Semantic Layer: What it is and when to adopt it

As data sources and use cases continue to explode within an organization, it’s critical to ensure data quality and consistency. With more stakeholders relying on accurate metrics to power their strategic decisions, companies need to ensure these metrics are actually accurate and consistent across teams, and that requires centralizing business logic in a single source of truth.

A semantic layer is one such tool companies can use to ensure metric consistency and quality across the organization. In this article, we’ll look into what a semantic layer is, how it works, some common use cases, and how to get started adopting one.

What is a semantic layer?

A semantic layer is a framework that allows organizations to create a unified, business-friendly representation of their data. In other words, a semantic layer translates data into common language. It serves as a unified interface and repository that enables data teams to build and store metric logic centrally so data consumers can access consistent, high-quality, and governed data across a variety of endpoints.

The semantic layer solves a key problem created by the explosion of data. Thanks to technologies such as cloud data warehouses and processes such as ELT, organizations can access, create, and manage more data than ever before.

However, this explosion of data and the increase in data stakeholders introduces new challenges. A Forrester survey in 2021 found that over 61% of organizations use four or more BI tools, and a staggering 25% use 10 or more. Defining metrics and business logic within each of these disparate interfaces leads to workflow inefficiencies and introduces data quality risk.

For example, you may start out with one approach to calculating and representing revenue. However, as time evolves and your approach to calculating this changes, older definitions become outdated. Updating this across multiple endpoints and reports is often unrealistic. The result is that different teams end up with different definitions and understandings of this key concept.

This is where the semantic layer comes in. It acts as an API for data, supplying data stakeholders with a single source of truth from which to pull key data sets and calculations.

A semantic model provides a “hub-and-spoke” architecture to your metric definitions. Metrics are written and defined centrally and can be queried from a number of “spokes”—analytics tools, APIs, LLMs, and more. Those endpoints are always accessing the same centralized data, so organizations can be sure that they’re working off the same metric everywhere, every time.

Benefits of a semantic layer

Implementing a semantic layer provides multiple benefits across your data stack, including:

Eliminates inconsistencies. Differing data definitions can lead different teams to make divergent assessments of the same question. And when teams spot the discrepancy, it can lead to long debates about whose view of reality is right—this derails data-driven initiatives and sabotages trust in data teams. A semantic layer eliminates these debates and trust issues, driving consistency of terminology and calculation for key data structures across different projects.

Improves data democratization. Stakeholders want to be empowered to get their own answers, not to mention, it isn’t scalable for data teams to be hands-on with every possible data request. However, data consumers may not know where to find the data they need or have the skills required to analyze it. Worse yet, they may end up using source data that’s out-of-date or incorrect.

A semantic layer enables teams to pull insights from data even if they aren’t comfortable with writing complex SQL data transformations. Additionally, it makes this data available in a self-service format, which reduces support requests placed on data engineers.

Promotes data reusability. Even when teams do have data engineers and analytics engineers on staff, that doesn’t mean they should spend half their time reinventing the wheel. A semantic model enables a single team to maintain one gold standard data set that other teams can leverage and build upon. This not only improves data consistency, it also streamlines data operations and optimizes costs.

Improves compliance. A semantic model also helps with increasing compliance. By acting as a central point of access, the model can enforce role-based access controls to data, ensuring that sensitive data is protected and made available only to authorized stakeholders.

Use cases for a semantic layer

The notion of a semantic layer has been around for a while now. How are organizations taking advantage of consistent metrics across the business? We’ve seen five key patterns emerge.

Reporting and BI

Populating BI tools with relevant and accurate data is an obvious and urgent use case for the semantic layer. The use of multiple BI tools and interfaces across an org leads to a host of problems:

• Data maintenance: Metrics must be manually built and updated across each BI tool as definitions evolve. It’s cumbersome to test and validate changes to logic or diagnose issues in a scalable, proactive manner.
• Data bottlenecks: Adding or updating business logic across various tools results in slow development cycles. Troubleshooting discrepancies is time-consuming and impairs trust.
• Data trust: Metrics drive business-critical decisions, and there is no room for error. Mistakes are noticed by C-level stakeholders and impair trust in data and data teams.

By utilizing the “hub-and-spoke” architecture, a semantic layer provides, data teams can store semantic models and definitions centrally. They can then deliver those metrics on demand into any downstream tool that queries it—whether from a first-class integrated tool or via a custom integration powered by an export to your data platform.

Embedded analytics

Data is powerful. It shouldn’t be relegated to an internal KPI scorecard to align internal employees. It can - and should - be embedded into customer- and partner-facing applications to create delightful, personalized user experiences that build brand equity.

With a semantic layer as your foundation, you can calculate complex metrics centrally right alongside the rest of your data. You can version control them and deliver them as up-to-date embedded visualizations in your app, website, or wherever your stakeholders consume data. Data teams can build custom web apps using developer-friendly APIs or SDKs and serve up relevant, personalized data to end-users - without driving up costs with legacy BI tools.

AI and LLMs

Everyone wants to embrace AI (or at least have a plan for adopting it). And no one wants bad data undermining those plans.

In our most recent State of Analytics Engineering Report, the majority of respondents noted that they manage data for AI model training (either currently or within the next 12 months). Unsurprisingly, data quality is an undeniable prerequisite for the successful adoption of AI. A recent Tableau / Salesforce study found that 86% of analytics and IT leaders agree that AI's outputs are only as good as its data inputs.

Using a semantic layer, you can ensure high-quality outputs across your AI stack that reduce hallucinations, eliminate redundant data transformation work, and lower the barrier to analytics. Using this data, your AI apps can empower less technical users to self-service answers to their questions.

Self-serve analytics

To scalably adopt data-driven practices across an organization, data teams can’t be involved in every data request. Manual human intervention is neither scalable nor efficient.

Self-serve analytics allows anyone—even those who aren’t technical—to get the data they need to make strategic decisions. By centralizing metrics with a semantic layer, data teams can minimize ad hoc requests while ensuring high-quality and governed data becomes easily accessible across the organization, whether through a spreadsheet, an AI chatbot, or any other accessible interface. As a result, data velocity, collaboration, and trust improve. That, in turn, further improves your data ROI.

Exploratory analytics

Exploratory analytics is a critical step in the data science workflow. It involves using Python libraries to inspect data, discover patterns, and verify hypotheses that ultimately inform winning business strategies and deliver data ROI.

While highly strategic, exploratory analytics can be challenging:

• Data sources are constantly expanding
• You need the ability to combine data across various sources to get a complete picture of reality
• You need the flexibility to continuously iterate your questions and quickly slice metrics across various dimensions to get to the bottom of something

…all while ensuring data integrity and quality along the way.

Using a semantic layer, data science teams can take advantage of centralized and governed metrics that live alongside other data models, querying and joining them to support iterative exploratory analytics workflows.

Getting started with the semantic layer

A semantic layer consists of four elements:

• Varied data sources that flow into a central data repository
• Data models
• Metrics definitions
• Endpoints (a BI tool, LMM, embedded site widget, etc.)

dbt has long supported defining the data model layer, bringing a new level of velocity, data quality, and democratization to data. Using the dbt Semantic Layer, you can define a common set of metrics that live alongside your data models, creating a unified data interface for all of your data stakeholders.

What’s more, with the dbt Semantic Layer, you can codify aggregation types and their underlying calculations, capturing business logic in a central location that can be consumed by downstream tools. This ensures that critical business concepts - like revenue - are defined and documented in one location, where everyone can both consume and maintain them.

The dbt Semantic Layer also reduces redundancy by following the DRY (Don’t Repeat Yourself) principle. Instead of calculating metrics in multiple places, you can define them in one location, alongside your dbt models and tap into them from any endpoint. This reduces duplicative work and code. It also prevents other teams from having to redo this work when they onboard a new downstream tool.

Early adopters of the dbt Semantic Layer are taking advantage of consistent metrics across their businesses—and five key use cases are emerging. Check out this blog to learn more about the five use cases for the dbt Semantic Layer.