This document provides an overview of business intelligence and data mining. It defines related terms like business analytics, data science, and big data. It describes the core disciplines involved like mathematics, statistics, and computer science. It also discusses different types of analytics like descriptive, diagnostic, predictive, and prescriptive analytics. Finally, it covers topics like data mining, concept learning, and operational business intelligence reporting versus analytical techniques.

1. Business Intelligence and
Data Mining
Session 01
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2. Variety of Terms
• Business Intelligence
• Data Mining
• Business Analytics
• Machine Learning
• Big Data
• Data Science
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3. Success Factors
• Processing Capabilities
• Storage Capabilities
• Networking Capabilities
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4. The Core Disciplines
• Mathematics
• Statistics
• Computer Science
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5. Business Analytics is the use of:
data,
information technology,
statistical analysis,
quantitative methods, and
mathematical or computer-based models
to help managers gain improved insight about their
business operations and make better, fact-based
decisions.
What is Business Analytics?
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6. Why there are so many techniques?
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
- Optimizations and predictive analytics
- Complex statistical analysis
- All types of data, and many sources
- Very large datasets
- More of a real-time
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7. Four Types of Analytics
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8. The Progression of Analytics
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9. Example 1.1 Retail Markdown Decisions
 Most department stores clear seasonal inventory by
reducing prices.
 The question is:
When to reduce the price and by how much?
 Descriptive analytics: examine historical data for
similar products (prices, units sold, advertising, …)
 Diagnostic Analytics: Analyze the relationships
between data for association and causation.
 Predictive analytics: predict sales based on price
 Prescriptive analytics: find the best sets of pricing
and advertising to maximize sales revenue
Scope of Business Analytics
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10. Scope of Business Analytics
Analytics in Practice:
Harrah’s Entertainment
•Harrah’s owns numerous hotels and casinos
•Uses analytics to:
- forecast demand for rooms
- segment customers by gaming activities
•Uses prescriptive models to:
- set room rates
- allocate rooms
- offer perks and rewards to customers
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11. What Can Computers Learn?
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12. Four Levels of Learning
 Facts
 Concepts
 Procedures
 Principles
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13. Facts
A fact is a simple statement of truth.
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14. Concepts
A concept is a set of objects, symbols, or events grouped
together because they share certain characteristics.
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15. Procedures
A procedure is a step-by-step course of action to
achieve a goal.
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16. Principles
Principles are general truths or laws that are basic
to other truths.
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17. Computers & Learning
Computers are good at retaining facts and learning
concepts by following specific principles and
procedures.
Concepts are the output of a data mining session.
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18. Three Concept Views
 Classical View
 Probabilistic View
 Exemplar View
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19. Classical View
All concepts have definite defining
properties.
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20. Probabilistic View
People store and recall concepts as
generalizations created by observations.
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21. Exemplar View
People store and recall likely concept
exemplars that are used to classify unknown
instances.
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22. Example
The figure below illustrates five initial cases (for n=1, 2, 3, 4,
and 5), where n points are placed on a circle and every point is
connected by a line to every other point so as to divide the
circle into many pieces…
No pieces = 1 2 4 8 16
n = 1 2 3 4 5
General Model is: (n4 - 6n3 + 23n2 - 18n + 24) / 24
How many pieces if there are 6 points?
What’s the General model for n points?
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23. Data Mining: Definition
The process of employing one or more computer
learning techniques to automatically analyze and
extract knowledge from data.
All Data Mining methods use induction based learning.
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24. Induction-based Learning
The process of forming general concept
definitions by observing specific examples of
concepts to be learned.
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25. BI Reporting Vs. Analytics
“Business Intelligence is needed to run the business while Business
Analytics is needed to change the business.”
“The difference between Business Intelligence is looking in the
rearview mirror and using historical data from one minute ago to many
years ago. Business Analytics is looking in front of you to see what is
going to happen. This will help you anticipate in what’s coming, while
BI will tell you what happened. ”
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26. 26

27. 27

28. BI Reporting Solution
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29. 29

30.  Metrics are used to quantify performance.
 Measures are numerical values of metrics.
 Discrete metrics involve counting
- on time or not on time
- number or proportion of on time deliveries
 Continuous metrics are measured on a continuum
- delivery time
- package weight
- purchase price
Data for Business Analytics
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31. Measurement Levels
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ORDER DISTANCE ORIGIN
QUALITATIVE
NOMINAL X X X
ORDINAL ✔ X X
QUANTITATIVE
INTERVAL ✔ ✔ X
RATIO ✔ ✔ ✔

32. OLAP
• Refers to the process of quickly conducting complex,
multidimensional analyses of data stored in a
database that is optimized for retrieval, typically using
graphical software tools.
• Measures and Dimensions
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33. OLAP Operations
• SLICING
• DICING
• ROLL UP
• DRILL DOWN
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