Trusted Metrics: Why KPI Drift Breaks Confidence in Data Platforms

In the previous article, I wrote about Semantic Views as a practical way to turn business definitions into reusable platform objects.

Now let’s move one level higher.

Because defining metrics is only the first step.

The real challenge is making sure the organization trusts and reuses them on a daily basis.

That is where Trusted Metrics come in.

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The problem is not always bad data

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When business users lose trust in reporting, the first assumption is often: “the data must be wrong.”

Sometimes it is.

But very often, the data is technically correct. The issue is that different teams calculate or interpret the same metric differently.

Sales has one definition of revenue, Finance has another, and Marketing’s customer segmentation rarely matches either. Operations, meanwhile, applies its own date logic entirely.

The dashboard looks fine on their own, but the meaning behind the number is not consistent, and when numbers are compared cross-departments issues arise.

This is what I call KPI drift.

It happens when business-critical metrics start moving away from their original, agreed definition because logic is copied, adjusted, filtered, or rebuilt across multiple tools and teams.

At first, the differences are small or even tiny, so nobody really notices something is wrong.

Over time, they become a trust problem, and trust is hard to win back.

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What makes a metric trusted?

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A trusted metric is a governed business object.

It should have:

• One approved definition: business one using business language

• A clearly named business owner

• A clearly named technical owner

• Documented calculation logic – technical definition of how we arrive at that number

• Approved filters and dimensions

• Known source data

• Defined usage context

• Visibility into where it is used

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This is where semantic governance becomes practical.

With Snowflake Semantic Views, teams can define business metrics, model entities, and describe relationships directly in the platform. Instead of leaving business logic scattered across dashboards, SQL queries, or documentation, Semantic Views help make that logic reusable and consistently available across different consumption layers.

In the context of Cortex Analyst, Semantic Views become especially important because they provide the business-friendly structure AI needs: logical tables, dimensions, facts, metrics, and relationships. So properly built semantic definition, not only ensure business uses common definition, but also AI is based on the same definition. This is the way to ensure AI is reasoning the same way as business does.

That matters because a metric is only trusted when people know not only what it returns, but also why it returns that number.

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The measurable goal: reduce KPI drift

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Governance becomes much easier to discuss when it is measurable.

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For trusted metrics, I would start with a few simple target KPIs:

1 approved definition per critical KPI
There should not be five official versions of revenue, margin, churn, or active customer. Better to spend time building solid single definition at the start, then to re-earn trust for metrics later.

0 ambiguity for official reporting
When a metric is used in executive, financial, operational, or customer-facing reporting, teams should know which definition applies, and it should be consistent across the board.  

80% reuse of governed metric definitions in core reporting
Most recurring dashboards, management reports, and operational analytics should reuse approved definitions instead of recreating logic locally.

100% governed definitions for business-critical AI questions
If AI is helping answer questions that influence business decisions, it should work from governed metric definitions, not raw interpretation of tables.

Named ownership: Each critical KPI should have one business owner and one technical owner.

These numbers are not universal benchmarks. They are practical operating targets.

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Why this matters even more for AI?

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AI makes KPI drift more visible, and more dangerous.

A human analyst may notice that two dashboards calculate revenue differently. An AI assistant may simply produce an answer based on whichever logic it can infer.

That answer may be syntactically correct, but business-wise misleading.  

This is also where Horizon Context becomes relevant: it gives AI agents and business-facing experiences a way to work from trusted semantic definitions instead of trying to infer meaning directly from raw schemas.

This is the important shift:

AI increases the need for metric governance.

If organizations want natural language analytics, agents, and AI-assisted workflows to be useful, they need a trusted metric layer underneath. Otherwise, AI will scale not only insight generation, but also ambiguity and discrepancies.

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What to watch out for?

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Trusted metrics are not created by declaring a “single source of truth” in a slide deck.

They require operating discipline.

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A few questions matter:

• Who can create a new official metric, and who approves changes to its logic?
• How are deprecated definitions removed, and how do we stop duplicates from reappearing?
• How do we monitor where a metric is reused, and how do we explain a change to the business users who rely on it?

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The hardest part is usually not technical implementation.

It is agreement.

Data teams can define the logic, but the business must own the meaning and stand behind it consistently.

That is why trusted metrics should sit at the intersection of business ownership, data engineering, analytics, governance, and platform design.

The thesis I would put forward is simple:

Ownership is what makes a KPI trusted. Without it, a KPI is just a calculation.

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My Perspective

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Trust is everything in data.

Not having governed KPI definitions leads to discrepancies and, eventually, loss of trust. This is one of the most dangerous things that can happen, because rebuilding trust is a long process.

If you build a trustworthy, managed semantic layer from the beginning, you also build trust in the data and in how it is calculated. With clearly defined ownership and definitions, business users know from the start what they are working with. They can focus on actual data analysis instead of wondering whether the underlying data is correct or whether the metric itself is reliable.

This also allows us to align AI around the same business definitions, creating better consistency with existing metrics and data. Business users do not need to “warm up” AI with additional context, because AI is already aware of the context it is expected to work with.

In the next article, I will look at AI and agentic workflows : why AI systems need governed business context, not just access to data, and how semantic foundations can improve the reliability of AI-powered analytics.

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