What is Headless BI? (And How Does It Impact Embedded Analytics)

Headless is a term one only really hears in the context of content management systems (CMS). However, headless BI is becoming relevant in running a business and launching a software product.

Planning for data analytics and business intelligence are essential parts of launching a successful startup. Doing so boosts customer experience and brand differentiation in different data teams.

Traditionally, companies look for a BI vendor that supports white label and embedded analytics. In most cases, the buy vs. build decision of partnering with a BI vendor rather than building your analytics solution makes sense.

However, with the growth of modern BI platforms and APIs, the concept of headless BI is becoming a popular option. It provides the ability to leverage a BI vendor for all the “plumbing” of a BI tool. Still, it allows you to seamlessly integrate the output into your brand and development teams’ desires, including the data tools they might need.

So what is headless BI, and how does it affect embedded analytics? Let’s take a look.

What is Headless BI?

The term “headless” refers to a system, usually a BI tool or CMS, that can perform its designated duty without the need for a front-end presentation layer, such as your website or a user interface. Without this “head,” developers can choose different types of outputs using an API. Headless architecture does not involve a heavy focus on display methods but rather on delivery and storage.

Headless software can run without a graphical user interface (GUI), to put it simply. Some software does not require a user interface whatsoever.

The primary purpose behind this is to eliminate the close partnership between BI components and “release” the tool’s semantic model as a service that users can share via APIs and different interfaces such as DataFrame, Dataset, along with other BI tools available.

This data model can measure a variety of things, such as definition, groups, permissions, etc., one time and share these measurements across all of one’s applications and business intelligence processes. It can also work for many data warehouse setups.

The Benefits of Headless BI

There are so many benefits to using headless BI for overall business intelligence. It can provide consistent insights that people can access in real-time. Such insights include a variety of different data points for many existing BI solutions. This makes it possible to establish consistency and top-notch governance through individual metrics and a simple semantic model.

With traditional business intelligence, one would constantly have to rebuild models, metrics, and deployment. It makes it possible to create a single source of truth without the need to rebuild continually. It is also usually self-hosted as an embedded headless BI solution.

There are several offerings that it provides that traditional BI does not:

  • A single source of metrics.
  • Analytics that are tracked in real-time.
  • Open APIs.
  • Architecture that allows for microservices.
  • Declarative definitions.
  • Composable and efficient UI.
  • Ultra-flexible deployment.

Metrics developed through headless BI are compostable and can be easily reused in various contexts without the need for knowledge of the existing data structure, regardless of metric definition. In the context of embedded analytics, data people can build analytics on top of simple and user-friendly data abstractions.

Headless BI makes its integration with your software to achieve embedded analytics much more manageable. It is also ideal for developing your front-end application very quickly, as one can build dashboards in a programmatic way in the native programming language your application already has.

To put it simply, data teams can build headless BI in layers without the need to rebuild anything using traditional BI tools by modern data teams.

Use Cases

There are a few significant use cases for headless BI in the context of embedded analytics. To start, it can be pretty helpful for straightforward production deployments. It can also make the process of developing self-service embedded analytics and larger production deployments much more precise, such as in a dashboard.

Headless BI and Embedded Analytics

As more businesses begin to use top-notch BI software, many are looking for ways to implement their embedded analytics into their software that doesn’t require an insane amount of development and complexity.

The need for embedded analytics for every new application is becoming more common, as end-users want quicker and better decisions based on data. Instead of forcing users to move their data into a third-party application for analytics, many startups are trying to simplify the process of implementing embedded analytics into their apps so that those who need it can access them in real-time.

Using this can simplify the process of embedding analytics into apps by programmatically delivering modular sets of functionality that make scaling up and down easier.

Choosing this instead of traditional BI applications to embed analytics removes the massive range of functions and modules that make the embedding process extraordinarily complex and time-consuming. Instead, headless BI capabilities can embed analytics through APIs that embedded analytics tools can leverage instead of forcing end-users to use separate user interfaces to utilize analytics for data science.

In general, it can make the development of embedded analytics much more intuitive without heavy development overload—an excellent solution for startups with minimal development teams on deck.

The Downsides of Headless BI for Embedded Analytics

No innovative software is perfect. There are some downsides to using headless BI software for embedded analytics, especially self-hosted embedded headless BI.

To start, you’re going to need developer resources. What’s nice about Yurbi is that we offer multiple ways to achieve embedded analytics, which includes a no-code option for software vendors who do not have the developer resources to spare.

However, by nature, headless BI puts the burden of front-end development and API integration on the developer team. So, only companies with a well-managed development team should consider the headless BI approach.

Also, since Yurbi is a self-hosted solution, it’s vital to have the right processes to maintain self-hosted headless BI software. You will need to maintain and operate your headless BI solution by backing it up regularly and keeping your servers patched. If your startup has a notably small development team, self-hosted headless BI may be a bit difficult to implement.

How Yurbi Can Deliver Headless BI Capabilities to Your Embedded Analytics

It would be best if you had a good BI tool for your business to execute your embedded analytics to the best of your abilities. Yurbi can help you deliver your headless BI solution and its capabilities to your embedded analytics when it comes to embedded analytics.

Yurbi provides a robust API capable of replicating almost everything that we have on our front-end interface.

Here are a few of the APIs that can provide a headless BI approach for everyday use cases:

Secure Data Broker

This is perhaps the most important of the use cases. Yurbi allows the creation of datasets and reports that can come from one or more data sources. Queries are performed live against the underlying data sources, and it doesn’t require the builder of the reports to know anything about the underlying data schema or how to write SQL code.

In a headless mode, developers can query the Yurbi API to query any report and retrieve the results in an XML or JSON data package. Further, this data is appropriately limited to the multi-tenant security policies applied to the authenticated user.

Scheduling facilities

An everyday use case for software vendors is to allow the scheduling and notification of reports and dashboards. Yurbi has an information scheduled built-in, which enables the execution of information, in the context of the logged-in user, with complete data governance and security policies enforced. Reports can be scheduled individually or in a group.

These APIs can quickly allow developers to integrate this functionality into their products while leveraging their front-end elements in a headless BI mode.

User Provisioning

A must-have for a seamless integration is to connect the user onboarding of your software with your embedded BI platform. In the Yurbi model, the user account is used for much more than just authentication; it contains the security model of roles, data-level security, report access, and all the personal preferences that can be applied, such as caching scheduling, timezone, and more.

All of these can be queries and set via APIs to automate the user management process.

Licensing for Headless BI

Yurbi also provides multiple methods for licensing, all of which can be managed programmatically via API. Yurbi offers concurrent/shared pool license models, named user per data source models, and public view/anonymous licensing options. This allows our partners to find a BI platform that is not only powerful but also affordable.

Contact Us To Learn More

Headless BI is one additional option that Yurbi provides when it comes to embedded analytics. For software vendors looking for an API-oriented solution to integrate with and provide them with the data broker-type features discussed above, we invite you to take a deeper look at Yurbi.

Yurbi can potentially save your development team countless hours by allowing them to focus on your interface and core functionality, and not diving into the rabbit-hole of creating a BI platform in additional to your core product.

Contact us to discuss your requirements and learn more about Yurbi. Schedule a live demo with one of our technical experts today.

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What is Headless BI? (And How Does It Impact Embedded Analytics)

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