Download 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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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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Difficulties ..2
• Differing legal requirements among
countries
• Selection of data management tool - large
number
• Data security, quality, and integrity
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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
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Data Sources and Collection
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Internal data
Personal data
External data
Internet and commercial database services
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Data Quality (DQ)
Intrinsic
– Accuracy, objectivity, believability, and
reputation
Accessibility
– Accessibility and access security
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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
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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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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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Processing Solutions
• 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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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)
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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
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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 ..2
• 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
• Example: People who buy Barbie dolls also
buy a particular chocolate bar – What can
we do with that information?
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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
Three 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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Components of a 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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Knowledge Management
• Knowledge management or managing
knowledge databases
• A knowledge base is a database that
contains information or organizational
know how.
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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
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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
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Accenture’s Knowledge Base ..3
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Customizable presentations
Process definitions
Directory of internal experts
Best control practice
Tax implementations
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Conclusion
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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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Conclusion ..2
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The legacy data problem
Data delivery
Privacy – especially customer information
What to do?
When to do it?
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