Semantic Layers

What it is

Without one, each report or tool carries its own definition of a measure such as revenue or active customer, and the answers differ. A semantic layer moves the definition out of the reports and into one place that they all read.

Tool documentation states the aim plainly: centralising metric definitions gives consistent self-service access to metrics in downstream tools.

Why it matters

Inconsistent metric definitions undermine trust in reporting and in AI answers over data. A first version of a vendor-neutral interchange specification for semantic models was published in January 2026.

A participant describes the Open Semantic Interchange specification as defining a vendor-neutral, extensible model for representing semantic layer constructs. An independent consultancy places the semantic layer in the Trial ring of its technology radar, notes that the main challenge is the upfront data modelling investment, and advises beginning with a single domain. DII has not read the specification itself.

The problem it addresses

DII editorial view

Revenue, active customer and margin are defined slightly differently in each report and tool, so the same question gets different answers and trust falls. A semantic layer holds each definition once. It records agreement; it cannot create it.

Business and organisational relevance

DII editorial view

The value depends on agreement before technology. A semantic layer records definitions; it cannot create agreement about them. Where the business has not settled what a metric means, the layer will expose that.

Practical use cases

• One definition of each core metric used by every dashboard
• A governed model for natural-language and AI assistants to query
• Moving metric logic out of individual reports

Benefits and opportunities

• The same question gets the same answer in every tool
• Definitions can be changed in one place

Risks, limitations and dependencies

• The upfront data modelling investment
• Definitions locked into one vendor's layer
• Building the layer before the business has agreed its metrics

Prerequisites for adoption

DII editorial view

• Business agreement on what the core metrics mean
• A named owner for each definition
• Capacity for the data modelling the layer requires up front

Implementation considerations

• Agree definitions and owners first. Settle the meaning and the owner of each core metric before modelling it.
• Start with one domain. Begin with a single domain and extend from there.
• Prefer exportable definitions. Favour tools that can export the definitions they hold.

The DII Radar assessment

Approved DII Radar assessment

DII assesses Semantic Layers as High relevance, with direction Rising. Its strategic impact is assessed as High and its enterprise adoption as Emerging. It is one of the ten entries on the current Radar.

DII relevance is our assessment of current significance and momentum, based on evidence, market development and adoption. Direction is the observed trajectory of relevance and adoption. Strategic impact is the potential or current significance to enterprise data capability, and enterprise adoption is the extent of current real-world enterprise adoption; together they set the position on the Radar. The Radar is not a prediction, vendor comparison or performance ranking.

Evidence basis: Thoughtworks Technology Radar Vol. 34 (April 2026): Semantic layer, Trial; dbt Labs on the Open Semantic Interchange specification (29 January 2026; a participant); vendor documentation.

See Semantic Layers on the Radar →

What to consider next

DII editorial view

• Agree the definitions and owners of core metrics first
• Start with one domain
• Prefer tools that can export the definitions they hold