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HomeBusiness IntelligenceAPI-First and Headless BI | What You Must Know

API-First and Headless BI | What You Must Know

The normal enterprise intelligence (BI) stack is constructed on many years of legacy applied sciences that do not match the brand new wave of information consumption. As organizations transfer from conventional desktop BI to cloud-based options, there’s an evolution by way of structure and the way in which analytics is delivered.

The ever-growing variety of information customers and use circumstances requires firms to have the ability to present analytics in an agile method – to builders, finish customers, and prospects – to help their quickly altering enterprise wants.

We’d like new methods to construct analytics to adapt to this new wave of information consumption. By utilizing an API-first strategy and headless BI, we will construct analytics options to share constant information with all customers in the way in which they need to devour it. Thus, headless BI and API-first analytics platforms are must-haves for firms that need to obtain the pliability required by trendy analytics.

What does API-first imply?

API-first is an strategy to product improvement the place APIs are considered as first-class residents. The strategy concentrates on constructing reusable and simply accessible APIs that consumer purposes can use and devour. Historically, firms would first develop the product after which add APIs on prime of it. In API-first, this mindset is reversed — APIs are constructed first and positioned on the middle of the product. By doing so, firms be sure that every part within the product is consumable by way of APIs.

What’s headless BI?

Headless BI is a newly launched information analytics structure idea to work together and devour metrics within the trendy information stack. Headless BI is an analytical back-end that makes standardized metrics accessible by way of APIs, SDKs, and customary protocols. It’s constructed utilizing the API-first strategy permitting all of the analytical definitions and capabilities to be out there via well-documented, declarative APIs.

GoodData headless BI visualization
Headless BI ensures that everybody works with the identical constant definitions of metrics.

In conventional BI, the backend (the “physique”) is tightly coupled with the platform’s UI (the “head”). As a result of different instruments can not entry the metrics outlined within the conventional BI, every separate device your finish customers want should have their very own metric definitions which they will use. In headless BI, the backend and the presentation layer are decoupled, permitting metric definitions to be consumed by any variety of completely different heads — information instruments, ML fashions, and purposes. And since each head accesses the identical supply of metrics, headless BI ensures that everybody in your group — staff, prospects, and companions — works with the identical constant definitions no matter what entrance finish they use.

Why do API-first and headless BI matter in analytics?

At present, firms are dealing with conditions the place constant metrics should be shared and made out there for varied purposes and customers — with various ranges of technical expertise — to make higher enterprise choices. However the issue shouldn’t be solely making metrics out there; firms are additionally struggling to develop analytics options and information purposes in a contemporary means.

Most analytics platforms should not designed to help software program improvement finest practices as a result of we’re not in a position to entry and handle the code we create after we construct analytics with the platforms. API-first analytics adjustments this paradigm by permitting us to learn and write all of the underlying metadata of the platform — in a declarative format — and offering open APIs to automate the continuing supply course of.

Analytics platforms constructed following the API-first strategy and supporting the headless BI use case can assist firms with these ache factors and allow customers to be extra productive of their domains. Now, let’s see how API-first and headless BI can assist completely different personas succeed of their roles.


Declarative APIs enable builders to handle and combine their analytics options like every other utility supply code. Knowledge groups can combine analytics improvement into their CI/CD processes and work in parallel to model, merge, robotically check, and roll out updates and new information merchandise to manufacturing. And since all analytics definitions are consumable by way of open APIs, they’re straightforward to reuse or repurpose utilizing templates. For instance, when there’s a must construct a brand new information utility, builders can keep away from ranging from scratch by leveraging the analytics they’ve already created.

Visualization of data analytics automation using CI/CD
Declarative APIs enable builders to combine analytics improvement into their CI/CD processes.

By serving metrics over APIs, API-first analytics enable builders to take the benefit of the developer instruments and UI frameworks of their selection when constructing information purposes, portals, and enterprise processes. They don’t must know the best way to be a part of tables or information units to create metrics as a result of they will simply devour the metrics from the headless BI platform and mix them as they should get the result they require. Thus, they will think about coding the wanted interface whereas the platform handles the computations. With open APIs and open supply SDKs (like Python and React), builders can construct customized analytics experiences sooner and increase them as wanted.

Finish customers

Decoupling the analytical backend and the presentation layer permits finish customers — analysts, information scientists, and enterprise customers — to make use of any information device they see as the perfect match for the job. Historically, information fashions and metrics needed to be created for every device individually, which is time-consuming and liable to errors. With headless BI, finish customers from completely different groups, departments, and areas can entry and use standardized metric definitions from a single repository and yield right outcomes throughout your complete enterprise.

GoodData analytics data model for external data consumers
Make the most of standardized metric definitions throughout your information instruments.

And since the decoupling makes the info stack front-end agnostic, finish customers can improve their information instruments and purposes when wanted. As soon as they establish a necessity to change from one device to a different — resulting from efficiency points, pricing issues, or expertise developments — they will simply join it to the headless BI platform and proceed analyzing the info with no need information groups to rebuild metrics for them.

How does GoodData slot in?

GoodData, after in-depth analysis and testing, re-engineered its analytics platform to help the API-first strategy. By opening the platform to be consumed not simply by way of its personal UI but in addition third get together interfaces, GoodData strives to fulfill the brand new wave of information consumption necessities.

GoodData’s API-first analytics platform, along with its Headless BI function, allows firms to develop analytics options like every other software program and supply constant analytics to all finish customers and purposes. Because the chief in BI, GoodData offers versatile and customizable options for all finish customers, no matter necessities or technical functionality.

Where GoodData sits in BI ecosystem
Present constant analytics to all finish customers with GoodData.

Searching for extra from GoodData?

GoodData invitations you to dive deeper into your journey by brushing up on the dear insights we offer into our merchandise and the enterprise intelligence trade at massive. Attempt GoodData’s absolutely managed, API-first analytics platform at no cost or learn the next assets concerning the subject:

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