.jpg)
Not having a single source of truth poses a range of risks for many organisations. Independent, siloed teams often assume they are operating under the same data, the same metrics. Not until they find out that certain metrics mean something different depending on who you ask in the team.
In this article, we’ll discuss how metric layers help organisations build a powerful single source of truth.
What a Single Source of Truth Really Means
Many organisations believe they already have a single source of truth because they store data in one warehouse. But storage is only one piece of the puzzle. A true single source of truth goes deeper. It covers not just where data lives, but how it is defined, transformed, and interpreted.
A real SSOT includes:
- Standardised, version-controlled definitions
- Validated data pipelines
- Role-based access and audit logs
The warehouse helps, of course. It centralises raw and modelled data. But without shared logic on top of it – a layer that defines what each metric means – teams will still end up with mismatched numbers.
This is where metric layers step in.
What Are Metric Layers
A metric layer is simply a central location where metric definitions live. Rather than having each team write its own SQL or create calculations inside a BI tool, the metric layer handles it once and shares it everywhere.
Basically, it serves as a dictionary for all of your organisation’s data. If someone asks, “What counts as an inactive account?” Instead of relying on interpretation, they can simply refer to one agreed-upon definition. This kind of clarity is the same principle taught in many of the best degrees in data analytics, where consistent logic and shared understanding form the basis of reliable insights.
‘Active users’, ‘revenue’, or ‘retention rate’ could all mean different things when their definitions aren’t aligned across the entire organisation. This slows down decision-making and often creates mistrust in data.
This is why metric layers have become so important. They act as a central home for definitions, ensuring that all the metrics mean the same thing wherever they appear. When linked properly to analytics tools, they create a stable backbone for accurate reporting and better decision-making.
Metric layers have grown in popularity for several reasons:
- More BI tools in use. Teams switch tools or use multiple platforms, making consistency harder.
- Growth of self-serve analytics. Non-technical users create dashboards, which leads to varied calculations.
- Faster decision cycles. Product and commercial teams rely on accurate daily or hourly metrics.
- Embedded analytics becoming standard. Customers now see internal metrics directly.
By placing metric definitions in one layer, rather than scattered through dashboards and code, you reduce drift, confusion, and the classic “Which number is correct?” debate.
How Metric Layers Connect to Stronger Analytics
To understand why metric layers matter, it helps to look at the flow of information:
- Warehouse or lakehouse stores the raw and modelled data.
- Metric layer defines how to turn that data into meaningful metrics.
- Analytics layer (dashboards, reports, or embedded tools) presents the metrics to users.
When these parts work together, teams trust the numbers and move with far more confidence. This has become increasingly important as organisations deepen their investment in data and AI.
Recent industry research shows that around 92.1% of organisations are now seeing clear, measurable gains from these initiatives, which means the quality and consistency of the underlying metrics directly influence the value they extract. With so much depending on accurate insight, the systems feeding those insights must be aligned.
Here’s a practical example.
Imagine your product team counts “active users” as anyone who opens the app in the last 90 days. Meanwhile, the finance team calculates it as a 30-day activity. Both numbers are valid. But they serve different purposes. If the definitions aren’t aligned, or at least documented and understood, you’ll see dashboards with conflicting values.
With a metric layer, the definition sits in one place. Both teams reference it automatically. And if a change is needed, it’s updated once and flows everywhere.
This reduces back-and-forth clarification, time wasted checking SQL logic, customer confusion in embedded dashboards, and errors caused by copying calculations between tools.
Why Embedded Analytics Raises the Stakes
Inside an organisation, mismatched metrics cause friction. Outside, they can damage trust.
More companies now provide analytics directly to customers. These insights often guide real business decisions. If your customer logs in and sees a retention number that doesn’t match their internal system, they’ll question the platform, not their own process.
The risks include higher support load, frustrated customers, cancelled contracts, and reputational damage. And because embedded dashboards surface metric definitions that once lived quietly inside SQL queries, any inconsistency becomes immediately visible.
A metric layer paired with embedded analytics ensures:
- Customers see reliable, transparent data
- Product teams avoid rewriting calculations across charts
- Updates to definitions roll out cleanly
- Every dashboard – internal or external – reflects the same truth
The Building Blocks of a True SSOT Architecture
A single source of truth isn’t a single tool. It’s a set of connected components working together. The strongest setups usually include the following.
1. A central warehouse or lakehouse
This holds all raw, cleaned, and enriched data. It ensures everyone starts from the same base layer.
2. A transformation layer
Tools like dbt standardise modelling, clean data, and apply business logic in a structured and tested way.
3. A metric or semantic layer
A metric or semantic layer serves as the glue that holds all models and dashboards. A metric layer defines:
- Calculations
- Filters
- Time windows
- Naming conventions
4. Analytics consumption tools
Dashboards, embedded analytics, APIs, and even spreadsheets all draw from the same definitions. This is where the value becomes visible.
5. Governance and documentation
Without ownership and clarity, even the best technical setup will fragment over time.
Together, these components create a stable and scalable foundation for reporting, product analytics, and customer-facing insights.
Best Practices for Linking Metric Layers to Analytics
Building a metric layer is only half the work. Maintaining it is just as important. The following practices help organisations keep their metrics consistent and useful.
This has become increasingly significant as more companies shift towards data-driven decision-making, a move that has helped boost operational productivity rates to around 63%. With productivity gains tied so closely to the quality of insights, keeping metric definitions aligned is essential.
Define metrics in code
Version control and testing provide transparency. And engineers can review changes before they reach dashboards.
Use a semantic layer when possible
It gives teams a shared language that matches business terms to data logic.
Avoid calculations inside BI tools
If a metric isn’t defined in the metric layer, it will drift. BI tools should read definitions, not create them.
Test everything
Schema tests, freshness checks, and metric tests reduce silent errors.
Document clearly
Documentation should always be clear. Every metric should have a description, a purpose, an owner, and the exact logic used.
Align across teams
Product, engineering, analytics, and commercial teams must agree on definitions. Transparency avoids confusion later.
Common Pitfalls and How to Avoid Them
Even well-intentioned teams make mistakes when implementing metric layers. Most of these issues come from rushing the process. Taking time to align definitions and governance upfront pays off in speed and clarity later.
The most common pitfalls include:
- Creating definitions in multiple places. This defeats the purpose of consistency.
- Not assigning ownership. Without a clear owner, changes become chaotic.
- Hard-coding logic in dashboards or application code. This creates shadow versions of the truth.
- Over-engineering the model. Start with core metrics and expand later.
- Skipping documentation. Assumptions lead to confusion, especially as teams grow.
How Embeddable Supports a Single Source of Truth
Many organisations struggle to connect their metric layer to the tools that surface analytics. Embeddable removes much of that friction.
The platform connects directly to warehouses and semantic layers, so dashboards always draw from the same definitions. There’s no need to recreate calculations inside charts. This ensures your internal team and your customers see the same numbers, calculated in the same way.
Key benefits of Embeddable include:
- Consistent metrics across every embedded dashboard
- Reduced maintenance effort for engineering teams
- Rapid development of analytics features
- Real-time updates when metric definitions change
Final Thoughts
A single source of truth isn’t just about storing data in one place. It’s about creating shared, reliable definitions that everyone – from engineers to customers – can depend on. Metric layers make this possible by centralising business logic and ensuring it flows consistently into every analytics tool.
This consistency matters. McKinsey’s research shows that companies that weave customer data analytics into their operational funnels can boost growth and increase profits by at least 50%. Results like these highlight how much impact clear, trustworthy metrics can have when they’re applied across a business. Without a unified foundation, it’s difficult to unlock gains on that scale.
When metric layers link smoothly to embedded dashboards and internal reporting, organisations make clearer decisions, move faster, and build products that customers trust.
References:
- NewVantage Partners. NewVantage Partners Releases 2022 Data and AI Executive Survey. Retrieved August 14, 2025, from https://www.businesswire.com/news/home/20220103005036/en/NewVantage-Partners-Releases-2022-Data-And-AI-Executive-Survey
- Edge Delta. Eye-Opening Data Analytics Statistics for 2024. Retrieved August 14, 2025, from https://edgedelta.com/company/blog/data-analytics-statistics
- McKinsey & Company. Five facts: How customer analytics boosts corporate performance. Retrieved August 14, 2025, from https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance
