Why are so many vendors building semantic layers now?

AI analytics requires business context to be accurate, and semantic layers provide that context. Every vendor recognizes this need. However, most build semantic layers to serve their own tools rather than to unify definitions across the organization.

What is wrong with having multiple semantic layers?

Multiple semantic layers recreate the problem they are supposed to solve. If your BI tool, your data platform, and your AI analytics each have different metric definitions, you still have inconsistent answers. The semantic layer value proposition is unity.

How do I evaluate whether a semantic layer will actually help?

Ask whether it unifies or fragments. Can it serve all your analytics tools? Does it become the single source of metric definitions? Or does it only serve one tool, adding another silo? Unity is the test.

Should we build our own semantic layer or use a vendor solution?

Building requires significant investment and ongoing maintenance. Vendor solutions offer faster time to value but may create lock-in or fragmentation. The best approach often combines vendor foundations with organization-specific customization.

The Semantic Layer Explosion: Why Everyone Is Building One and Why Most Do Not Solve the Problem

Five years ago, semantic layers were a niche concept familiar mainly to data architects. Today, every data warehouse, BI tool, and analytics platform claims semantic layer capabilities. This proliferation should be good news - finally, the industry recognizes that business context matters for analytics accuracy.

But the reality is more complicated. Most of these semantic layers create new silos rather than solving the consistency problem they claim to address.

The Semantic Layer Moment

Why Now

Several forces converged to make semantic layers urgent:

AI requirements: Generative AI needs business context to produce accurate answers. Without semantic layers, AI hallucinates.

Self-service failures: Years of self-service BI produced inconsistent metrics and eroded trust. Organizations need controlled consistency.

Data mesh influence: Data mesh concepts elevated semantic clarity as essential for decentralized data ownership.

Cloud data growth: As data moved to cloud warehouses, the need for business abstraction became acute.

The market recognized that raw data is not enough - meaning must be captured.

The Vendor Response

Every category of vendor has responded:

Data warehouses: Snowflake, Databricks, BigQuery all added semantic capabilities BI tools: Looker, Tableau, Power BI enhanced their modeling features Metrics platforms: dbt, Metriql, Cube emerged as dedicated solutions AI platforms: Every AI analytics vendor claims semantic foundations

The checkbox is checked everywhere. But checking a box is not the same as solving the problem.

The Fragmentation Problem

The Scenario

Consider a typical enterprise:

Snowflake with its semantic layer capabilities
Looker with LookML
Power BI with its data model
AI analytics platform with its own metric definitions

Each has a "semantic layer." But do they share definitions?

Usually not.

Why Fragmentation Happens

Tool-centric design: Each vendor builds for their tool, not for enterprise consistency

Different philosophies: Vendors disagree on where semantic logic belongs

Lock-in incentives: Portable definitions reduce switching costs for customers

Technical barriers: Making semantic layers interoperate is genuinely hard

The result: multiple semantic layers that do not talk to each other.

The Ironic Outcome

Organizations adopting multiple semantic layers end up with:

Revenue defined one way in the warehouse semantic layer
Revenue defined differently in the BI semantic layer
Revenue defined yet another way in the AI analytics layer

This is exactly the problem semantic layers were supposed to solve - now with more complexity.

Evaluating Semantic Layers

The Unity Test

The fundamental question: Does this semantic layer unify or fragment?

Signs of unity:

Can serve multiple consumption tools
Becomes the single source of metric definitions
Definitions are portable, not locked in
Designed for organization-wide use

Signs of fragmentation:

Only serves one tool
Creates another definition silo
Proprietary format with no portability
Designed for tool adoption, not organizational consistency

Unity is the value proposition. Fragmentation is the failure mode.

Categories of Semantic Layers

Different types serve different purposes:

Universal semantic layers: Designed to serve all downstream tools

Pros: True consistency, vendor independence
Cons: Additional layer to manage, integration complexity
Examples: Cube, AtScale, certain custom implementations

Platform semantic layers: Embedded in data platforms

Pros: Close to data, potentially simpler
Cons: May not reach all tools, platform lock-in
Examples: Snowflake views, Databricks Unity Catalog

BI tool semantic layers: Native to visualization tools

Pros: Tight tool integration, familiar to users
Cons: Limited to that tool, creates silos
Examples: LookML, Power BI Data Model

AI-native semantic layers: Built for AI analytics

Pros: Optimized for AI context needs
Cons: May not serve traditional BI
Examples: Various AI analytics platforms

The Right Choice

The right semantic layer depends on your situation:

If you have one BI tool: Its native semantic layer may suffice If you have multiple tools: A universal layer is likely needed If AI analytics is priority: Ensure the layer serves AI context needs If you have complex governance: Look for strong governance integration

No single answer fits all situations.

The Architecture Question

Where Should Semantic Logic Live?

Vendors disagree fundamentally:

In the warehouse: Close to data, enforced at source

Argument: Single source of truth at the data level
Counter: Warehouses are not designed for rich business context

In a dedicated layer: Separate semantic platform

Argument: Purpose-built for semantic concerns
Counter: Another layer to manage and integrate

In BI tools: Where users work

Argument: Familiar, integrated experience
Counter: Creates tool silos

In AI platforms: Where context is consumed

Argument: Optimized for AI needs
Counter: Does not serve traditional BI

The honest answer: there is no industry consensus. The best architecture depends on your specific tools, teams, and requirements.

Codd AI's Approach

Codd AI takes a pragmatic position:

Context-first design: Built from the ground up for the rich context AI needs

Integration philosophy: Works with your existing semantic investments rather than replacing them

Governance integration: Respects and extends your governance models

Practical focus: Solves the actual problem of accurate AI analytics

Rather than claiming to be the universal semantic layer for all purposes, Codd AI focuses on being excellent for AI analytics while integrating with broader semantic infrastructure.

Making Progress Despite Fragmentation

Strategic Principles

Start with the problem, not the tool: Define what consistency you need before selecting solutions

Accept imperfection: Perfect semantic unity may not be achievable; aim for "good enough"

Prioritize high-value metrics: Ensure critical metrics are consistent everywhere they appear

Plan for evolution: The landscape is changing; design for adaptability

Practical Steps

1. Inventory current state

What semantic capabilities exist?
Where are definitions inconsistent?
Which inconsistencies matter most?

2. Define target architecture

Which tools need consistent definitions?
Where will the authoritative definitions live?
How will definitions flow to consumption points?

3. Implement incrementally

Start with highest-value metrics
Prove value before expanding
Build organizational support through success

4. Establish governance

Who owns metric definitions?
How are changes approved?
How is consistency enforced?

What to Avoid

The checkbox trap: Adopting semantic features because they exist, not because they solve your problem

The perfect architecture pursuit: Waiting for the perfect solution while inconsistency persists

The vendor lock-in slide: Building deeply on proprietary semantic formats without exit strategies

The governance vacuum: Deploying semantic layers without processes to maintain them

The Future of Semantic Layers

Likely Trends

Standardization pressure: Organizations will demand portability, pushing vendors toward interoperability

AI as the forcing function: AI accuracy requirements will drive semantic investment

Governance convergence: Semantic layers will integrate more deeply with data governance

Consolidation: The crowded vendor landscape will consolidate

Implications

For organizations, these trends suggest:

Invest in foundations: Regardless of which tools win, semantic foundations will matter Maintain flexibility: Avoid deep lock-in to any single semantic approach Build skills: Semantic modeling skills will remain valuable Watch the market: The landscape is evolving rapidly

Conclusion

The semantic layer explosion is both opportunity and risk. The opportunity is that the industry finally recognizes the importance of business context for analytics accuracy. The risk is that fragmented implementation recreates the consistency problems semantic layers are meant to solve.

Organizations that approach semantic layers strategically - focusing on unity over checkboxes, solving real problems over adopting features - will capture the benefits while avoiding the pitfalls.

The question is not whether you need semantic capabilities. The question is whether your semantic approach actually delivers consistent, governed, accessible business context - or just adds another layer of complexity.