Most enterprises already have metric logic. The problem is that it lives in too many places: dashboards, SQL queries, spreadsheets, data pipelines, and the knowledge of individual teams.
The same business term can produce different answers depending on who asks, which system they use, or how a query is written. That inconsistency creates reporting disputes for people and unreliable context for AI systems.
Why a semantic layer is needed
A question such as “What was last quarter’s revenue?” sounds straightforward. Before it can be answered, someone still has to decide whether the quarter is fiscal or calendar, whether revenue is recognized or collected, which entities are included, and which accounting treatment applies.
Those choices are business meaning. When they remain embedded inside individual reports or queries, every new consumer has to recreate them. Analysts spend time reconciling answers, dashboards drift apart, and AI agents inherit the same ambiguity.
A semantic layer makes business meaning an enterprise asset instead of leaving it inside individual queries.
How ReKnew approaches it
ReKnew’s SLayer creates a governed contract between business language and physical data. We work with business owners and data teams to identify the concepts that matter, resolve how they should be defined, and connect those definitions to the systems and fields that support them.
We then encode the definitions as reusable semantic models. Cubes describe logical business domains. Dimensions provide consistent ways to group information. Metrics, filters, joins, and window calculations capture the rules behind approved measures.
The work does not stop at modeling. We establish ownership, testing, certification, documentation, and change controls so the semantic layer remains trustworthy as systems and business requirements evolve.
How SLayer works
SLayer sits between source data and the tools that consume it. It does not replace the data platform. It gives the platform a consistent business vocabulary that can be reused across multiple channels.
We typically begin with a bounded domain rather than trying to model the entire enterprise at once. A focused implementation proves the governance pattern, establishes reusable components, and creates a practical path for expansion.
What clients receive
A SLayer engagement produces working data assets, not just a glossary or strategy document. The exact implementation depends on the current data platform and priority use cases, but the core deliverables usually include:
- Business glossary and metric catalog
- Owned and approved definitions for the concepts that drive decisions and reporting.
- Semantic models
- Reusable cubes, dimensions, metrics, window calculations, filters, and joins implemented above physical data sources.
- Source mappings and lineage
- A clear connection from each business definition to the fields and transformation logic behind it.
- Governance and quality controls
- Certification, testing, documentation, ownership, and change practices that maintain trust over time.
- Consumption patterns
- Approved ways for BI tools, data products, analysts, and AI agents to retrieve the same meaning.
Where to begin
The strongest starting point is a business area where inconsistent definitions already create visible friction. That could be financial reporting, operational performance, customer and product definitions, risk indicators, or the metrics required by a new agentic workflow.
That first domain becomes the reference implementation for expanding SLayer across the organization.
Build a shared language for data