supporting end-user access chapter 15. what is business intelligence? “business intelligence is...
TRANSCRIPT
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