What it is
A Common Data Model (CDM) is the governed, integrated representation of the organisation’s shared data meaning.
It brings together coherent Subject Areas into a common enterprise structure so that people, systems, reports, analytics, governance processes and AI can work from consistent definitions and relationships.
A Common Data Model is not simply a database design or technical integration artefact.
It is part of the organisation’s Knowledge Base.
It helps establish:
- common organisational language,
- agreed definitions,
- semantic relationships,
- ownership and stewardship,
- and a stable reference point for organisational meaning.
Why shared meaning matters
Every organisation already has a language.
The problem is that the language is often fragmented.
Different parts of the organisation may use the same term differently:
- Customer
- Product
- Asset
- Agreement
- Service
- Risk
At the same time, several different terms may describe the same underlying thing.
When meaning fragments:
- communication becomes harder,
- siloed thinking strengthens,
- interpretations diverge,
- reports conflict,
- systems represent concepts differently,
- trust declines,
- and coordinated action becomes increasingly difficult.
Fragmented meaning creates fragmented organisations.
The problem is therefore much larger than data integration or data quality.
It is a problem of organisational coherence.
The relationship between BCM, Conceptual Data Model and Common Data Model
The Business Capability Model (BCM) describes what the organisation must be capable of doing.
The Conceptual Data Model provides the enterprise semantic map: the major things the organisation deals with and the important relationships between them.
The Common Data Model develops from that foundation as a governed mosaic of detailed, validated Subject Areas.
Put simply:
- BCM — what the organisation must be capable of doing.
- Conceptual Data Model — the enterprise highway map.
- Subject Areas — mind-sized detailed portions of the model.
- Common Data Model — the governed mosaic formed as those Subject Areas are developed and validated.
Together, the BCM and CDM provide relatively stable reference structures even while systems, processes and organisational structures change.
Shared meaning before technology
Enterprise software platforms contain sophisticated data structures, but vendor data models are not automatically the organisation’s shared meaning.
They reflect the requirements, assumptions and structures of the software.
The organisation still needs to establish:
- what its own concepts mean,
- how they relate,
- who owns their meaning,
- and how those meanings are validated in operational reality.
Without this, the organisation can gradually adopt the language and assumptions of its systems rather than ensuring that systems reflect organisational meaning.
Applications support operations. The Knowledge Base preserves organisational meaning.
Governance and Data Stewardship
A Common Data Model cannot be sustained by modelling alone.
It requires governance.
Governance establishes:
- ownership,
- decision rights,
- Data Stewardship,
- controlled change,
- escalation where meaning is contested,
- and education for the people expected to govern and use the model.
Data Stewards need enough modelling knowledge to challenge definitions, relationships and assumptions and to recognise when deeper validation is required.
Governance therefore has an important educational role.
Governance without education produces titles without capability.
Shared meaning is learned together
Strong data models are not produced simply by drawing diagrams.
They emerge through:
- dialogue,
- challenge,
- negotiation,
- storytelling,
- reflection,
- and Gemba validation.
The process exposes:
- competing definitions,
- hidden assumptions,
- local interpretations,
- unclear ownership,
- and tacit operational knowledge.
The purpose is not merely to produce a technically correct model.
The outcome is shared meaning across the people who define, govern, use and depend on the data, reducing conflicting interpretations and enabling coherent action.
AI changes the scale, not the need for governance
AI can rapidly generate candidate:
- entities,
- relationships,
- definitions,
- patterns,
- and Subject Areas.
This creates enormous opportunity.
It also creates risk.
AI can generate far more structure than humans can reasonably validate.
For this reason:
AI may generate at machine scale. Humans can only validate at human scale.
AI can propose meaning.
Data Stewards govern semantic integrity.
Gemba validates operational reality.
Governance establishes accountability.
The detailed Human–AI approach is developed further in:
SA — Data Modelling: Negotiating and Governing Shared Meaning
and:
SA — Human–AI Kanban Architecture
Closing thought
A Common Data Model provides more than consistent data structures.
It helps an organisation maintain a coherent understanding of itself across people, systems, decisions and change.
Shared meaning is organisational infrastructure.