HERM's data model names the data topics and which system is authoritative for each. DAMA-DMBOK adds the full discipline of managing that data well — quality, stewardship, lineage, and master data — the depth beneath the source-of-record.
DAMA-DMBOK (the Data Management Body of Knowledge, from DAMA International) is the reference framework for the practice of data management. It organizes the field into knowledge areas around a central function of data governance, giving institutions a shared definition of what managing data well actually involves.
Its purpose is the question the data model raises but does not answer: not which system owns this data, but is it accurate, well-defined, secure, and trusted. It is how the source-of-record earns its authority.
DAMA arranges its knowledge areas as a wheel with data governance at the hub. Adapted from the DAMA-DMBOK framework.
Every area serves the governance hub; several — master data, metadata, quality — map straight onto attributes you would add per data topic.
HERM's data reference model gives DAMA a place to attach. The data topics and source-of-record designations are the skeleton; DMBOK's knowledge areas add the flesh — quality scores, stewards, lineage, and definitions per topic.
DAMA governance attaches to the HERM data topics the model already names.
Each authoritative system gains a named data steward accountable for quality.
Quality scoring targets the topics that feed the most workflows and decisions.
Data governance isn't a separate layer here. It's built into the record every system already carries. These are the pieces, and where to find each one.
Every system has an accountable owner. Stewards sit beneath owners and keep definitions and quality in order for their data topics.
Roles on Implementation →HERM's data reference model names the data topics. Each topic has one system of record, so there is one answer to "where does this come from?"
HERM data reference model →Data classification (Public, Internal, Sensitive, Restricted) is a local extension on every record. It sets handling rules and security attention.
Catalog by sensitivity →The catalog's data view shows which systems hold each topic and where it moves, so stewards can see copies and handoffs.
Catalog by data →Owners confirm their records on a set cadence, and changes are logged. That review is how the model stays accurate enough to trust.
Cadence on Implementation →For AI systems, the Campus AI Registry's visibility labels govern which documentation is published, alongside this model's classification.
Registry visibility labels →Extending the model toward DMBOK means adding a data-management layer to each data topic. A pragmatic starting set:
The person accountable for the definition and quality of each data topic.
Accuracy, completeness, and timeliness measures per topic.
Where the data originates and how it flows between systems.
An agreed definition for each topic, so the same term means the same thing everywhere.
Which topics are mastered where, and the golden-record rules.
How long data is kept and under what governance policy.
Start with the data topics that feed the most workflows. Name a steward, agree a definition, and score quality — and the source-of-record designations in the model stop being labels and become accountable, trusted data.
DAMA-DMBOK is published by DAMA International. This page describes how it complements HERM; stewards, quality scores, and glossaries are local extensions.