- Introduction
- Basic Security Principles
- Data Management: Determining and Maintaining Ownership
- Data Governance Policies
- Roles and Responsibilities
- Data Ownership
- Data Custodians
- Data Documentation and Organization
- Data Warehousing
- Data Mining
- Knowledge Management
- Data Standards
- Data Lifecycle Control
- Data Audits
- Data Storage and Archiving
- Data Security, Protection, Sharing, and Dissemination
- Privacy Impact Assessment
- Information Handling Requirements
- Record Retention and Destruction
- Data Remanence and Decommissioning
- Classifying Information and Supporting Asset Classification
This chapter is from the book
This chapter is from the book
This chapter is from the book
Knowledge Management
Knowledge management seeks to make intelligent use of the data in an organization by applying wisdom to it. This involves turning data into intelligence through analytics by tying together databases, document management, business processes, and information systems. The result is a huge store of data that can be mined to extract knowledge using artificial intelligence techniques.
There are three main approaches to knowledge extraction:
Classification: This approach is used to discover patterns and can be used to reduce large databases to only a few individual records or data marts. (Think of data marts as small slices of data from a data warehouse.)- Probabilistic: This approach is used to permit statistical analysis, often in planning and control systems or in applications that involve uncertainty.
- Statistical: This is a number-crunching approach in which rules are constructed to identify generalized patterns in the data.