Until very recently, data management in most IT departments has been largely ignored as a formal discipline. This lack of understanding has brought us to a point today where the biggest problems in most major IT projects revolve around data integration and information management. DAMA has now addressed this problem by creating the DAMA Data Management Guide to the Body of Knowledge (DAMA-DMBOK) – the first “authoritative resource” for data management best practices in 40 years of IT practice. (Available now from Technics Publications or on Amazon.com)
The DAMA-DMBOK was published April 2009. It is a product of DAMA International that represents a new level of maturity in the organization’s product offerings. This guide has been created through a multinational corps of volunteer data management practitioners from a wide variety of industries. Compiled by more than 120 data management practitioners, it is similar in nature to the PMBOK (Project Management) or the BABOK (Business Analysis).
Through the data management functional framework, DAMA-DMBOK provides data management practitioners, organizations, enterprises and academics the best practices and foundational references:
- Data Governance – planning, supervision and control over data management and use.
- Data Architecture Management – as an integral part of the enterprise architecture.
- Data Development – analysis, design, building, testing, deployment and maintenance.
- Database Operations Management – support for structured physical data assets.
- Data Security Management – ensuring privacy, confidentiality and appropriate access.
- Reference and Master Data Management – managing golden versions and replicas.
- Data Warehousing and Business Intelligence Management – enabling access to decision support data for reporting and analysis.
- Document & Content Management – storing, protecting, indexing and enabling access to data found in unstructured sources (electronic files and physical records).
- Meta-Data Management – integrating, controlling and delivering meta-data.
- Data Quality Management – defining, monitoring and improving data quality.
- Professional Development – Data management as a career, certification, ethics for the data professional.
Perhaps John Zachman has summed it up the best (from the Foreword):
