Abstract image showing different metric layer code for three audiences: marketing, finance, and growth.
Data Talks

The metric layer: Why you need it, examples, and how it fits into the modern data stack

Guest Author
Guest Author

A metric layer is a centralized repository for key business metric. This “layer” sits between an organization’s data storage and compute layer and downstream tools where metric logic lives—like downstream business intelligence tools.

A metric layer is a semantic layer where data teams can centrally define and store business metrics (or key performance indicators) in code. It then becomes a source of truth for metric—which means people who analyze data in downstream tools like Hex, Mode, or Tableau will all be working with the same metric logic in their analyses.

The metric layer is a relatively new concept in the modern data stack, mainly because until recently, it was only available to companies with large or sophisticated data teams. Now it is more readily available to all organizations with metric platforms like Transform.

In this article, you’ll learn what a metric layer is, how to use your data warehouse as a data source for the metric layer, and how to get value from this central metric repository by consuming metrics in downstream tools.

How a Metric Layer fits into a Modern Data Stack

The modern data stack is composed of a number of elements organized in the order of how data flows:

  • Managed ETL (or ELT) pipeline that ingests data from a variety of data sources
  • Data storage solution in the form of a data warehouse or data lake on-premise or in the cloud
  • Data transformation pipeline that processes stored data using languages like SQL and YAML for downstream business operations, analytics, and data science solutions
  • BI or data visualization platform
  • Data governance framework
  • Metric layer / metric store
Diagram showing modern data stack, including metric stores, reverse ETL, Data Integration tools, etc.
Read Continual's blog post: Modern Data Stack Ecosystem - Spring 2022 Edition

One central benefit of a metric layer is that it sits between the data warehouse and downstream analytics tools. People can access metrics in business intelligence (BI) tools like Tableau, Mode, and Hex, bringing metrics consistency across all business analysis.

Diagram showing today's architecture and how it would change with the metrics layer in between the data warehouse and downstream tools.
Image source: Benn Stancil

Use cases for the Metric Layer

The formulation and implementation of metric layers was pioneered by prominent tech companies like Airbnb, Spotify, Slack, and Uber. Airbnb designed a metric layer called Minerva to serve as a single source of truth (SSOT) metric platform. They did this by standardizing the way metrics are created, calculated, served, and used across the organization.

Uber built uMetric, a standardized metric platform that underlies the entire lifecycle of a metric from definition, discovery, planning, calculation, quality, and consumption. These pillars not only enable rapid metric computation for business decisions, but also help create useful features for training ML models and promoting data democratization.

A new component in the Modern Data Stack

With the emergence of big data, predictive analytics, and data science, most companies have access to enormous amounts of valuable data. Many organizations have evolved their data stack to simplify computation, transformation, and access to key business metrics, which can accelerate data-driven decision-making.

However, as Benn Stancil noted in his popular Substack blog, there was no central repository for defining metrics. This causes confusion and misalignment across an organization.

The core problem is that there’s no central repository for defining a metric. Without that, metric formulas are scattered across tools, buried in hidden dashboards, and recreated, rewritten, and reused with no oversight or guidance.
—Benn Stancil, The missing piece of the modern data stack

Another common issue is “dashboard sprawl” where metric logic is spread across different tools and data artifacts. Since this logic is different for every tool, teams often end up with different numbers for the same metrics and no one knows where to find the “correct” metric to answer their most important business questions.

This problem led to the metric layer becoming a new artifact in the modern data stack. With a single shared store of metrics definitions and values, the metric layer ensures consistent and accurate analysis and reporting of metrics.

A metric layer not only centralizes key business data but also helps improve the efficiency of data teams by removing the need for repeated analytics. This helps data stakeholders become key advocates and enablers of data-driven decision-making and data democratization across the entire organization.

Reutilization of metrics in diverse contexts and external tools

One of the benefits of having a single metrics repository is that it can be connected to a variety of tools; for example, CRM’s, BI tools, tools developed in-house, as well as data quality and experimentation tools.

A centralized architecture ensures that no matter how a tool’s internal logic is configured, the end result will be based on the same metric logic and consistent across tools and applications. For instance, MetricFlow, the metric layer behind Transform, has an API that enables users to express requests for their Transform metrics directly within SQL expressions.

Core metrics like Net Promoter Score (NPS), Monthly Recurring Revenue (MRR), Customer Acquisition Cost (CAC), loan-to-value (LTV), and Annual Recurring Revenue (ARR) capture the health of the business and need to be accurate for reporting and decision-making. With a metric layer, it’s possible to see the lineage of each metric, how it’s built, what the data source is, and how it’s consumed. By unifying metrics extraction and data analytics on these metrics, the metric layer provides the much-needed consistency that is lacking in modern data stacks.

Enhancing transparency between technical and non-technical teams with a single interface
A single interface for metrics information gives data stakeholders across an organization—in development, sales, marketing, and more—to have the same view and understanding of key metrics to track goals. This consistency allows all of these teams to speak the same language regardless of the tools they use to compute the metrics. This is a tremendous benefit of a metric layer and promotes stronger data democratization and governance across the entire organization.

Transform is unique in that it has the addition of a metrics catalog on top of MetricFlow, its open source metric layer. The metrics catalog is a central location where both data teams and non-technical users can interact with, build context, collaborate on, and share key metrics.

Tracking changes is easier
Because businesses are constantly evolving and creating new metrics or changing the definition of existing metrics, each data stakeholder has to manually keep track of changes in a data warehouse to update their metrics definition and logic.

However, with the combination of a metric layer and a metrics catalog, tracking changes metrics owners are alerted anytime the lineage or definition of a metric changes. This enables data stakeholders to make better sense of data, especially when a new metric definition leads to anomalous or unexpected results.

Dig into the Metric Layer

A metric layer reduces the problem of disparate results when the same metric is computed by different teams using a wide variety of BI tools. And it makes data-driven analytics more precise and promotes faster and more accurate decision-making.

If you’re looking for a streamlined and centralized metric layer, MetricFlow is now open source. You can explore the project on Github. Find more information about Transform’s metric layer and its benefits in the product documentation.

This post is guest authored by Dr. Sundeep Teki. Dr. Sundeep Teki is a leader in AI and neuroscience with professional experience in the US, UK, India, and France. He has published 40+ papers; built and deployed AI for consumer tech products like Amazon Alexa; advises and consults tech startups on AI/ML, product, and strategy; and coaches data and AI professionals and executives.