When you break down the basic architecture of a BI tool, you have the semantic layer and modeling part that’s typically handled by data teams, and the visualization part (reports, dashboards, charts) that should ideally be accessible to both analysts and business users. The semantic layer is where you define your metrics and dimensions, and create a company-wide “data language” that everyone in the organization can understand. The visualization part is where the data from the semantic layer is shown visually in a way that makes it easy to derive actionable insights.
In recent years, the term “Headless BI” has popped up and gained popularity in the data world. Headless BI removes the “user interface” (visualization layer), and refers to the approach of consuming data in different “heads.” It detaches and uncouples the components of the BI process so that the semantic layer can function as a single entity on its own, separated from the visualization layer. This way, consistent information / metrics can be provided to the various “heads”. Essentially, it’s a BI tool that delivers data and insights to other applications or systems via API, rather than presenting them through a visual dashboard within the same tool.
One of the main reasons Headless BI has made a splash lately is because it avoids the “lock-in” of one single visualization or data consumption layer. While Headless BI can offer great flexibility in terms of integrating BI data with specific, existing applications and satisfy different visualization requirements, it’s important to watch out for some of the downsides — especially for companies who are just getting started on their data journey.
The biggest downside for companies who are getting started on their data journey is that they’re going to require developer resources and technical expertise to put a Headless BI tool in place. It places a heavy burden on the technical team and requires effort in terms of set up, maintenance, development and API integration.
This means that the time to insight and action may not be fast enough for mission-critical business decisions, since technically skilled data teams must be involved in the process, and will likely stand in the way as a bottleneck.
These technical resources can also become quite expensive. If your company is small or medium-sized, or if you’re operating on lean teams, it may be tough to put a full developer team in place to manage this. Especially when there are modern, robust BI tools on the market include a semantic layer - it’s not necessary to keep things separated.
To overcome the limitations of Headless BI tools, and save costs, companies should consider adopting a comprehensive BI Platform with a built-in semantic layer. Here's why:
A semantic layer acts as a unified “translation” layer, providing a logical representation of the underlying data sources. This empowers users to model complex data relationships, define business rules, and create reusable calculations and metrics. With the ability to harmonize and integrate data from multiple sources, organizations can leverage advanced analytics and gain deeper insights.
As mentioned, Headless BI tools require technical expertise to set up and maintain - slowing down self-service initiatives and data adoption. Many modern BI platforms include a robust semantic layer as well as a user-friendly interface and intuitive data exploration capabilities. Business users can access pre-modeled, curated data, and leverage self-service features such as drag-and-drop visualizations, ad-hoc querying, and data discovery. Not only does this empower users to autonomously explore data and uncover actionable insights without relying on technical teams, they build familiarity with one tool, which will deepen their trust in data.
By centralizing data definitions, business rules, and calculations, a semantic layer that’s built into the BI platform ensures consistency across reports and visualizations — throughout the end-to-end data process. Changes made to data models and definitions automatically propagate throughout the system, reducing the risk of errors and ensuring data accuracy. Furthermore, a BI platform provides robust governance features, including access controls, data lineage, and versioning, promoting data integrity and accuracy through overall better control.
A BI platform with a semantic layer is designed to accommodate the evolving needs of growing organizations. It provides scalability to handle increasing data volumes and supports integration with emerging technologies such as AI and machine learning. The semantic layer serves as a foundation for a scalable and future-proofed data process that can support your company as it grows.
Having a semantic layer that’s built into your BI platform removes dependency on technical resources, saves costs, and reduces risk around scalability and governance. By having a semantic layer that’s highly in sync with the visualization layer (within the same platform), companies can ensure that the data that’s being consumed by end business users and decision-makers is based on accurate definitions and metrics. These days, modern BI platforms include strong visualization capabilities, as well as built-in governance and a robust semantic layer. Having everything in one place will maximize the chances of end data consumers building trust in data and making more decisions on the data based in their BI platform.
If you have any questions, comments, or want to discuss how to set up your BI stack, get in touch with our data experts!