Rethinking business intelligence: Why the semantic layer matters now

Building reports isn’t broken, but how we think about them is.

Over the past two decades, organizations have poured effort into dashboards, reports, and analytics tools. Yet most still struggle with inconsistent KPIs, duplicated metrics, and siloed insights. Changing your BI (business intelligence) tool’s front end or backend won’t fix that. What’s needed is a mindset shift: from building reports to defining and delivering trusted metrics.

This post explores why the semantic layer is central to the next generation of business intelligence and what it takes to get there.

From sprawling reports to governed metrics in your bi tools 

Traditional reporting has focused on specific BI tools, tables of data, visualizations and, of course, exporting data to Excel. But real value comes from consistently answering business-critical questions in all user interfaces. That requires a clear chain of logic:

1. Define the questions the business needs to answer
   
   
2. Clarify the business concepts and KPIs involved
   
   
3. Link metrics to those questions, and assign ownership
   
   
   1. Deliver governed metrics to users across Power BI and other tools they already use
      
      
4. Continuously monitor usage and relevance

Without this chain, organizations end up with disconnected reports, competing definitions, and a lack of accountability. Take for example a question like, how many active users do we have? How many different answers can you have to this question: one Excel from last week says this, PowerBI says that, and yet one analyst’s SQL code gets you to one more different figure.

Enter the semantic layer 

A semantic layer sits between your raw data and the tools your teams use every day. It acts as a single source of truth for metrics, dimensions, and business definitions.

In technical terms, the semantic layer resembles a modernized version of the fact/dimension model but built to support AI, real-time apps, and modular data stacks. There are also a lot of similarities to OLAP (Online Analytical Processing) cubes from past days. 

In semantic layers, entities or dimensions define the “lens” (e.g., customer, product, time), and measures capture the metrics (e.g., revenue, churn rate, active users). All calculations happen dynamically; no need to replicate data into each tool’s engine.

Terminology

In recent years vendors have called semantic layers with different names like Headless BI, Metrics Layer, or Metrics store. Now luckily it seems that everyone is talking about semantic layers. But it can still be that the same concept is referred to with a slightly different name. Also, vendor talks about semantic models, which are essentially data models. The semantic layer consists of all the data models business needs, and those should be interoperable. 

Semantic layer in Modern Data Platform

In modern data platforms, the semantic layer is a natural step to centralize metric definitions. I have noticed that when a customer has a data platform in place and initial reports are constructed, they start to grave for more metrics. Usually, the data is already in the data platform.

For example, when you integrate a web analytics system (e.g. Google Analytics) to your data platform, you get tons of data and metrics that you don’t initially need or use. When you start to build metrics to current dashboards or build a number of new dashboards with this data, there is a huge overhead of old reports needed to match the new reports. Scaling the report building becomes important, but in current systems, this is usually very hard or even impossible. If every metric needs to be defined separately in every system or report, it will be expensive. 

What I also see is that a need for a metric in a given dashboard might be just one of case: for one time and one analysis, the business needs to see active users side by side with system uptime. When the question is answered and issues are solved, the analysis is no longer relevant.

•   Report scaling is time consuming; a lot of time is spent on defining unnecessary metrics
•   Reports are harder to maintain, complexity rises with new sources.

Why it matters

• Consistency: Every tool and report references the same logic
• Speed: Queries are optimized for performance
• Simplicity: Developers define logic once, not in every dashboard
• Governance: Centralized control with decentralized access.

Why now?

Semantic layers aren’t new, but the need for them is finally urgent. Why?

• Generative AI is only as smart as the context it’s given. Semantic layers provide that context
• Modern BI tools like Sigma, Omni, Steep, and Evidence are built to work with semantic layers from day one
• Data teams are scaling, and centralized metric governance helps them move faster without losing control.

How to get started

You don’t need to rebuild everything at once. Start with these steps:

• Design a modular architecture: Tools like Snowflake and dbt make this easier.
• Build a robust data model as a foundation (don’t skip this!)
• Layer in metrics logic using dbt semantic layer, Cube, or similar
• Pilot with a semantic-layer-compatible tool. Even Excel or an API layer can work
• Gradually phase out overlapping BI logic from legacy dashboards.

Done right, your business intelligence setup evolves your reporting into something more powerful: a governed, AI-ready knowledge layer for your business.

Final thoughts

Business intelligence is evolving and it’s not about prettier charts. It’s about trust, speed, and scalability. The dashboards you have in use can never answer all the questions. What do you do when this happens? Submit a request ticket for a new dashboard?

At Recordly, we help organizations build semantic-layer-based BI architectures that fit, using modern data platforms. Whether you’re migrating from Power BI, modernizing Snowflake use, or designing a semantic-first data stack, Recordly can help you get there. Reach out and we'll take a look!