Transcript warehouse_chapter15

Supporting End-User Access
Chapter 15
What is Business Intelligence?
“Business intelligence is the process of
transforming data into information and
through discovery transforming that
information into knowledge.”
Gartner Group
Business Intelligence
The purpose of business intelligence is to
convert the volume of data into value
for the end users.
Decision
Value
Knowledge
Information
Data
Volume
Multidimensional Query
Techniques
Why?
What?
Slicing
Product
Time
Geography
Why?
Dicing
Why?
Drilling
down
Multidimensional Query
Techniques
Categories of Business
Intelligence Tools
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Reporting tools
Query tools (data access)
On-line analytical reporting (OLAP) tools
Analytical suites
Data mining tools
Analytical applications
Evolution of Reporting
Mainframe
•Batch oriented
•IS controlled
•3GL-based
•Not user-specific
•Inflexible
•IS intensive
ClientServer
•End user
empowered
•Reduced IS
manageability
•Expensive
•Localized
Multitier
Enterprise
reporting
•Easy to use
•Manageable
•Scalable
•Accessible
Oracle Discover 3.1
User
Edition
Viewer
Edition
End User Layer
Transaction Database or Data Warehouse
Administration
Edition
Discoverer for the Web
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View workbooks using a Web browser
Business intelligence tool that provides
information anywhere and at any time
Cost-effective
Online Analytical Processing
(OLAP)
Prod
Product mgr.view
Market
Sales
Regional mgr.view
Time
Financial mgr.view
Ad hoc view
Advanced Analytical Tasks
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Comparative and relative analysis
Exception and trend analysis
Time series analysis
Forecasting
What-if analysis
Modeling
Simultaneous equations
Analytical Suites
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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.
Data Mining Tools
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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.
Analytical Applications
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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 enduser 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.
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
Use of Data Mining
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Customer profiling
Market segmentation
Buying pattern affinities
Database marketing
Credit scoring and risk analysis
Functions of Data Mining
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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
Comparing DSS and Data
Mining Queries
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DSS queries:
- Based on prior knowledge and
assumptions
- User-driven
Data mining queries:
- Require domain-specific knowledge
to interpret data
- User-guided
Artificial Neural Networks
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Predictive model that learns
Developed from understand of the
human brain
Multiple regression and other statistical
techniques
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Decision Trees
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Represent decisions
Annual salary
Generate rules
100,000
Classify
Annual
outgoing
<10,000
Good
Annual
credit
>50,000
Bad
Other Techniques
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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
Associates
Which items are purchased in a retail
store at the same time?
Sequential Patterns
What is the likelihood that a customer will
buy a product next month, if he buys a
related item today?
Classifications
Determine customers’ buying patterns
and then find other customers with
similar attributes that may be targeted for
a marketing campaign.
Modeling
Use factors, such as location, number of
bedrooms, and square footage, to
Determine the market value of a property
Oracle Data Mining Partners
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Angoss International, Ltd.
DataMind Corp.
Datasage, Inc.
Information Discovery, Inc.
SPSS Inc.
SRA International, Inc.
Thinking Machines Corp.
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