Data Management and Analytics
This domain tests whether you can treat data as an asset with ownership, quality rules, and lifecycle controls. Expect questions on DAMA-DMBOK knowledge areas, data quality dimensions, master data, metadata, analytics maturity, and privacy.
Knowledge areas and governance
DAMA-DMBOK 2 defines 11 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 management, and data quality. Data governance assigns decision rights and accountability: who defines policy, who stewards domains, who resolves conflicts, and who approves access. Stewardship is often federated, with a central council setting standards.
Quality, metadata, and analytics
Common data quality dimensions are accuracy, completeness, consistency, timeliness, uniqueness, and validity. Metadata management covers technical, business, and operational metadata; lineage shows where data came from and how it changed. Master data management creates a trusted golden record through matching, survivorship rules, and stewardship. Analytics maturity typically moves from descriptive (what happened) to diagnostic (why), predictive (what will happen), and prescriptive (what to do).
Worked example
Two systems hold customer records and 12% of addresses disagree. The team profiles the data, defines matching rules, applies survivorship rules such as most recent verified address, and creates a golden record. Stewards measure completeness and accuracy before and after. Lineage documents source systems and transformations. The business case is reduced returned mail and fewer duplicate marketing contacts. Buying a visualization tool first would not fix the disagreement; governance and matching rules would.
Traps that cost marks
- Buying a BI or catalog tool before defining ownership, definitions, and quality rules.
- Treating data quality as an IT defect rather than a business accountability.
- Building a data lake without a catalog, lineage, or access controls.
- Confusing accuracy with validity: an accurate value can still be in the wrong format.
- Ignoring retention, legal hold, and deletion obligations.
- Letting dashboards circulate without metric definitions or owners.