1. DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation of dashboard, dashboards and scorecards measure and display what is important. It provide a real time view of data. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-43

2. Dashboards / Brio performance suite’s © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-44

3. Dashboards © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-45 BI Dashboards have spread to various nonfinancial departments of firms, including sales and customer service. The table below give an example of how dashboard have spread through organizations.

4. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-46

5. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-47

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-48 Business Intelligence and Analytics

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-49 OLAP Activities performed by end users in online systems –Specific, open-ended query generation SQL –Ad hoc reports –Statistical analysis –Building DSS applications Modeling and visualization capabilities Special class of tools // using SQL is helpful but not sufficient for OLAP here a special class of tools is used, known as :- –DSS/BI/BA front ends –Data access front ends –Database front ends –Visual information access systems

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-50 OLAP The rules to evaluate OLAP on are : - 1.Accessibility. 2.Transparency. 3.Multimedia conceptual view. 4.Consistence reporting performance. 5.Client – server architecture. 6.Generic dimensionality. 7.Multi- user support. 8.Flexible reporting. 9.Intuitive data manipulation. 10.Unlimited dimension & aggregation level. 11.Unrestricted cross dimensional operation.

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-51 Data Mining Hollywood data mining case study. P.191

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-52 Data Mining Organizes and employs information and knowledge from databases Statistical, mathematical, artificial intelligence, and machine-learning techniques Automatic and fast Tools look for patterns –Simple models –Intermediate models –Complex Models

11. How data mining works Data mining discovers information within data warehouses that queries and reports can’t effectively reveal. Data mining tools find pattern in data, there are 3 types of methods to identify patterns in data:- Simple models (SQL- based query, OLAP, human judgment) Intermediate models (regression, decision tree, clustering) Complex models (neural network) © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-53

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-54 Data Mining Data mining solving these classes of problems –Classification –Clustering –Association –Sequencing // like association but over a period of time. –Regression // form of estimation. –Forecasting –Others Hypothesis (we assume a situation & start investigation) or discovery driven (it come from the facts).

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-55

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-56

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-57

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-58

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-59 Tools and Techniques Data mining techniques –Statistical methods –Decision trees // by dividing the problem into sub- problems. –Case based reasoning // using historical cases. –Neural computing –Intelligent agents –Genetic algorithms Text Mining –Hidden content // like document properties. –Group by themes // by the common complaints –Determine relationships // look for hidden unnoticed that shows differences.

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-60 Summary : Knowledge Discovery in Databases Data mining used to find patterns in data –Identification of data –Preprocessing –Transformation to common format –Data mining through algorithms –Evaluation

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-61 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

20. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-62 Multidimensionality Data organized according to business standards, not analysts Conceptualization business model Factors –Dimensions: products, salespeople, business unit.. –Measures: money, sales volume, head count.. –Time: daily, weekly,.. Significant overhead and storage Expensive Complex

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-63 Analytic systems Real-time queries and analysis Real-time decision-making Real-time data warehouses updated daily or more frequently –Updates may be made while queries are active –Not all data updated continuously Deployment of business analytic applications

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-64 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

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-65

24. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-66 Web Analytics/Intelligence Web analytics –Application of business analytics to Web sites Web intelligence –Application of business intelligence techniques to Web sites

25. Questions 1.Explain the issue of data quality and some of the measures one can take to improve it. 2.Why OODBMS are the best solution to DSS. 3.What is data warehouse, and what are its benefits? Why is web accessibility important? 4.Describe the major dimension of data quality. 5.Discuss what an organization should consider before making a decision to purchase data- mining software. 6.Explain the process of text mining. 7.State the business intelligence assessment. 8.What is critical challenges for business intelligence success. 9.State the data warehouse risks 10.What is the characteristics of data warehousing? 11.What is the main database structures? © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-67

26. End of chapter 5 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-68