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 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 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





Chapters include:


Data Management

Data Handling Ethics

Data Governance

Data Architecture

Data Modeling and Design

Data Storage and Operations

Data Security

Data Integration & Interoperability

Document and Content Management

Reference and Master Data

Data Warehousing and Business Intelligence

Metadata Management

Data Quality Management

Big Data and Data Science

Data Management Maturity Assessment

Data Management Organization and Role Expectations

Data Management and Organizational Change Management





Standardization 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.