Metric Trees, Layers and Catalogs serve different purposes. While Metric Catalogs and Layers help define, manage, and distribute metrics, Metric Trees focus on driving business strategy and operations with analytical rigor.
Let’s break it down:
Metric Catalog
- A Metric Catalog is essentially a list of metrics and their associated metadata. It serves as a central hub for data teams to maintain, discover, and utilize metric definitions for repeated use.
- Its main strength lies in organizing, searching, and maintaining metric definitions efficiently, providing clarity and boosting operational velocity.
Metric or Semantic Layer
- Metric Layers define metrics centrally in code, offering a flexible and powerful serving layer.
- Built primarily by data platform teams, they ensure consistent, scalable serving of metrics to downstream tools and consumption modes.
- Metric Layers go beyond catalogs in that they are directly responsible for nuanced, performant calculations and serving of metrics.
Metric Trees
- Metric Trees are an advanced framework that models input and output metrics, capturing business equations or processes. These go beyond Metric Layers by modeling the relationships between metrics, pushing data closest to the underlying business model.
- Ideal for both data and business teams, Metric Trees enable analytics operations. They help align the organization on key metrics and drivers. Automated algorithms built on the metric tree structure can drive and enhance business operations.
In short, while Metric Catalogs and Metric Layers focus on standardizing and making metrics accessible, Metric Trees offer a structured, visual representation of how business processes and metrics interconnect.
This approach brings organizations closer to the ultimate goal of operating with rigor.
