Data Management Fundamentals
The map of the discipline
DAMA-DMBOK organizes data management into knowledge areas: Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata, and Data Quality. In the DMBOK "wheel," Data Governance sits at the center and the other areas surround it.
The data lifecycle
Plan → Design → Enable (specify/build) → Create/Obtain → Store → Share → Use → Maintain → Enhance → Dispose. Exact wording varies by source, but understand the flow: planning and design precede build; disposal is planned up front, not bolted on at the end.
Principles
Data is an asset with measurable value; data is shared and reusable; data quality must be managed deliberately; data management is a business function, not merely an IT function; it requires strategy, leadership, and sustained funding.
Traps
- Treating data management as IT-only. Questions describing business accountability for definitions point to business ownership.
- Confusing data (raw facts/representations) with information (data placed in context with meaning).
- Lifecycle ordering - retention and disposal requirements belong in design/plan, not only at end of life.
- Assuming every knowledge area is equally mature; maturity is assessed per area.
- Confusing Data Storage and Operations (databases, backups, capacity) with Data Architecture (design and blueprints).
Rule of thumb
Map a question by its deliverable, not by which department would do it: business glossary → Metadata; golden customer record → Reference and Master Data; retention schedule → Document and Content Management (with Governance); query performance and backups → Data Storage and Operations.