1. Data management is a critical success factor for organizations but presents several difficulties including an exponentially increasing amount of data from multiple sources, with only a small portion being useful for specific decisions. Effective data management requires collecting, storing, processing, analyzing, and presenting data to facilitate decision making. 2. Knowledge management systems aim to organize relevant information and best practices into accessible databases in order to support organizational learning and improve performance. For example, Accenture developed a global knowledge base containing case studies, tools, and expertise from various engagements. 3. Effective data and knowledge management requires addressing issues such as data quality, storage, security, privacy, and legacy systems while utilizing techniques including data warehousing, mining, visualization

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

9. 9

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?