Reliable Decisions Start with Trusted Metrics

Why Metric Governance Matters

Experimentation is about understanding how the changes we introduce impact users and the business. In other words, it is about learning what works and what does not. 

Product teams often focus on experiment design, statistical methods, or result interpretation. These are all important. But one foundational element is frequently overlooked: the way metrics are defined, managed, and used across experiments and across the organisation.

Metrics are more than numbers. They are the lenses through which teams interpret outcomes and make decisions. When those lenses are clear, consistent, and trusted, experiments lead to reliable decisions. When they are not, the same data can support multiple and often conflicting conclusions.

You won’t be able to make trusted decisions unless you can trust the metrics which infer those decisions.

In this post, I will explain what metric governance is, why it matters for reliable experimentation, and how to build a practical governance practice that supports confident and reliable decision making.

What is Metric Governance

Metric governance is the set of practices, rules, and ownership that ensure the metrics used in experimentation are trustworthy, consistent, discoverable, and fit for decision making.

Most teams already manage data quality and data access. Metric governance is narrower and more specific. It focuses on the meaning, definition, purpose, and usage of metrics in the context of controlled experiments. It goes beyond simply tracking data and ensures that everyone uses the same definitions, understands why a metric exists, and knows when it should or should not influence decisions.

In an experimentation context, metrics typically play different roles. Some metrics are primary decision-making criteria. Others are secondary and provide additional context. Guardrail metrics help detect negative side effects, while exploratory metrics can surface new signals without driving the final decision.

Metric governance ensures that these roles are explicit and consistently applied.

The Hidden Cost of Poor Metric Governance

When metrics are not governed well, the costs show up quickly.

Teams may use slightly different definitions of what appears to be the same metric. Revenue, conversion, or engagement can mean different things depending on who defined them and when. These differences may seem small, but they accumulate and make experiment results difficult to compare.

Decision makers may hesitate to act because they are unsure what was actually measured. Even well-designed experiments can fail to drive action if the underlying metrics are unclear or contested.

Over time, this ambiguity erodes trust in experimentation, and evaluating results turns into endless debates about what a given outcome actually means. When teams do not fully trust experiment results, experimentation stops being a decision tool and becomes a reporting exercise.

Why Metric Governance Matters at Scale

In early experimentation programs, informal agreements about metrics often work. A small group of people can align quickly and resolve differences through conversation. As experimentation scales, this might no longer be enough.

More teams start running experiments in parallel. Metrics are reused across products, domains, and teams. Leadership wants to compare outcomes and understand overall impact. Without consistent metric definitions, those comparisons become unreliable.

At scale, experiments also carry more risk. Decisions based on incorrect or misunderstood metrics can affect customers, revenue, or compliance. Metric governance becomes a critical part of managing that risk while still moving forward at pace.

Core Pillars of Effective Metric Governance

Clear and Shared Metric Definitions

Every metric should have a single, shared definition that captures its business meaning and how it is computed. This includes the unit of analysis, aggregation logic, and any important inclusion or exclusion rules.

Clear definitions prevent the same metric from being implemented differently across teams or tools and ensure that experiment results are interpreted consistently.

Ownership and Accountability

Metrics need owners. A metric owner is responsible for maintaining the definition, ensuring accuracy, and answering questions about appropriate usage. Ownership creates accountability and makes it clear who is responsible when a metric needs to evolve.

Metric ownership does not mean exclusive control. Many teams may use the same metric, but someone needs to steward it.

Metric Lifecycle Management

Metrics evolve over time. Governance should define a clear lifecycle, from draft to active use and eventually to deprecation or archival. Changes to active metrics should be deliberate and visible.

Lifecycle management helps teams understand which metrics are recommended, which are no longer valid, and how changes affect historical experiment results.

Discoverability and Context

A governed metric should be easy to find and easy to understand. Descriptions, metadata, tags, and usage examples provide essential context.

Seeing how a metric has been used in past experiments helps teams decide whether it is appropriate for their own hypotheses and decisions.

Metric Governance and Decision Quality

The goal of metric governance is not process compliance. It is better decisions.

When teams use well-defined and consistently governed metrics, they can link hypotheses to outcomes more clearly. Results are easier to explain, defend, and act on.

Governance also reduces the temptation to focus on metrics that look good after the fact but do not align with the original decision criteria. It supports clearer ship or stop decisions and helps teams move forward with confidence.

How Experimentation Platforms Can Help

Experimentation platforms can make metric governance practical by embedding it directly into workflows.

Metric catalogs, ownership models, versioning, and usage tracking help teams understand and trust the metrics they use. Consistent computation across experiments reduces ambiguity and supports fair comparisons.

Platforms can also surface guardrails and alerts that help teams detect unintended effects early.

Getting Started with Metric Governance

A good place to start is with the metrics that matter most for decisions. Focus on getting their definitions right, assigning owners, and documenting them clearly.

Introduce governance incrementally. As teams experience the benefits of clarity and trust, it becomes easier to extend governance to additional metrics.

The success of metric governance should be measured in decision confidence, not in the number of rules.

Conclusion

Metrics are the foundation of experimentation. When they are clearly defined, well-managed, and consistently used, experiments produce reliable evidence and better decisions.

Metric governance is not about control. It is about enabling teams to learn faster and decide with confidence as experimentation scales.