1. 1 Data and Knowledge Management

2. 2 Data Management: A Critical Success Factor The difficulties and the process Data sources and collection Data quality Multimedia and object-oriented databases Document management

3. 3 Difficulties Data amount increases exponentially Data: multiple sources Small portion of data useful for specific decisions Increased need for external data

4. 4 Difficulties..2 Differing legal requirements among countries Selection of data management tool - large number Data security, quality, and integrity

5. 5 Data Life Cycle Process and Knowledge Discovery Data collected and stored in databases Processed and stored in data warehouses Transformation - ready for analysis Data mining tools - knowledge Presentation

6. 6 Data Sources and Collection Internal data Personal data External data Internet and commercial database services

7. 7 Data Quality (DQ) Intrinsic – Accuracy, objectivity, believability, and reputation Accessibility – Accessibility and access security

8. 8 Data Quality..2 Contextual DQ – Relevancy, value added, timeliness, completeness Representation DQ – Interpretability, ease of understanding, concise representation, and consistent representation

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10. 10 Complex Databases Object-Oriented database Multimedia database Document management

11. 11 Data Warehousing, Mining, and Analysis Transaction versus analytical processing Data warehouse and data marts Knowledge discovery, analysis, and mining

12. 12 Good Data Delivery System Easy data access by end users Quicker decision making Accurate and effective decision making Flexible decision making

13. 13 Processing Solutions Business representation of data for end users Client-server environment - end users query and reporting capability Server-based repository (data warehouse)

14. 14 Data Warehouse and Marts The purpose of a data warehouse is to establish a data repository that makes data accessible in a form readily acceptable for analytical processing activities. A data mart is dedicated to a functional or regional area. (subset of a warehouse)

15. 15 Data Warehouse A data warehouse contains historical data, not operational It contains data from a number of databases so the data must be ‘cleaned’ to ensure that the data definitions are consistent

16. 16 Characteristics of Data Warehousing Organization Consistency Time variant Nonvolatile Relational

17. 17 The Data Warehouse and Marts Benefits Cost Architecture Putting the data warehouse on the internet Suitability

18. 18 Knowledge Discovery, Analysis, and Mining Foundations of knowledge discovery in databases (KDD) Tools and techniques of KDD Online analytical processing (OLAP) Data mining

19. 19 The Foundations of Knowledge Discovery in Databases (KDD) Massive data collection Powerful multiprocessor computers Data mining algorithms

20. 20

21. 21 OLAP Queries Access very large amounts of data Analyze the relationships between many types of business elements Involve aggregated data Compare aggregated data over hierarchical time periods

22. 22 OLAP Queries..2 Present data in different perspectives Involve complex calculations between data elements Able to respond quickly to user requests

23. 23 Data Mining Automated prediction of trends Automated discovery of previously unknown patterns Example: People who buy Barbie dolls also buy a particular chocolate bar – What can we do with that information?

24. 24 Data Mining Characteristics and Objectives Data often buried deep within large databases Data may be consolidated in data warehouse or kept in internet and intranet servers Usually client-server architecture

25. 25 Data Mining Characteristics and Objectives Data mining tools extract information buried in corporate files or archived public records The “miner” is often an end user “Striking it rich” usually involves finding unexpected, valuable results Parallel processing

26. 26 Data Mining Characteristics and Objectives Data mining yields five types of information Data miners can use one or several tools

27. 27 Data Mining Yields Five Types of Information Association Sequences Classifications Clusters Forecasting

28. 28 Data Mining Techniques Case-based reasoning Neural computing Intelligent agents Others: decision trees, genetic algorithms, nearest neighbor method, and rule reduction

29. 29 Data Visualization Technologies Data visualization Multidimensionality Geographical information systems (GIS)

30. 30 Data Visualization Data visualization refers to presentation of data by technologies digital images, geographical information systems, graphical user interfaces, multidimensional tables and graphs, virtual reality, three-dimensional presentations and animation.

31. 31 Multidimensionality Major advantage – data can be organized the way managers prefer to see the data Three factors – dimensions, measures, and time

32. 32 Examples Dimensions –Products, salespeople, market segments, business units, geographical locations Measures –Money, sales volume, head count, inventory, profit, actual versus forecasted Time –Daily, weekly, monthly, quarterly, yearly

33. 33 Geographical Information Systems (GIS) A GIS is a computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps.

34. 34 Components of a GIS Software Data Emerging GIS applications

35. 35 Emerging GIS Applications Integration of GIS and GPS –Reengineer aviation and shipping industries Intelligent GIS (integration of GIS and ES) User interface –Multimedia, 3D graphics, animated and interactive maps Web applications

36. 36 Knowledge Management Knowledge management or managing knowledge databases A knowledge base is a database that contains information or organizational know how.

37. 37 Accenture’s Learning Organization Knowledge Base Global best practices These data combined with ongoing research identify areas to be developed Research analysis team with content experts to develop best practices Qualitative and quantitative information and tools in Intranet for corporate wide access

38. 38 Accenture’s Knowledge Base..2 Best company profiles Relevant Accenture engagement experience Top 10 case studies and articles World-class performance measures Diagnostic tools

39. 39 Accenture’s Knowledge Base..3 Customizable presentations Process definitions Directory of internal experts Best control practice Tax implementations

40. 40 Conclusion Cost-benefit analysis Where to store data physically Disaster recovery Internal or external Data security and ethics Data purging

41. 41 Conclusion..2 The legacy data problem Data delivery Privacy – especially customer information What to do? When to do it?