Matt Aslett's Analyst Perspectives

In an earlier Analyst Perspective, I discussed data democratization’s role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery, data-driven organizations provide more workers with the ability to analyze and use data. I’ve also examined how generative artificial intelligence (GenAI) could revolutionize business intelligence software by using natural language interfaces to lower the barriers to working with analytics software. Today, however, data democratization ensures that access is not limited to analytics software. Users in different roles should be able to use data through whatever applications or tools best align with their business workflow and objectives. This is the driver behind growing interest in a new category of products that enable headless BI.

At first glance, “headless BI” seems like an oxymoron. We tend to think of BI software as providing an interface that enables analysts and decision-makers to create and access reports and dashboards with data, charts and narratives. However, there is more to BI software than the interface. Under the cover of BI products are semantic modeling and data processing capabilities that define and generate the metrics presented in the data, charts and narratives. Headless BI is an architectural approach that separates the presentation layer from the metrics layer, presenting defined and generated metrics through a variety of interfaces, including reports and dashboards but also operational applications and productivity tools.

I sometimes see the terms headless BI and metrics layer used interchangeably. However, there is a subtle but important distinction. Headless BI is the architectural approach to separating the generation of business metrics from the user interface. The metrics layer (or metrics store) is the functionality that enables this architectural approach by providing the capabilities to calculate and process business metrics and present them to users through a variety of tools and applications.

Headless BI expands access to data by facilitating consumption by workers who are not BI software users. It has the potential to alleviate some of the challenges associated with traditional BI products. Almost all participants in ISG’s Analytics and Data Benchmark Research stated that reports and dashboards are critical to their organization, with 97% rating reports as important or very important and 96% saying the same for dashboards. Importance does not always equate to satisfaction, however. Only one-third (33%) of participants declared themselves fully satisfied with their organization's analytics and BI technology.

Ironically, traditional approaches to BI software may be contributing to a lack of satisfaction. In theory, semantic modeling provides an agreed definition of the business logic used across enterprise analytics and data initiatives. This includes the raw data generated by enterprise applications and metrics derived from that raw data. However, the tight coupling of the presentation and metrics layers in traditional BI products has contributed to multiple fragmented semantic models tied to specific BI products and projects. The more reports and dashboards an enterprise has, the greater the risk of disagreements about data definitions and accuracy. As I have previously noted, this can lead to ostensibly simple questions such as, “How many customers does the organization have?” being fiendishly difficult to answer, especially for enterprises with multiple business entities, regions, departments and applications. Even the best data integration and management approaches will fail to address this challenge if the reports and dashboards used to make decisions have conflicting definitions and metrics. Adopting a separate metrics layer as part of a headless BI architecture can enable the creation of a single semantic layer that can be utilized by a variety of applications and BI products.

In many enterprises, much of the work to create agreed business definitions has already been done, either directly through data governance initiatives or indirectly through the standardization of business processes. Rather than reinventing the wheel, a metrics layer should enable enterprises to extract definitions from existing applications and platforms to form the basis of the semantic model. While metrics layers facilitate data democratization, access to data should not be a free-for-all. Access control ensures that metrics are available to appropriate individual users, tools and applications. It is also important that metrics are up-to-date. Given that the volume of metrics and the number of concurrent users are potentially very high, caching can ensure consistency and alleviate the performance impact on the underlying process engine.

I also see alignment between the goals of headless BI and data as a product, which applies product thinking to data initiatives, ensuring that data can be shared and reused across the business for multiple use cases. I assert that by 2027, more than 3 in 5 enterprises will adopt technologies to facilitate the delivery of data as a product, adapting cultural and organizational approaches to domain-based data ownership. The combination of domain-oriented ownership and the application of product thinking encourages data owners to be aware of and responsive to data requirements from across the enterprise. A universal metrics layer can support this goal by defining a common semantic model that considers and manages metrics as a data product and facilitates data sharing between business domains.

Separating the metrics layer from the BI interface also facilitates standardization on agreed metrics used to support multiple workloads, including GenAI and agentic AI applications, as well as BI use cases. Finally, if the generated metrics will be presented in a variety of reports, dashboards, applications and tools, then open application programming interfaces are essential to ensure that enterprises can use a variety of consumption layer products as required for users in different roles, locations and requirements.

Headless BI is an important aspect of data democratization, alongside self-service data discovery and GenAI interfaces. Each has a role in facilitating and accelerating data-driven decision-making across an enterprise. Metrics layer products that enable the adoption of a headless BI architecture are still in the early stages of maturity. Nevertheless, I recommend that all enterprises evaluating BI products with a view to facilitating data-driven decision-making evaluate the potential benefits of headless BI as an architectural approach, as well as metrics layer products that deliver it.

Regards,

Matt Aslett