Data Management Fundamentals (DMF)
Data Management Fundamentals
This domain tests the vocabulary and framework that the rest of CDMP assumes: what data management is, which functions make it up, and how they interlock. Expect definitional questions, questions that map a business scenario to a DAMA knowledge area, and questions about roles, lifecycle, and maturity.
Core rules and concepts
- Data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. It is a business capability, not an IT sub-function.
- The DAMA-DMBOK wheel has eleven 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 BI, Metadata, and Data Quality. Data Governance sits at the centre because it provides decision rights and oversight.
- Environmental elements often called the DAMA framework: goals and principles, data management functions, specialized disciplines, organization and culture, roles, processes, technologies, and metrics.
- The data lifecycle: Plan, Design, Enable, Maintain, Use, Enhance, Dispose. It is iterative, not a waterfall; governance, quality, and metadata work cut across every phase.
- DIKW: data (raw facts), information (data in context), knowledge (integrated understanding), wisdom (applied judgement). Data management primarily produces reliable information.
- Roles: data owner (accountable), data steward (custodian of quality and definitions), data custodian (technical safekeeping), CDO, data governance council.
- Maturity models assess capability across levels such as initial, repeatable, defined, managed, and optimized; strategy must link data goals to business goals and be measured.
Worked example
A retailer has three systems recording customer addresses. Finance reports by region disagree with Marketing campaign counts. Which knowledge area is primary? Reasoning: the disagreement is about a shared business entity, not about a single report. Reference and Master Data Management is primary because it defines the trusted customer record and cross-system identifiers. Data Quality is involved in measuring accuracy and completeness, Data Integration moves and reconciles records, Metadata documents definitions and lineage, and Data Governance decides who owns the definition. Naming governance as primary is the trap; governance enables and arbitrates, but the work of creating one trusted customer view is MDM.
Traps that cost marks
- Equating data management with database administration.
- Treating the eleven knowledge areas as sequential phases; they are concurrent and interdependent.
- Saying governance does data management; it sets decision rights and oversight while functions execute.
- Forgetting that Dispose is part of the lifecycle and that retention and archiving need policy.
- Choosing a technology answer for a problem framed as accountability or definition.