The popularity of data-driven management has led to the proliferation of business intelligence (BI) tools that provide all kinds of data-based services, like automation, embedded analytics, or data visualization. Each one serves a purpose and can be used by different teams within the same company. But what happens when they yield disparate numbers, despite drawing information from the same source?
The answer relates to their architecture and the solution is headless BI. Here’s why.
For BI solutions to transform raw data into helpful visualizations or reports that generate actionable insights, they first have to define the metrics and model the data they retrieve from datasets according to those metrics. The problem is that each tool defines metrics differently and they don’t communicate well with each other.
This means that setting up each BI solution requires the repetition of the same process, which is very time-consuming. It also runs the risk of inputting different parameters that can lead to varying outputs and inconsistencies between reports. And finally, transferring information from one tool to another becomes very labor-intensive as it often has to be done by manually exporting and importing data.
To avoid these issues and to make BI truly useful to customers, metric standardization should be the norm. This can be achieved using tools with a headless BI approach that provide agility and consistency to their users by having a one-time setup and by sharing information with other BI tools. Let’s take a look at how it works.
How Does Headless BI Work?
In the basic architecture of a BI tool, the semantic layer does the data modeling and measuring, and it is tightly coupled with the data visualization (i.e. reports, dashboards, etc.). As each tool has its own semantic layer, there are multiple measures and data structures, which is where inconsistencies in analytics stem from.
What the “headless” approach does is that it uncouples each of the components of the BI process so that the semantic layer can function as a single service providing unified information to different outputs. This way, all analytics are only defined once and can be shared across multiple applications from a single data model.
To achieve this, the service uses various APIs that connect to all the platforms, so instead of connecting the tool directly to the data warehouse or lake, it extracts data from the headless BI tool.
Benefits of Using Headless BI
Besides the consistency and availability of analytics throughout platforms, headless BI also presents other benefits to SaaS companies. One of them is the one-time setup process in which metrics, models, security, and deployment are defined. This eliminates the need to replicate configurations multiple times in different tools, and the risk of doing it differently each time.
Another advantage is that it manages and audits access to a data source, helping with governance and security. Permissions and specifications about the people and type of accessible data only have to be established once, and it also allows for dynamic metric definitions, which are context-specific to certain users. This allows for better reporting depending on their needs.
Basic Requirements For Headless BI
To deploy a successful headless BI tool, it should have strong semantic capabilities like user-friendly language, real-time streaming, and multiple data source support. Additionally, it should be open to other BI tools, and its basic functions should be easily manageable by non-IT personnel. Finally, it should have enterprise capabilities to service complex customers as they grow their operations.
Developing a solution that complies with all of these requirements can be resource-intensive as it calls for a robust team. However, as most startups don’t have developers to spare, Verb can help in this regard by providing a no-code solution using a headless BI approach. With a one-time setup, our platform provides consistent metrics across all data experiences, and information can be easily shared with third-party BI tools through the use of APIs that guarantee cohesive reporting and analytics across a variety of outputs.
If you would like to request a demo or to have more information on how Verb can help with your analytic requirements, contact us.