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Supporting End-User Access

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Presentation on theme: "Supporting End-User Access"— Presentation transcript:

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

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