Learn why implementing a metrics layer will make gathering data-driven insights much more robust in your organization!
Metrics layer is a framework that empowers organizations to unlock valuable insights and drive data-informed decision-making by consolidating, analyzing, and visualizing key performance indicators in a unified and intuitive manner.
In this article, we’ll explore the significance of the metrics layer, its benefits, key differences as compared to the semantics layer, and requirements for a successful implementation.
A metrics layer (also known as the metrics store or headless BI) is a framework for standardizing metrics, i.e., to centralize how a company calculates its metrics. It can be seen as the single source of truth when it comes to defining KPIs (or metrics, we will use those terms interchangeably) used within the organization.
💡 Bonus trivia: In case you were wondering, the term “headless BI” derives from the fact that these solutions enable various BI tools to connect to an API for accessing metrics. Consequently, they provide the flexibility to swap out tools while maintaining the integrity of metric definitions.
In essence, the concept of metrics layer is not entirely unfamiliar. For instance, you already store a project’s codebase in a central repository, versioned with Git. Similarly, the organization’s data warehouse or data lake serves as the single source of truth for all data. Analogously, the metrics layer functions as the single source of truth for the definitions of all KPIs used within the organization.
As illustrated in the schema below, the metrics layer should reside between the data warehouse (or the data source in a broader sense) and all the relevant applications (such as dashboards, reports, AI models, etc.) that consume these metrics.
This post originally appeared on TechToday.