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Introduction to Metrics Layer in Tableau

The insights that data offers are what really make it valuable. The challenge lies in giving insights to those who require them to make wise business decisions in the appropriate business setting. A metrics layer is used in this situation. A metrics layer provides analysts with the capacity to create an organizationally-wide business lexicon of metrics or KPI definitions, which aids in the delivery of insights that are relevant and timely. 

The Metrics Layer, a crucial part of Tableau Pulse driven by Tableau AI, allows analysts to provide meaningful new experiences to their stakeholders and data into every part of the organisation. To learn more, check out the Tableau online course.

What is a metrics layer?

An organisation can standardise its measurements and calculation methods with the help of a metrics layer. It creates a single source of truth for any KPI or metric definition across all organisational data sources. Business users’ consumption tools are powered by a metrics layer that resides on top of the data sources. The layer of metrics defines:

  • The key metrics that users find most important
  • What the data indicates, including technical details like whether a rise in the data is positive or not
  • The relationship between metrics 

The Metrics Layer in Tableau Pulse is capable of comprehending the business semantics that underlie the queries that users pose. Consequently, this enables the Tableau Pulse Insight platform to more effectively respond to the “what, why, and so-what” questions made by business users when asking for specific metrics. 

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Introduction to Metrics Layer in Tableau

Tableau Pulse Metrics Layer

A metrics layer must be scalable, secure, and adaptable to be successful. To guarantee that users only access data they are permitted to view, Tableau Pulse makes use of the publicly available data sources on the Tableau platform. In addition, Tableau Pulse divides metrics and metric definitions in the Metrics Layer.

Data analysts set up and manage metrics definitions in Tableau’s site-wide Metrics Layer. They are made up of a collection of metadata, such as the following, that define the metric’s meaning and business context:

  • Name of metric
  • Measurement, aggregation, and computations if the measure is not yet present in the data source
  • For the metric to have meaning, fixed definitional filters are necessary, such as Status = Open. 
  • a temporal dimension for orientation and time comparison 
  • a group of connected dimensions that give business users the ability to analyse the measure in a meaningful way and give the insight platform something to work with when producing insights.
  • Extra metadata, like whether a rise in the metric would be advantageous or disadvantageous

As a single source of truth, Tableau Pulse makes sure that a collection of metadata only has one metric definition in the system.

Metrics incorporate a time and filter context and are placed on top of a metric or KPI description. Any person who has access to the data source can begin tracking the measure by choosing the relevant dimensions and time period and then filtering results from those dimensions. This implies that a metric definition can have other metrics on top of it, making them extremely configurable.  

The Tableau Pulse Metrics Layer is scalable, meaning that any modifications to the measure or KPI definition only need to be changed once, and all metrics associated with that definition will automatically benefit from that change.

Considerations when creating a metric layer and KPI definitions

The most important key performance indicators (KPIs) for your users should be in the Metrics Layer. Think about developing metric definitions for the data that business users would typically utilise.

In order to make sure the statistic is pertinent within a comparative time frame, Tableau Pulse Metrics relies on a time context, which includes time filters like month-to-date or week-to-date. Context and insights on how a metric is trending can be obtained through temporal comparison. Make sure that the data source has an appropriate time dimension before defining a metric. 

Introduction to Metrics Layer in Tableau

Tableau Pulse offers metric definition bootstrapping capabilities straight from the Data Guide, allowing users to capitalise on investments in already-existing Tableau dashboards.

How the Tableau Pulse Metric Layer helps analysts empower their organisation

By means of interactive dashboards, self-service analytics, and data-driven decision-making, analysts have demonstrated to enterprises the significance of data in enhancing business results. Even with dashboards provided by analysts, around 70% of business users today still do not use data for decision-making. 

These users would greatly benefit from monitoring their KPIs because they are accountable for a successful business outcome. Additionally, they want to show off how they use data to guide their decisions. However, when data is locked in programs they don’t often use, people have trouble finding and accessing it. Even when they do get access to the data, they frequently find it difficult to comprehend. Furthermore, even if they comprehend the facts, they might be unable to act upon them due to a lack of knowledge. Finally, considering all the many dashboard versions, they question whether they can trust the data they are viewing. 

By reinventing their data experience (built on the Tableau platform), Tableau Pulse tackles these difficulties for business users by making it smart, contextual, and personalised. 

To help them find new possibilities, anticipate problems, and make better decisions, business users receive a tailored collection of data and insights. Tableau Pulse integrates data into their workflow to facilitate seamless access, sharing, and collaboration on data. When and where they need it, users may access trustworthy and relevant information to support rapid, data-driven decision-making. Additionally, Tableau Pulse can automatically recognize and generate insights, summarise data, and enable on-demand exploration with generative AI throughout the experience, enabling businesses to deliver analytics at scale.

But before analysts fill out the Metrics Layer, none of this is feasible. Tableau Pulse may respond to frequently asked questions by business users about data and proactively surface responses in plain language to address the “what, why, and so-what.” This is made possible by the business semantics in the Metrics Layer.


Analysts may be the data hero who powers the next business user experience and spreads data throughout the entire company with Tableau Pulse as their superpower. Check out the Tableau training online to learn more.

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