the class.
 Find a model for class attribute as a function of the
values of other attributes.
 Goal: previously unseen records should be assigned a
class as accurately as possible.

18. Classification Example
 Direct Marketing
 Goal: Reduce cost of mailing by targeting a set of consumers
likely to buy a new cell-phone product.
 Approach:
 Use the data for a similar product introduced before.
 We know which customers decided to buy and which decided
otherwise. This {buy, don’t buy} decision forms the class attribute.
 Collect various demographic, lifestyle, and company-interaction
related information about all such customers.
 Type of business, where they stay, how much they earn, etc.
 Use this information as input attributes to learn a classifier model.

19. Clustering
 Given a set of data points, each having a set of
attributes, and a similarity measure among them,
find clusters such that
 Data points in one cluster are more similar to one another.
 Data points in separate clusters are less similar to one another.
 Applications:
 Marketing: finding groups of customers with similar buying
pattern

20. Regression
 Predict a value of a given continuous valued variable
based on the values of other variables, assuming a
linear or nonlinear model of dependency.
 Greatly studied in statistics
 Examples:
 Predicting sales amounts of new product based on advertising
expenditure.
 Predicting wind velocities as a function of temperature,
humidity, air pressure, etc.

21. Regression Example
Weight(K
Height(CM) G) Weight=1.41 * Height - 175.3
160 55
162 57
165 60
168 64
170 72
171 75
175 78
178 80
182 82