What is a semantic layer? How to fix messy metrics

Different teams, same campaign, conflicting reports — this type of data discrepancy happens all the time.

The performance marketer thinks they’re smashing targets with 120 conversions a month. But the analyst only sees 90. The problem isn’t usually the data itself. It’s how key business concepts like “lead” or “conversion” are defined across tools and teams.

Enter the semantic layer. 

The semantic layer helps teams speak the same language, even when they use different tools. It defines what “a lead” or “customer acquisition cost” actually means, so everyone sees the same version of the truth across all analytics tools.

Let’s take a closer look at what a semantic layer is, how it works within the enterprise data architecture and why having a semantic foundation for marketing data is essential for navigating today’s world of messy, multi-platform data management.

What is a semantic layer?

A semantic layer acts as a translation layer for your raw data assets. Its job is to turn your data structure into shared terms that everyone understands. This way, every team interprets your data in the same way, even if they use different analytics tools.

Making sure data is structured properly so your teams are speaking the same language is extremely important. When teams use different tools and platforms, inconsistent terminology leads to varying (and sometimes conflicting) conclusions, even though all business users seem to be looking at the same data.

However, messy data is a common and growing problem. Companies are finding it harder to centralize relevant data than in previous years. Only 31% of marketers feel satisfied with how they unify data. Meanwhile, 70% of business leaders say their customer and prospect data comes from too many data sources to easily make sense of it.

So, how can a semantic layer help?

Imagine a messy spreadsheet that’s filled with system-generated field names. These entries might look something like:

1. AdClick_XYZ
2. Campaign_Spring25
3. User_JaneDoe
4. ProductPage_Premium
5. Purchase_Order_987
6. Deluxe Coffee Maker

It’s not easy to understand what these values tell you on their own. 

The semantic layer provides a lens that rewrites the same data in business terms you can easily understand, improving data access for all stakeholders.

This same information above would look more like…

Spring 25 Google Ad click resulted in Jane Doe visiting the Deluxe Coffee Maker product page and making the 987th purchase for the Deluxe Coffee Maker.

Using a semantic layer is not just about simplifying data analytics. It’s about translating raw data into something more actionable by applying business context to technical fields. 

This levels the playing field. 

Over half of marketers complain that they generally need technical help to understand performance analytics. But with a tool that automatically harmonizes marketing data (which can be a true semantic layer or a no-code marketing data integration tool like Funnel), you don’t have to be a data scientist ‌to get trustworthy business intelligence. While Funnel isn’t a semantic layer in the strict architectural sense, it gives marketers many of the same benefits: reusable logic, clean metrics and consistent language across platforms — all without code.

In practice, this means teams don’t have to wrestle with SQL, BI tools or engineering bottlenecks to get answers.

The semantic layer — or a tool like Funnel that supports some of its core principles — helps take away technical complexity, giving business users direct access to meaningful data. This leads to faster insights and quicker decision-making.

But it’s not just speed that matters. Semantic layers are especially powerful in situations where data definitions vary from tool to tool. One platform might define a “lead” as someone who fills in a form. Another might only count ‌someone as a lead if they’ve booked a meeting.

A semantic layer platform applies a logical layer so definitions are uniform. No matter the underlying data sources, the conclusion will be the same, as all data label variations are defined in a consistent way. 

In short, if you’re juggling multiple data sources, your semantic layer offers a single version of truth and one that everyone can understand.

How does a semantic layer work?

A semantic layer bridges the gap between data structures and the way people actually understand them. It translates cryptic field names and turns them into familiar business terms to help you focus on insights, rather than interpretation.

Here’s how it works.

Abstraction and simplification

Raw data isn’t user-friendly. When you see data in its original state, it’s often in technical shorthand. This is hard for business users outside the data team to understand.

What does “campaign_cost_usd” and “utm_source” mean?

A semantic layer acts as the translator. It takes terms like the ones above and changes them into readable labels like “Ad Spend” or “Traffic Source.”

This layer of abstraction enables non-tech data users to access data without needing to know how SQL or the underlying schema works. In other words, marketing is less dependent on the data team. 

Data governance and consistency

One of the biggest frustrations with reporting is when everyone’s using the same data but seeing different results.

This is rarely a data issue. It’s usually a logic issue. 

While the data might clearly say one thing, the confusion between term definitions means you end up with differing conclusions.

The semantic layer fixes this issue by implementing robust data governance to ensure consistency. It centralizes rules, providing a logical layer for metric definitions, naming conventions and calculations. Whether it’s the CMO’s dashboard or the marketing analyst’s spreadsheet, the “conversion rate” is always defined in the same way.

Cross-platform alignment

Platforms speak different languages. Shopify might refer to “Total Sales”, while your finance tool might call it “Net Revenue.” These mismatches can cause chaos when you’re trying to analyze performance, as you may not ‌be comparing the same numbers or including all the data.

A semantic layer aligns definitions across platforms. It pulls in data from diverse systems, providing one shared view of data, regardless of the format or vocabulary the original platform uses.

Performance optimization

Performance suffers when business logic lives inside dashboards on tools like Power BI or Tableau. 

These tools have to recalculate metrics every time a report loads. This slows performance, overloads the data warehouse and forces duplicate work.

Semantic layers sit between the data and data virtualization platforms. When you handle logic upstream in this way, you create cohesion before it ever hits the dashboards. This keeps dashboards running smoothly since they’re focusing on visualizing data, not processing it.

Semantic layer vs. data models and ETL tools

It’s easy to confuse a semantic layer platform with other parts of the modern data stack. But they’re not the same tools. Each plays a unique role in managing enterprise data to solve different challenges.

Here’s a breakdown of the differences and a summary of how they work together to analyze data.

ETL (Extract, Transform, Load): moves and cleans data

ETL tools handle data transformation at scale by connecting disparate data sources, like Meta Ads or Shopify.

They extract raw data, correct broken data formats and unify fields. Following this process, they load the data into a data warehouse or data lake.

While an ETL is important for cleaning and structuring business data, it doesn’t help business users understand the meaning of the data products. So you might transform “campaign_cost_usd” into a clean field, but it’s still a string of code for those who don’t understand what it means.

Data model: the data mart blueprint

A data model acts as a map for your physical data structures. It’s the tables, fields and data relationships within a data warehouse. By defining how data should be arranged, it gives structure to data pipelines so data entities — like orders, sessions and users — are loaded and connected correctly every time.

But a logical data model only defines how your data connects. It doesn’t explain why they matter. That’s up to the semantic layer.

Semantic layer: adds meaning to the structure

The semantic layer sits on top of your physical data models to provide ‌business representation. By defining shared data definitions, every team can refer to the same view of the data.

How does the semantic layer work with BI tools?

The semantic layer supports your business intelligence tools. It feeds them consistent logic so they’re ready to query performance data.

You no longer slow down data visualization by rewriting metrics within the tools themselves. You define them once in the semantic layer and reuse those metrics everywhere. This makes data analysis faster, lighter and more reliable.

Why does a semantic layer matter? Benefits to business users 

A semantic layer makes data usable and useful across your entire organization. 

Here’s how.

Consistency across teams

When different tools and teams define metrics differently, it gets confusing. A lack of alignment often ends in conflicting conclusions after reading data sets.

A semantic layer solves this by providing central definitions for business terms. This ensures everyone’s using the same logic, no matter the dashboard, tool or team they work with. 

As a result, performance metrics are clearer, so you’ll find yourself in fewer tug-of-wars over what’s ‘correct.’

Improved data literacy

Not every marketer is a data expert. They shouldn’t have to be — that’s not their job.

A semantic layer helps teams focus on their core work by making data insights easier to read. By turning your technical data structure into a familiar, business-friendly structure, more people can explore campaign data, track KPIs and spot trends. They no longer have to rely on BI and data analysts to interpret the data for them.

This leads to more confident marketers driven by data-backed ideas.

Faster reporting and data analytics

When teams have to spend time pulling, cleaning and fixing data from all different platforms, they lose time for strategy.

This kind of data admin can be a real time drain, especially for non-tech-savvy team members who struggle to do this quickly.

A semantic layer gives you a single access point to integrated, transformed data. Reports take minutes, instead of hours, opening up more time to optimize campaigns.

Enhanced data quality

As they say in data handling: garbage in, garbage out. If your data is a mess, your results will be.

A semantic layer handles business rules, field definitions and transformations in one place. This ensures mismatched currencies, broken formulas and outdated tags don’t creep into your data.

Instead, you get cleaner data products so decision-makers can trust that their results aren’t skewed by data inaccuracies.

Better decision-making

More than a third of marketers agree that it’s become even more important in the last year to use data to inform decisions.

But that only works if the data products are clean, consistent and well-defined. With coherent data, marketers can get a holistic view of what’s working and what isn’t across all platforms. 

It becomes far easier to justify strategy and figure out marketing spend when you can see how to attribute performance more clearly.

Improved cross-functional collaboration

One in five marketers says that their greatest data-driven marketing challenge is collaboration between departments. This often comes down to a lack of coherence around what the data means.

A semantic layer gives a shared language to that data, facilitating smoother interactions with analytics tools. As a result, marketing, product, sales and leadership teams can work better together since everyone’s drawing from the same source of truth.

With less second-guessing, there’s greater alignment on what’s‌ happening and how to solve challenges.

Future-proof scalability

Marketing tech stacks evolve quickly. New tools overtake legacy tools, introducing more confusion around the meaning behind data.

A semantic layer acts like a universal adapter for future data workloads. It allows you to plug in new platforms without reengineering your pipeline each time.

Not only does this mean smoother onboarding for new tools. A more agile data infrastructure allows you to pivot strategies as trends change, without having to reconfigure everything from scratch.

How Funnel works as a semantic foundation to fix messy marketing data

Funnel provides a semantic foundation for marketing data, aligning terms, metrics and transformations before they reach your BI tools.

It exports modeled data, but it’s not a full semantic layer because it doesn’t have the same architecture. Where a semantic layer serves logic via a query interface or metrics APIs, Funnel is a no-code interface that allows marketing teams to define and reuse data without ever touching the raw tables. 

It’s useful for marketing because it operates before the semantic layer, giving teams a head start by aligning schema definitions like cost and spend across 500+ marketing platforms. 

Marketing data is transformed before it reaches the data warehouse and moves through the semantic layer so it’s ready for analysis.

That said, it’s important to clarify the scope of metric reuse. Funnel’s logic is reusable within its own platform, but once data is exported to tools like Looker or Power BI, those definitions are no longer governed by Funnel. Any metric consistency beyond that point needs to be handled in your BI or semantic modeling layer.

Enterprise data teams might use Funnel with a semantic layer tool like dbt or Cube. However, Funnel is not a true semantic layer replacement for teams that need a data interface for things like training large language models (LLMs) and creating self-serve analytics for non-marketing data. 

How it all comes together

Here’s how a foundational tool like Funnel works in practice, connecting raw data to real insights.

1. Data sources: Data is pulled ‌from different sources, such as social, CRM and ad platforms. At this stage, it’s often messy and inconsistent.
2. Data integration: Funnel harmonizes fields like “spend” versus “cost,” acting as a reliable data normalization layer that’s purpose-built for marketing. Funnel enables no-code transformations so teams without technical knowledge can clean and unify data.
3. Metadata repository: Funnel’s system remembers all definitions, source mappings and field relationships. This way, ‌logic doesn’t get lost over time, and you don’t have to repeat the work.
4. Data model: Funnel enables users to set up shared rules without writing any code. Once these are defined, they work the same across all platforms and reports. 
5. Data export: After harmonizing and transforming in Funnel, you can send data to your data warehouse, dedicated semantic layer platform and BI tools with a couple of clicks. 
6. What’s next for Funnel?

While Funnel isn’t a semantic layer in the architectural sense, we’re building toward that vision for marketing data.

Internally, we’re evolving from simple data harmonization to deeper semantic modeling concepts — like defining reusable tables, relationships and logic layers that will enable consistent metrics across sources, teams and tools.

Our goal? A future where data definitions live natively inside Funnel, giving marketers semantic power without needing to learn SQL, manage a warehouse or patch together tools.

Today, Funnel gives you clean, harmonized data. Tomorrow, it gives you shared meaning at the core of your stack.

While we don’t yet support features like metric versioning or granular access control, we’re building toward that level of semantic maturity — without compromising the no-code accessibility that makes Funnel so powerful for marketers.

Data harmonization in action: 3 real-world data management use cases

When your marketing team only has to define logic once, they can apply it everywhere for faster reporting, better decision-making and real cross-departmental alignment.

Let’s explore how three different businesses use Funnel to put this into action.

Marketing agency use case: Digital Reach

Digital Reach is a B2B digital marketing agency that juggles campaign reporting across multiple clients. 

Before Funnel, they were managing dozens of spreadsheets with custom logic for each client. Not only was each version at risk of human error and incorrect formulae, but it was also very confusing.

Funnel fixed this by bringing together all client data — ads, CRM, web, email. Once inside Funnel’s no-code Data Hub, they created coherent definitions for all core metrics. Now they can reuse these definitions to power consistent dashboards in Google Data Studio and BigQuery, with every dashboard tailored to each client.

Thanks to this centralized metric logic, Funnel offers a lot of the same benefits that a semantic layer provides. Every account, report and team member can now operate using one source of truth.

And the impact?

• A reduction of 10,000+ hours of manual work per year
• 12x ROI in year one
• Reporting became a revenue driver, not a time sink

Retail use case: Limango

Online retail brand Limango was struggling to run Meta’s dynamic product ads. Performance data and backend sales numbers sat in separate systems without any shared logic to define their highest-performing products.

Funnel now acts as their bridge. It pulls ad data and combines it with backend performance metrics, applying product-level rules. After applying the logic once, the team at Limango can reuse it daily without redefining the metrics. The data exports to Power BI and BigQuery for reporting, visualization and optimization.

Now, teams know what “good” looks like in the context of their data pipeline and what to cut back on.

As a result, Limango benefits from:

• Up to 20% lower costs per lead
• Reduced manual work
• A more reliable and scalable, data-driven approach to Meta Ads optimization

Multi-brand ecommerce use case: Witt-Gruppe

Multi-brand ecommerce company, Witt-Gruppe runs 20+ online shops across Europe. One of its greatest struggles was scattered marketing data and inconsistent KPI definitions across brands and countries.

By integrating Funnel, they now automate data collection from more than 100 digital marketing partners and standardize KPI logic. Thanks to Funnel’s no-code platform, non-technical marketers can manage logic updates directly without SQL.

Now, every stakeholder sees the same reports, based on the same definitions.

The outcome?

• 90% reduction in manual reporting time
• Daily reporting rather than weekly reporting
• Better cross-team trust in campaign performance

How Funnel supports semantic layer principles

While Funnel isn’t technically a universal semantic layer, it supports semantic layer principles.

But it’s not just about ‌semantics. Funnel is also a data integration tool. This gives marketers a complete structure to turn raw data into reusable, trustworthy insights. And it does it all without code or the need for IT and data analyst support.

Funnel connects to over 500 platforms, standardizing incoming data on import. This puts data feeds from Meta, Google Ads, Shopify and other platforms into the same interface, rolling them up into comparable metrics. 

The reason it’s a good choice for marketing teams is that it offers a lot of the same benefits as a full semantic layer by normalizing schemas and helping marketers and analysts speak the same data language — but without needing help from engineers. 

It’s all in the semantics

You can have clean data. You can have a solid data model. 

But do you have a shared meaning for everything? 

If everyone’s speaking different languages, reports still won’t match. That’s where the semantic layer comes in. It translates structure into clarity and confusion into action, without the need for IT and data teams to step in to help. It turns jumbled data into readable metrics so teams can move faster, make better decisions and trust what they see.

Funnel supports these principles from the ground up.

Combining no-code data integration, reusable logic and clean exports into BI tools, it gives you both the building blocks and the blueprint.

If you're serious about marketing intelligence, semantic clarity isn’t optional. You need to get everyone on the same page.

See how Funnel helps clarify your marketing data — no SQL required.