1. Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS INTELLIGENCE, Eighth Edition 1

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-2 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

3. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-3 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

4. 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. 4

5. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-5 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.

6. 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 6

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-7 Data Mining algorithms  Classification  Clustering  Association  Sequencing  Regression  Forecasting  Others

8. 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. 8

9. 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. 9

10. 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. 10

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-11 Tools and tools and techniques  Data mining  Statistical methods  Decision trees  Case based reasoning  Neural computing  Intelligent agents  Genetic algorithms

12. 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 12

13. 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. : 13

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-14 Knowledge Discovery in Databases  KDD define as process of using data mining method to find useful information and pattern in the data.

15. 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. 15

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-16 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

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-17 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

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-18

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-19 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