1 / 41

Data and Knowledge Management

Data and Knowledge Management. Data Management: A Critical Success Factor. The difficulties and the process Data sources and collection Data quality Multimedia and object-oriented databases Document management. Difficulties. Data amount increases exponentially Data: multiple sources

mstouffer
Download Presentation

Data and Knowledge Management

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data and Knowledge Management

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

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

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

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

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

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

  9. Complex Databases • Object-Oriented database • Multimedia database • Document management

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

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

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

  13. 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)

  14. 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

  15. Characteristics of Data Warehousing • Organization • Consistency • Time variant • Nonvolatile • Relational

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

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

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

  19. 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

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

  21. 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?

  22. Data MiningCharacteristics 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

  23. Data MiningCharacteristics 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

  24. Data MiningCharacteristics and Objectives • Data mining yields five types of information • Data miners can use one or several tools

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

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

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

  28. 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.

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

  30. 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

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

  32. Components of a GIS • Software • Data • Emerging GIS applications

  33. 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

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

  35. Accenture’sLearning 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

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

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

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

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

More Related