Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

DECISION SUPPORT SYSTEMS AND BUSINESS INTELLIGENCE , Eighth Edition

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Data, Information, Knowledge Data

Items that are the most elementary descriptions of things, events, activities, and transactions

May be internal or external

Information Organized data that has meaning and value

Knowledge Processed data or information that conveys

understanding or learning applicable to a problem or activity

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Business Intelligence and Analytics Business intelligence

Acquisition of data and information for use in decision-making activities

Business analytics It adds an additional dimension to BI : Models and

solution methods

Data mining Applying models and methods to data to identify

patterns and trends

DashBoard It provides managers with exactly the information

they need in the correct format at the correct time. Dashboard and scorecards measure display what is

important.

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Data Mining Organizes and employs information and

knowledge from databases It uses statistical, mathematical, artificial

intelligence, and machine-learning techniques Automatic and fast by focusing attention on the

most important variables.. Data mining includes tasks known as knowledge

extraction, data exploration, data pattern processing.

How data mining works? It discovery information within data warehouses

that queries and reports cannot effectively reveal. Data mining tools find patterns in data and infer

rule from them. These pattern and rule can be used to guide

decision-making.

It is supported by set of algorithms approach to extract the relevant relationship in the data

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Data Mining algorithms Classification Clustering Association Sequencing Regression Forecasting Others

Data Mining algorithms (cont.) Classification : defining the characteristic of

certain group. Decision tree and neural network are useful

techniques.

Clustering : identifies group of items that share a certain characteristics. Clustering approach can be used to identify class of

customers.

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Data Mining algorithms (cont.) Association : identifies relationship between event that

occur at one time. Statistical method are typically used.

Sequencing: similar to association except that the relationship occurs over a period time .

Regression :used to map data to a prediction value. Linear and nonlinear technique are used

Forecasting: estimate future value based on pattern within large set of data.

Other: it is based on advanced artificial intelligence method.

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Data Mining (cont.) Data mining can be either Hypothesis or discovery

driven. Hypothesis driven data mining begins with

proposition by the user. Discovery driven data mining finds pattern,

associations and relationship among the data.

Data mining is iterative.

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Tools and tools and techniques Data mining

Statistical methods Decision trees Case based reasoning Neural computing Intelligent agents Genetic algorithms

Text Mining It is the application of data mining to nonstructred

or less structured text files.

Text mining help organization to Find Hidden content Group by themes Determine relationships

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Sampler of data mining application Marketing : prediction which customer will

respond to buy a particular product. Banking: which kind of customers will best

respond to new loan offers. Manufacturing: predicting when to expect

machinery failures.:

:

:

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Knowledge Discovery in Databases

KDD define as process of using data mining method to find useful information and pattern in the data.

KDD process Selection : Identification of data Preprocessing: missing data must be dealt with,

involves correction and/or utilizing predicted values.

Transformation to common format Data mining : applying through algorithms Interpretation /Evaluation: data must be presented

in manner that is meaningful to the users.

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Data Visualization Technologies supporting visualization and

interpretation Digital imaging, GIS, GUI, tables,

multidimensions, graphs, VR, 3D, animation Identify relationships and trends

Data manipulation allows real time look at performance data

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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GIS Computerized system for managing and

manipulating data with digitized maps Geographically oriented Geographic spreadsheet for models Software allows web access to maps Used for modeling and simulations

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Web Analytics/Intelligence Web analytics

Term used to describes the application of business analytics to Web sites

Web intelligence Term used to describes the application of business

intelligence techniques to Web sites