Transcript Data and Knowledge Management

Data and Knowledge
Management
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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
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The Difficulties and the Process:
The Difficulties
• Data amount increases exponentially
• Data: multiple sources
• Small portion of data useful for specific
decisions
• Increased need for external data
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The Difficulties and the Process:
The Difficulties
• Differing legal requirements among
countries
• Selection of data management tool - large
number
• Data security, quality, and integrity
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The Difficulties and the Process:
Data Life Cycle Process and
Knowledge Discovery
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Data Collection
Stored in databases
Processed
Stored in data warehouse
Transformation - ready for analysis
Data mining tools - knowledge
Presentation
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Data Sources and Collection
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Internal data
Personal data
External data
Internet and commercial database services
Methods for collecting raw data
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Data Quality (DQ)
• Intrinsic DQ:
– Accuracy, objectivity, believability, and
reputation
• Accessibility DQ:
– Accessibility and access security
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Data Quality (DQ)
• Contextual DQ:
– Relevancy, value added, timeliness,
completeness
• Representation DQ:
– Interpretability, ease of understanding, concise
representation, and consistent representation
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Multimedia and Object-Oriented
Databases
• Object-Oriented database (multimedia
database)
• Document management
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Data Warehousing,
Mining, and Analysis
• Transaction versus analytical processing
• Data warehouse and data marts
• Knowledge discovery, analysis, and mining
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Transaction Versus Analytical
Processing
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
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Transaction Versus Analytical
Processing
Solution
• Business representation of data for end
users
• Client-server environment - end users query
and reporting capability
• Server-based repository (data warehouse)
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The Data Warehouse and Marts
The purpose of a data warehouse is to
establish a data repository that makes
operational data accessible in a form readily
acceptable for analytical processing
activities . . .
A data mart is … dedicated to a functional
or regional area.
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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
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Knowledge Discovery, Analysis,
and Mining
• Foundations of knowledge discovery in
databases (KDD)
• Tools and techniques of KDD
• Online analytical processing (OLAP)
• Data mining
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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
• Present data in different perspectives
• Involve complex calculations between data
elements
• Able to respond quickly to user requests
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Data Mining
• Automated prediction of trends
• Automated discovery of previously
unknown patterns
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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
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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
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Data Mining
Characteristics and Objectives
• Data mining yields five types of
information
• Data miners can use one or several tools
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Data Mining Yields Five Types of
Information
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Association
Sequences
Classifications
Clusters
Forecasting
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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
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Data Visualization Technologies
• Data visualization
• Multidimensionality
• Geographical information systems (GIS)
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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.
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Multidimensionality
• Major advantage - data can be organized the
way managers prefer to see the data
• There factors: dimensions, measures, and
time
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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
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Geographical Information
Systems (GIS)
A GIS is a computer-based system for
capturing, storing, checking, integrating,
manipulating, and displaying data using
digitized maps.
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Geographical Information
Systems (GIS)
• Software
• Data
• Emerging GIS applications
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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
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Marketing Databases in Action
• The Marketing Transaction Database
(MTD)
• Implementation Examples
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The Marketing Transaction
Database (MTD)
… a new kind of database, oriented toward
targeting and personalizing marketing
messages in real time.
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Knowledge Management
• Knowledge management or managing
knowledge databases
• A knowledge base is a database that
contains infromation or organizational know
how.
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Knowledge Management
• Knowledge bases and organizational
learning
• Implementing knowledge management
systems
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Arthur Andersen’s
Learning Organization Knowledge Base
• Global best practices hotline
• 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 are released on CD-ROM for
corporate wide access
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Arthur Andersen’s
Knowledge Base
• Best company profiles
• Relevant Arthur Andersen engagement
experience
• Top 10 case studies and articles
• World-class performance measures
• Diagnostic tools
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Arthur Andersen’s
Knowledge Base
• Customizable presentations
• Process definitions and directory of internal
experts
• Best control practice
• Tax implementations
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Managerial Issues
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Cost-benefit analysis
Where to store data physically
Disaster recovery
Internal or external
Data security and ethics
Data purging
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Managerial Issues
• The legacy data problem
• Data delivery
• Privacy
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herein.
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