What is Headless BI?

Data Engineering
Feb 2, 2024
What is Headless BI?

In recent years, there has been an explosion of new data analytics tools and technologies. And with more tools, the complexity of data analytics increases. More specifically, there are two challenges: having consistent data and metric definitions.

Instead of looking into several different apps to solve this problem, data teams can kill two birds with one stone: a modern approach called headless BI. If traditional business intelligence tools give you issues with data definitions and consistency - we have a solution.

What is Headless BI?

Headless BI is an approach to business intelligence (BI) where the presentation layer is separated from the data processing and analytics. It detaches the way that data is analyzed and visualized from the way the end-users see it in your app.

In a traditional BI tool, the UI or the presentation and the backend data processing are tightly integrated with each other, making it difficult to change the visualizations your end-users see.

Headless means that there is no front-end: there is no presentation layer. The front and back ends are separated, which allows you to connect any front- or back-end to the headless BI through APIs.

headless bi structure

This solves one major problem that many businesses have: the consistency in data. Defining and using your metrics consistently across apps and workflows can be challenging and this is where headless BI steps in - solving many different use cases for a business using a modern data stack.

What does a headless BI architecture look like?

If you’ve never encountered headless BI before, here is a simple breakdown of how it works - and why you should care.

First, there is a semantic layer sitting between your data sources and connectors on the one hand, and your presentation layer on the other hand, such as BI tools, embedded analytics tools or metrics shown in custom apps, websites or parts of your app.

For example, you can have a lead score in a marketing automation app. It can be used for both triggering and segmenting emails but is also presented as a part of an analytics dashboard. The metric stays the same, but its intended use changes.

Second, headless BI serves as a single source of truth, where you have naming conventions for metrics (in the semantic layer) and model your data before visualizing it.

Last but not least, open APIs allow you to send data from any data source (such as a data warehouse or database) to the headless BI and then visualize it through the API. For visualization, you can use any BI tool, embedded analytics layer, or app.

headless BI in embedded infrastructures

Why consider headless BI?

If you’re happy with your current business intelligence setup, you may think that it makes no sense to give headless BI a shot. However, some unique advantages to this approach make it worthy of consideration.

Developer-friendly to maintain

Business users can benefit from headless BI but the ones that will be even happier are your developers and data engineers. The headless setup lets them easily update an underlying data structure or migrate it to a different data warehouse. Since the backend is separated from the presentation, the presentation layer does not suffer.

This ensures your entire data stack is easier to maintain and allows your developers to work uninterrupted without causing any downtime and interrupting the end-user experience. Your developers can help create a self-service visualization tool that just about any end-user can manage.

Consistent and reusable metrics

The metrics in headless BI are defined in the semantic layer. This ensures ease of use as anyone in your team can reuse the same definitions and metrics.

It can save some costs for your team, but more importantly, it saves a lot of time for your team who don’t have to define the same metrics every time from scratch.

Seamless integration

Headless BI uses a modular infrastructure, which means that you can benefit from the easy integrations into other apps, website, and workflows. For anyone using embedded analytics, headless BI is the logical choice as you can present personalized, real-time analytics to your app users.

This makes headless BI apps much easier to use compared to enterprise solutions like Looker, Tableau, or Power BI.

What makes BI “Headless”?

What kind of functionality makes a business intelligence app headless? Let’s take a look.

Data modeling

Want to make sure that both marketing and sales get the same results when creating visualizations from your data? A headless BI platform makes this possible through data modeling.

At this stage, you can model the relationships between data, set up data schemas, columns, tables and dimensions.

The end results are datasets that can be used consistently by everyone in your business. No matter who uses the headless BI solution in your team, they’ll get to use the same data in the same way.

Semantic layer

Within the semantic layer, you define and store the metrics and business concepts you use throughout your business. For example, defining what sales metrics you will use in your eCommerce sales dashboard.

They should only be defined once, and everyone in your team can understand where the metrics are pulled from. This means that if 10 different people create 10 different dashboards, they’ll all get consistent results when querying data.

Access control

Within your typical headless BI tool, you can set up access control and determine who can access which data, metrics, data sources, etc.

For example, you can set up your headless business intelligence system so that only managers have access to high-level metrics. Besides making it easy to assign permissions, this setup ensures that you stay compliant, be it laws in your country or industry.

You can define access once in the central system, instead of defining it later on for every application where the data is used. This ensures that there are no data breaches or inconsistencies.


You get to define how often your data is refreshed and when it is stored, all from a central location. This ensures that no matter how many applications you use, the person viewing them sees the same dataset with the most recent updates.

For example, whether it’s a customer of yours looking at an embedded analytics report in their dashboard, or an internal team analyzing user metrics in your internal BI tool - they get to see the same set of data.

Another added benefit is that all data is cached at once, which avoids having the same caching requests being made from different apps. This saves time and money for everyone involved.


Without APIs, there would be no headless BI. On its own, headless BI does not have a user interface, which means you need to connect it to different apps, such as those for data visualization and embedded analytics.

APIs also allow you to connect the data from various sources, such as data warehouses. 

Using headless BI for embedded analytics

If you want to use headless BI for embedded data analytics, there might be some concerns. However, it’s actually quite simple to do.

In the first scenario, you’re showing reports to your SaaS product end-users. As they use the product, their needs change and you should be able to quickly make changes to the dashboards and the underlying data models. For example, someone needs to drill down deeper into their marketing KPIs.

With headless BI architecture, this is a breeze because the front-end and the back-end are completely separate. You can make underlying changes in the front-end without affecting in-production reports.

In the second scenario, let’s picture this: you want to give your customers fully customized, branded reports for different user profiles. On top of that, all of these users need to have different types of access to different data sources. And if you have multiple apps or parts of an app, all of this can get very confusing, very fast.

Headless BI helps simplify the process as all of these interfaces work from the same semantic layer. With tools like, you can build as many reports and insights as you want. The basis is comprised of the same data models and definitions which are completely separate from the data visualizations that the end-user sees.

How to use headless BI with Luzmo

Luzmo is embedded analytics software that lets you add customizable data visualizations to just about any SaaS app. Luzmo is built API-first, allowing you to connect it to your existing tech stack, without hiring additional developers, data engineers, or analysts. You don’t even need to be an SQL expert to get started!

Here is how Luzmo can help you use headless BI to give your end-users more control of their data.

Data modelling

With Luzmo, you can add various data sources and build derived columns on top of your datasets using formulas. You can rename these columns, add descriptions to them and make datasets and predefined formulas your developer team can immediately use in your organization.

Access control

Luzmo’s multi-tenant embedding allows you to set access control intuitively and securely, reusing any authentication system you already have in place. As you’re reading this, we’re about to release our Access Control Layer, one of the most technically advanced access management layers in the industry, allowing you to pinpoint who has access to what data and when.


Luzmo caches your queries and optimizes their speed. Our acceleration service, Warp, lets you build advanced analytics applications, even if your data infrastructure isn’t optimally built for the analytics use case. We don’t cache the results, we cache the source, which is more powerful and faster.


When your formulas and datasets are ready, you’ve defined access controls and set up caching, you can run your queries through our API to start exploring your data. This can be used in your SaaS product, for your internal reports or anywhere else where an API can be connected. This is what makes Luzmo truly headless - you can use it for data visualizations without depending on the user interface of our app.

Wrapping up

Headless BI is the future of business intelligence as it’s reusable, developer-friendly, and modular. Apps such as Luzmo take this philosophy one step further - our API-first approach allows you to get amazing analytics insights without using the UI of our app.

If you’re tired of complex analytics experiences, architectures that are difficult to maintain and asking your developers to fix issues all the time - try out Luzmo!

Build powerful analytics without being dependent on user experience - grab your free demo and we’ll show you how!

Mile Zivkovic

Mile Zivkovic

Senior Content Writer

Mile Zivkovic is a content marketer specializing in SaaS. Since 2016, he’s worked on content strategy, creation and promotion for software vendors in verticals such as BI, project management, time tracking, HR and many others.

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