DAMA-DMBOK Basic Definitions Every CDMP Candidate Should Know

These are working definitions written for CDMP candidates getting oriented — not official DAMA International definitions. For the authoritative wording, refer to the DAMA-DMBOK2 guide itself.

DAMA International

The professional association behind DMBOK — a vendor-neutral, non-profit body that promotes data management as a discipline with clear business value, rather than representing any particular vendor or technology.

DMBOK (DAMA-DMBOK2)

The DAMA Data Management Body of Knowledge, 2nd edition — the reference guide that organizes data management into the 14 knowledge areas the CDMP exam is based on.

CDMP

Certified Data Management Professional — the certification DAMA International offers based on DMBOK2, assessed at Associate, Practitioner, and Master levels depending on score.

Data Governance

The policies, decision rights, and accountability structures that determine who can do what with data and how. Usually described as sitting at the center of the DMBOK wheel, since most other knowledge areas carry out governance decisions rather than make them independently.

Data Owner

The person (or role) accountable for a specific data domain's quality, definition, and appropriate use — typically a business role, not a technical one, since ownership is about accountability rather than day-to-day upkeep.

Data Steward

The person responsible for the day-to-day care of a data domain on the owner's behalf — maintaining definitions, monitoring quality, and acting as the practical point of contact for questions about that data.

Metadata

Data about data: definitions, formats, lineage, and business context that let people understand what a piece of data actually means and where it came from, rather than just seeing a raw value with no context.

Data Lineage

The traceable history of where a piece of data originated and every transformation it went through to reach its current form — important for trust, auditability, and diagnosing where a data quality issue was actually introduced.

Business Glossary

A shared, agreed-upon set of definitions for business terms (e.g. what "active customer" actually means) so different teams aren't unknowingly working from different definitions of the same concept.

Data Catalog

A searchable inventory of an organization's data assets — what exists, where it lives, who owns it — that helps people actually find and evaluate data before using it, rather than relying on institutional knowledge of "who to ask."

Master Data

The core, shared entities a business depends on — customers, products, suppliers, locations — that multiple systems all need to reference consistently, as opposed to transactional data that records individual events.

Reference Data

Standardized, usually stable sets of values used to categorize other data — think country codes, currency codes, or status lists — that need to stay consistent across every system that uses them.

Golden Record

The single, trusted, authoritative version of a master data entity, reconciled from potentially multiple conflicting sources — the practical goal of most master data management efforts.

Data Quality Dimensions

The set of measurable characteristics — commonly accuracy, completeness, consistency, timeliness, validity, and uniqueness — used to describe and assess whether data is actually fit for its intended purpose.

Data Architecture

The blueprint describing how an organization's data assets, structures, and integration points fit together — the structural counterpart to data modeling, operating at the enterprise level rather than a single system.

Conceptual, Logical & Physical Data Models

Three levels of increasing detail used to design data structures: conceptual captures business concepts and relationships in plain terms, logical adds detailed attributes and rules independent of any specific database, and physical defines exactly how it's implemented in a particular system.

Data Warehouse vs. Data Lake

A data warehouse stores structured, modeled data optimized for reporting and business intelligence. A data lake stores raw data, structured or not, in its original form — typically used for broader exploratory analytics and data science work.

ETL / ELT

Extract, Transform, Load (or Extract, Load, Transform) — the process patterns used to move data between systems while reshaping it into the format the destination needs.