Study why implementing a metrics layer will make gathering data-driven insights rather more strong in your group!
Metrics layer is a framework that empowers organizations to unlock priceless insights and drive data-informed decision-making by consolidating, analyzing, and visualizing key efficiency indicators in a unified and intuitive method.
On this article, we’ll discover the importance of the metrics layer, its advantages, key variations as in comparison with the semantics layer, and necessities for a profitable implementation.
A metrics layer (often known as the metrics retailer or headless BI) is a framework for standardizing metrics, i.e., to centralize how an organization calculates its metrics. It may be seen as the one supply of fact in terms of defining KPIs (or metrics, we’ll use these phrases interchangeably) used inside the group.
💡 Bonus trivia: In case you had been questioning, the time period “headless BI” derives from the truth that these options allow numerous BI instruments to hook up with an API for accessing metrics. Consequently, they supply the flexibleness to swap out instruments whereas sustaining the integrity of metric definitions.
In essence, the idea of metrics layer just isn’t fully unfamiliar. For example, you already retailer a challenge’s codebase in a central repository, versioned with Git. Equally, the group’s information warehouse or information lake serves as the one supply of fact for all information. Analogously, the metrics layer features as the one supply of fact for the definitions of all KPIs used inside the group.
As illustrated within the schema beneath, the metrics layer ought to reside between the info warehouse (or the info supply in a broader sense) and all of the related purposes (similar to dashboards, experiences, AI fashions, and many others.) that devour these metrics.
