How does the metrics layer enhance the power of advanced analytics?

Amid all the buzz around advanced AI-powered data analytics, one crucial component often goes unnoticed: the metrics layer. This is where metrics creation takes place, the process that defines and manages the metrics that turn data signals into actionable, meaningful insights.

Although it’s increasingly vital for effective analytics, metric creation often does not receive enough attention in the broader business intelligence (BI) package, and enterprises frequently fail to understand its role.

What is the metrics layer?

Metrics turn a concept into something that can be measured. They provide the framework upon which stakeholders can track changes in whatever they want to track. Until raw data signals are converted into metrics, there’s no way to measure improvements or degradation, and no way to identify patterns or trends.

The metrics layer, metrics creation, metrics store, metrics platform or headless BI are all different terms for creating, managing, defining, enforcing and delivering metrics. The bundle of best practices, features and tools resides between the data source and the apps that use the data and deliver insights — hence the term “metrics layer.”

A metrics layer:

Serves as a single source of truth for metrics across all your dashboards, reports, applications and more.

Holds information about how to calculate metrics and the attributes that should be used to evaluate KPIs, like data repositories do for data and GitHub does for code.

Translates requests for metrics into SQL queries, executes the requests and then returns the metrics to the users.

Defines key metrics, explains what the data represents, such as whether an increase is favorable or negative, and shows how metrics relate to each other.

According to Gartner, which was one of the first entities to use the phrase, metrics creation is a use case that “Enables organizations to connect to data, prepare data and define standardized metrics that can be shared throughout the organization.”

“A metrics layer allows an organization to standardize its metrics and how they are calculated. It builds a single source of truth for all metric or KPI definitions for all data sources in the organization,” explains Christina Obry, a product manager at Tableau.

Is a metrics layer essential for BI success?

Metrics creation is so critical that Gartner considers it a mandatory component for any BI platform. Without a strong metrics layer, BI platforms struggle to deliver useful business intelligence.

There’s simply too much data flooding into enterprises, but also, there are too many tools measuring and analyzing that data, resulting in inconsistent metrics. Even simple metrics can become muddled, with tools disagreeing about how to measure them.

Avi Perez, CTO and co-founder of Pyramid Analytics, says that “Mature organizations understand the need for a protocol that ensures formulas are calculated consistently, maximizing their usefulness to users across departments. They don’t promote self-service at the expense of a single source of truth, and they seek out mechanisms for standardizing metrics.”

Data only has value when it’s transmuted into insights, but those insights need to reach the right decision-makers together with the right context. A metrics layer enables the creation of a universal glossary of metrics that every business stakeholder can utilise to inform sound decisions.

The dangers of operating without a metrics layer

Imagine counting the number of active users for an app. Should they be measured weekly, monthly or annually? How long can users go between logins before they are no longer considered “active” users? What’s the best way to segment them geographically?

The gaps in how these questions are answered lead to wasted time, a loss of trust in the data and widespread confusion. Without a universally managed metrics layer, departments can become misaligned and measure the same metric differently. In an era of data-driven business decision-making, muddled or inconsistent data can lead to damagingly erroneous decisions.

Fixing these inconsistencies can be a nightmare. First, you have to find them all, scattered across all your data sources, analysis tools and custom queries. As they are reused without oversight, the inconsistencies grow. Changing the business logic definition for every tool, every department leader, every time, means that data teams waste time firefighting instead of working on tasks that deliver value.

“Your organization has multiple dashboards. It may have multiple BI tools, too. Do you really want to define the business logic for your metrics every single time in each of those outlets? What if the logic changes as the business grows? That increases the chances of one instance being slightly off or out of date by the time someone looks at it and makes a decision,” warns Chris Nguyen, a BI analyst at Keller Williams Realty International.

A centralized metrics layer is a way to define and store metrics in a single place, so that everyone in your organization uses the same logic, every time.

What are the benefits of a metrics layer?

Metrics creation delivers value that goes beyond the critical need for consistent metrics. By setting up a centralized repository for business metrics and KPIs, organizations enjoy numerous benefits:

More trust in data, thanks to consistency in the metrics used across the organization

Improved accessibility to vital metrics for line-of-business users who aren’t data experts

Increased scalability for business logic across the company

Shorter time to insights and real-time updates

Greater adaptability to changing business needs

IT consultant Sean Michael Kerner emphasizes that “metrics stores provide a consistent way for organizations to use and reuse metrics definitions and calculations across different data tools and teams.” Everyone can inspect metrics definitions at will, helping improve transparency and trust in data.

Integrating centralized metrics management with modern data architecture makes it easy to update definitions as business requirements evolve and then propagate them across the organization. This improves both scalability and collaboration, as the whole organization speaks the same data “language” without gaps or misunderstandings.

Metrics stores are built to integrate natively with open APIs, making it possible to surface metrics in the workflows and apps where LOB users need them most. Moreover, headless BI infrastructure enables real-time and near real-time updates, helping to keep decision-making relevant and informed.

A metrics layer is also a boon for software engineers. Because it translates metric definitions into code, it helps tech teams follow established best practices, such as version control, tracking and the DRY (don’t repeat yourself) principle. This increases efficiency and reduces repetitive work.

Metrics creation is advanced analytics’ crucial ingredient

Robust metrics creation is the glue that holds true advanced BI solutions together. Without this use case, data would languish unused, metrics would diverge across the organization, teams would struggle to coordinate and insights would arrive too late or not at all.

FAQs:

What is a metrics layer?

A metrics layer is a centralized data modeling layer that defines consistent business metrics across different tools and teams. It ensures everyone uses the same logic and calculations for analysis and reporting.

Why does a metrics layer matter for business analytics?

A metrics layer ensures consistency and accuracy in business analytics by standardizing metric definitions, reducing errors and enabling faster, relevant insights across teams. Without metrics creation, data would become confused and trust would drop.

What are the business benefits of a metrics layer?

The business benefits of a metrics layer include:

Consistent and accurate metrics across tools

Faster decision-making with trusted data

Minimal manual errors and duplicated logic

Improved collaboration between data and business teams

What are the use cases for a metrics layer in enterprise analytics?

Use cases for a metrics store in enterprise analytics include:

Different teams can collaboratively define, refine and use metrics

Consistent, real-time metrics for executive dashboards and operations

Uniform financial metrics for accurate reporting and forecasting

Centralized customer, product and HR metrics for deeper analysis

Headless commerce and supply chain optimization with API-driven, consistent metrics

A single source of truth for metrics used in regulatory compliance reporting and internal audits

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