1. Rajesh Math
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 111
BI I Lecture 1

2. Syllabus/Course
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 122
Same group for communication
Syllabus
Books
Tools/Software
Title of Course is BI

3. Data Mining
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 133
Data Mining is process of exploration and analysis by
automatic or semiautomatic means of large quantities of data
in order to discover meaningful patterns and rules.
Data Miners are the people who apply this potent mixture of
massive computing power, clever algorithms, business
knowledge and human intuition.

4. What Data Mining Can Do ?
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 144
 Activities that are to extract meaningful new
information from the data.
The activities are :
1. Classification
2. Estimation
3. Predication
4. Affinity grouping or association rules.
5. Clustering
6. Description and visualization

5. Classification
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 155
e.g.
i. Classifying credit applicants as low, medium or high risk.
ii. Assigning customers to predefined customer segments.
iii. Assigning key-words to articles as they come off the
news-wire.

6. Estimation/Prediction
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 166
Use modeling to estimate
the probability
e.g. estimating the value of a
real estate.(MLS in US)
Prediction: It can be
thought as combination of
classification and
estimation but the
difference is in emphasis
i.e. we wait to see if our
prediction is correct.
E.g. Predicting if customer
will move away

7. Affinity Grouping or Association Rules
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 177
To determine what things go together.
e.g. Retail Chains can arrange items together that are purchased
together.
Used to cross sell different opportunities.

8. Clustering
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 188
Task of segmenting a diverse group into a number of more
similar subgroups or clusters
Different from Classification is absence of predefined classes
rather records are grouped together on the basis of self-
similarity
Normally a first step before doing something else e.g. Before
‘What type of promotion works best for customer ?’ divide
people into clusters of similar buying habits.

9. Data Mining
Discovering Knowledge based on Data
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 199
 Descriptive Models
 Discovery of patterns & relationships in the underlying data
 e.g. A customer who purchases diapers is 3 times more likely to buy beer.
 e.g. There is a cluster of households w $60-80K incomes and 2 cars (more than
($60-$80k and 1 or 3 cars, or 2 cars w $40-60K or $80-100k) who have recently
bought life insurance.
 Predictive Models & Anomaly Detection
 Predictions of trends & behaviors;
Noticing deviations from those predictions
 e.g. How much profit will this customer generate? Is this credit card stolen?
 Uses
 Sales & Marketing, Diagnosis, Fraud Detection, …

10. Data mining
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11010
Simply describe what is going on complicated database to
better understanding of people products or process that
produced the data.
Data visualization : one powerful form of descriptive data
mining. i.e. one picture is worth many thousand association
In business context knowledge out of data-mining can be
used to lower costs or raise revenue.

11. Data mining as Research Tool
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11111
Pharmaceutical Industry to develop leads on binding
molecules.
Bioinformatics is based on data mining which interprets data
being generated by high throughput screening and mapping
of the entire human genetic sequence.

12. Data Mining for Marketing
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11212
Used to do target marketing for right customers.
Prospective targets of a marketing campaign.
Identify trends/events that correlate

13. Data Mining For CRM
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11313
To make the customer data into meaningful information
Good CRM means providing a good image of company
across multiple channels.

14. 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11414
BI Definition
Business intelligence (BI) is a broad category of applications and
technologies for gathering, storing, analyzing, and providing
access to data to help enterprise users make better business
decisions. BI applications include the activities of decision
support systems, query and reporting, online analytical
processing (OLAP), statistical analysis, forecasting, and data
mining.

15. BI Applications
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11515
Business intelligence applications can be:
Mission-critical and integral to an enterprise's operations or
occasional to meet a special requirement
Enterprise-wide or local to one division, department, or
project
Centrally initiated or driven by user demand

16. BI Goal
09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11616
Main business intelligence goal is to provide sufficient
information for making business decisions. Depending on the
aim of the business decision, business intelligence methods can
provide information about company customers, market trends,
effectiveness of marketing campaigns, companies competitors,
or even predict future activities.