1. Supporting End-User Access
Chapter 15

2. What is Business Intelligence?
“Business intelligence is the process of transforming data into information and through discovery transforming that information into knowledge.”
Gartner Group

3. Business Intelligence
The purpose of business intelligence is to convert the volume of data into value for the end users.
Decision
Value
Knowledge
Information
Volume
Data

4. Multidimensional Query Techniques
Product
Time
Why?
Slicing
Geography
What?
Why?
Dicing
Why?
Drilling
down

5. Multidimensional Query Techniques

6. Categories of Business Intelligence Tools
Reporting tools
Query tools (data access)
On-line analytical reporting (OLAP) tools
Analytical suites
Data mining tools
Analytical applications

7. Evolution of Reporting
Mainframe
Client-
Server
Multitier
Enterprise
reporting
Batch oriented
IS controlled
3GL-based
Not user-specific
Inflexible
IS intensive
End user
empowered
Reduced IS
manageability
Expensive
Localized
Easy to use
Manageable
Scalable
Accessible

8. Oracle Discover 3.1 User Viewer Edition Edition End User Layer
Transaction Database or Data Warehouse
Administration
Edition

9. Discoverer for the Web View workbooks using a Web browser
Business intelligence tool that provides information anywhere and at any time
Cost-effective

10. Online Analytical Processing (OLAP)
Prod
Product mgr.view
Regional mgr.view
Sales
Market
Time
Ad hoc view
Financial mgr.view

11. Advanced Analytical Tasks
Comparative and relative analysis
Exception and trend analysis
Time series analysis
Forecasting
What-if analysis
Modeling
Simultaneous equations

12. Analytical Suites Enterprise business intelligence (EBI) toolsets:
- Web-enabled query, reporting, and
analysis tool that runs on a robust
application server
- EBI toolset tightly integrates query,
reporting, and analysis capabilities within a
single tool
- Shares a common look and feel
Business portals:
- EBI toolset with a Yahoo-like user interface
- Flexible repository handles structured and
unstructured data objects.

13. Data Mining Tools
Identify patterns and relationships in data that are often useful for building models that aid decision making or predict behavior
Data mining uses technologies such as neural networks, rule induction, and clustering to discover relationships in data and make predictions that are hidden, not apparent, or too complex to be extracted using statistical techniques.

14. Analytical Applications
Packaged analytical application has a predefined:
- Extraction feeds and transformation
routines for a specific data source
- Data model, application-specific
report templates, and a custom end-
user interface.
Custom analytic applications are workbenches that enable developers to quickly create analytic applications from coarse-grained components, including user interface widgets, data access and analysis components, and report layouts.

15. Definition of Data Mining
“ Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns, trends, relationships, and rules. ”
Data mining is also known as:
Knowledge discovery
Data surfing
Data harvesting

16. Use of Data Mining Customer profiling Market segmentation
Buying pattern affinities
Database marketing
Credit scoring and risk analysis

17. Functions of Data Mining
Discovers facts and data relationships
Finds patterns
Determines rules
Retains and reuse rules
Presents information to users
May take many hours
Requires knowledgeable people to analyze the results

18. Comparing DSS and Data Mining Queries
DSS queries:
- Based on prior knowledge and
assumptions
- User-driven
Data mining queries:
- Require domain-specific knowledge
to interpret data
- User-guided

19. Artificial Neural Networks
Predictive model that learns
Developed from understand of the human brain
Multiple regression and other statistical techniques 1 5 2 6 8 3 7 4

20. Decision Trees Represent decisions Generate rules Classify
Annual salary
100,000
Annual
outgoing
Annual
credit
<10,000
>50,000
Good
Bad

21. Other Techniques Genetic algorithms based on evolution theory
Statistics such as averages and totals
Nearest neighbor to find associations
Rules induction applying IF-THEN logic
Experiment with different techniques

22. Associates
Which items are purchased in a retail store at the same time?

23. Sequential Patterns What is the likelihood that a customer will
buy a product next month, if he buys a related item today?

24. Classifications Determine customers’ buying patterns
and then find other customers with
similar attributes that may be targeted for
a marketing campaign.

25. Modeling Use factors, such as location, number of
bedrooms, and square footage, to
Determine the market value of a property

26. Oracle Data Mining Partners
Angoss International, Ltd.
DataMind Corp.
Datasage, Inc.
Information Discovery, Inc.
SPSS Inc.
SRA International, Inc.
Thinking Machines Corp.

27. Summary This lesson covered the following topics:
Describing the importance of business intelligence
Identifying where data mining might be employed in a warehouse environment
Identifying data mining tools