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Rethinking the Semantic Layer: Empowering Business Logic First
MARKO JFebruary 10, 2025
Semantic layers have established themselves as a critical component for bridging the gap between raw data and business insights. However, as data landscapes evolve, so too must the tools we use to interact with that data. Traditionally, semantic layers are built on top of an existing physical model of the data — requiring users to first create or adapt a physical schema before layering business logic on top. But what if you could flip that process on its head and focus on the business logic first?
At Asemic, we’ve developed a new approach tailored specifically for Product Analytics. Our solution prioritizes business-level design while letting the application take care of the physical modeling, allowing users to seamlessly work with behavioral data. Here’s a closer look at why this difference matters and how it transforms the way teams work with data.
Semantic Layer: Physical Model First
Currently available semantic layer solutions follow a well-worn path: they sit on top of a predefined physical model of your data. In this workflow:
- Define the Physical Model: Data engineers create or adapt the schema in the data warehouse. This schema represents how data is stored and structured.
- Build the Logical Layer: Analysts or business users then apply business definitions, metrics, and hierarchies over this schema to create a logical model that maps closer to how the business thinks about the data.
- Deliver Insights: Reports and dashboards are built on top of this logical layer, translating raw data into actionable insights.
This approach works for teams with mature data models but often leads to complexity. In a fast-paced business environment, this step doesn't always get enough attention it deserves, leading to the explosion of the number of tables in the data warehouse. Managing the rising complexity becomes difficult event with a semantic layer. In the worst scenario business users will lose trust in data and the team managing it. Setting up and maintaining a good model can be labor-intensive and still fall short in delivering actionable insights.
The Asemic: Logical Design First
At Asemic, we’ve taken a different route. We let you focus on designing at the logical/business level first while our application takes care of the underlying physical modeling for you. Here’s how it works:
- Define Business Logic Directly: Instead of worrying about how data is stored or structured, users define metrics, KPIs, and relationships in terms that make sense for the business. You focus purely on what you want to analyze, without getting bogged down in schema design. Users can define metrics, events, and sequences in plain business terms using a low-code or even no-code interface.
- Automated Physical Modeling: Asemic’s intelligent engine then automatically optimizes and structures the physical model in the data warehouse based on the business definitions. It adapts the data structure dynamically as your logic evolves, removing the manual step of defining a rigid physical model first.
- Scalable, Agility-Focused Insights: Because the physical model is auto-generated and optimized behind the scenes, your analysis remains flexible. If business requirements change, you can easily update the logical design without all the overhead work in the data warehouse.
- Unlock Advanced Functionality: Even after significant setup, traditional tools may struggle to achieve the depth and flexibility needed for advanced behavioral analytics. Asemic’s Semantic Layer provides this functionality natively, unlocking richer insights with less effort.
This approach significantly reduces the burden on data engineers and allows analysts and business users to iterate faster. The result? More time spent on deriving insights and less time on managing data infrastructure.
Why This Matters: Flexibility and Agility in Analytics
In today’s rapidly changing business environment, flexibility and speed are key. Traditional semantic layers require teams to lock in physical models before they can explore business insights, which can slow down innovation and create bottlenecks. Asemic’s logical model-first approach eliminates that bottleneck.
By allowing you to focus purely on business logic, you get:
- Faster Iteration: Skip the lengthy physical modeling phase and go straight to testing and refining your metrics and analyses.
- Greater Alignment with Business Needs: Business users can define logic in terms they understand which brings them a step closer to the data people, improving communication and implicitly fostering data culture in the organisation.
- Lower Engineering Overhead: Data teams can offload the responsibility of managing complex schema design, leaving them more time to focus on strategic data initiatives. Because the application maintains the physical model based on the logical model, there is no overhead in synchronising these, as well.
- Tailored for Behavioral Data: Our solution is built from the ground up for Product Analytics, allowing you to achieve a level of functionality and insight that would be difficult or impossible with general-purpose tools.
The Future of Semantic Layers
As product teams become more data-driven, tools must evolve to support deeper, faster insights. Traditional semantic layers — built to model data first—can be cumbersome and difficult to align with ever-changing business needs, particularly when dealing with the complexity of user behavior. Asemic’s approach turns that model around by focusing on what matters most: business logic and behavioral insights.
By building a Semantic Layer tailored specifically for Product Analytics, we empower teams to quickly define, refine, and explore metrics that truly matter—without the heavy setup. Whether you’re tracking user engagement, activation, or feature adoption, Asemic gives you the flexibility to work at the speed of business.
Ready to see how Asemic’s Semantic Layer can revolutionize your product analytics? Reach out to learn more about how our platform can help you get from data to insights faster.