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Data and Knowledge
Management
1
Data Management:
A Critical Success Factor
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The difficulties and the process
Data sources and collection
Data quality
Multimedia and object-oriented databases
Document management
2
Difficulties
• Data amount increases exponentially
• Data: multiple sources
• Small portion of data useful for specific
decisions
• Increased need for external data
3
Difficulties ..2
• Differing legal requirements among
countries
• Selection of data management tool - large
number
• Data security, quality, and integrity
4
Data Life Cycle Process and
Knowledge Discovery
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Data collected and stored in databases
Processed and stored in data warehouses
Transformation - ready for analysis
Data mining tools - knowledge
Presentation
5
Data Sources and Collection
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Internal data
Personal data
External data
Internet and commercial database services
6
Data Quality (DQ)
Intrinsic
– Accuracy, objectivity, believability, and
reputation
Accessibility
– Accessibility and access security
7
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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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
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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
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Organization
Consistency
Time variant
Nonvolatile
Relational
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The Data Warehouse and Marts
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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
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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
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OLAP Queries ..2
• Present data in different perspectives
• Involve complex calculations between data
elements
• Able to respond quickly to user requests
22
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?
23
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
24
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
25
Data Mining
Characteristics and Objectives
• Data mining yields five types of
information
• Data miners can use one or several tools
26
Data Mining Yields Five Types of
Information
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Association
Sequences
Classifications
Clusters
Forecasting
27
Data Mining Techniques
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Case-based reasoning
Neural computing
Intelligent agents
Others: decision trees, genetic algorithms,
nearest neighbor method, and rule reduction
28
Data Visualization Technologies
• Data visualization
• Multidimensionality
• Geographical information systems (GIS)
29
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.
30
Multidimensionality
Major advantage
– data can be organized the way
managers prefer to see the data
Three factors
– dimensions, measures, and time
31
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
32
Geographical Information
Systems (GIS)
A GIS is a computer-based system for
capturing, storing, checking,
integrating, manipulating, and
displaying data using digitized maps.
33
Components of a GIS
• Software
• Data
• Emerging GIS applications
34
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
35
Knowledge Management
• Knowledge management or managing
knowledge databases
• A knowledge base is a database that
contains information or organizational
know how.
36
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
37
Accenture’s Knowledge Base ..2
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Best company profiles
Relevant Accenture engagement experience
Top 10 case studies and articles
World-class performance measures
Diagnostic tools
38
Accenture’s Knowledge Base ..3
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Customizable presentations
Process definitions
Directory of internal experts
Best control practice
Tax implementations
39
Conclusion
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Cost-benefit analysis
Where to store data physically
Disaster recovery
Internal or external
Data security and ethics
Data purging
40
Conclusion ..2
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The legacy data problem
Data delivery
Privacy – especially customer information
What to do?
When to do it?
41