DAMA-DMBOK
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.
DAMA-DMBOK provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles:
Data is an asset with unique properties; the value of data can be and should be expressed in economic terms; managing data means managing the quality of data; it takes metadata to manage data; it takes planning to manage data; data management is cross-functional and requires a range of skills and expertise; data management requires an enterprise perspective; data management must account for a range of perspectives; data management is data lifecycle management; different types of data have different lifecycle requirements; managing data includes managing risks associated with data; data management requirements must drive information technology decisions; effective data management requires leadership commitment.
Description
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.
DAMA-DMBOK provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles:
Data is an asset with unique properties; the value of data can be and should be expressed in economic terms; managing data means managing the quality of data; it takes metadata to manage data; it takes planning to manage data; data management is cross-functional and requires a range of skills and expertise; data management requires an enterprise perspective; data management must account for a range of perspectives; data management is data lifecycle management; different types of data have different lifecycle requirements; managing data includes managing risks associated with data; data management requirements must drive information technology decisions; effective data management requires leadership commitment.
