DAMA-DMBOK: Data Management Body of Knowledge: 2nd Edition

The Data Management Body of Knowledge (DAMA-DMBOK2) presents a comprehensive view of the challenges complexities and value of effective data management. Today's organizations recognize that managing data is central to their success. They recognize data has value and they want to leverage that value. As our ability and desire to create and exploit data has increased so too has the need for reliable data management practices. The second edition of DAMA International's Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible authoritative reference book written by leading thinkers in the field and extensively reviewed by DAMA members DMBOK2 brings together materials that comprehensively describe the challenges of data management and how to meet them by: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-DMBOK2 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 propertiesThe value of data can be and should be expressed in economic termsManaging data means managing the quality of dataIt takes metadata to manage dataIt takes planning to manage dataData management is cross-functional and requires a range of skills and expertiseData management requires an enterprise perspectiveData management must account for a range of perspectivesData management is data lifecycle managementDifferent types of data have different lifecycle requirementsManaging data includes managing risks associated with dataData management requirements must drive information technology decisionsEffective data management requires leadership commitmentChapters include:Data ManagementData Handling EthicsData GovernanceData ArchitectureData Modeling and DesignData Storage and OperationsData SecurityData Integration and InteroperabilityDocument and Content ManagementReference and Master DataData Warehousing and Business IntelligenceMetadata ManagementData Quality ManagementBig Data and Data ScienceData Management Maturity AssessmentData Management Organization and Role ExpectationsData Management and Organizational Change ManagementStandardization of data management disciplines will help data management professionals perform more effectively and consistently. It will also enable organizational leaders to recognize the value and contributions of data management activities.

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