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 IntelligenceThe purpose of business intelligence

is to convert the volume of data into value for the end users.

Decision

Knowledge

Information

Data

Value

Volume

Multidimensional Query Techniques

What?Why?

Why?

Why? Slicing

Dicing

Drillingdown

ProductTime

Geography

Multidimensional Query Techniques

Categories of Business Intelligence Tools Reporting tools Query tools (data access) On-line analytical reporting (OLAP)

tools Analytical suites Data mining tools Analytical applications

Evolution of 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

MainframeClient-Server

MultitierEnterprisereporting

Oracle Discover 3.1User

EditionViewerEdition

End User Layer

Transaction Database or Data Warehouse

AdministrationEdition

Discoverer for the Web View workbooks using a Web

browser Business intelligence tool that

provides information anywhere and at any time

Cost-effective

Online Analytical Processing (OLAP)

Product mgr.view

Financial mgr.view

Time

Prod

Market Sales

Ad hoc view

Regional mgr.view

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

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.

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.

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.

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 Customer profiling Market segmentation Buying pattern affinities Database marketing Credit scoring and risk analysis

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

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

Artificial Neural Networks Predictive model that learns Developed from understand of the

human brain Multiple regression and other

statistical techniques1

4

3

2

7

6 8

5

Decision Trees Represent decisions Generate rules Classify

Annual salary100,000

Annualoutgoing

Annualcredit

>50,000

BadGood

<10,000

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

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 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