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Re-Upload - Metrics in Tableau Next Part 4 of 6 [Reposted]

I passionately hate that these are called metrics when there's an identical product called Tableau Pulse that works better.

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  • Metrics should ideally be defined at the semantic level before building visualisations, since they can be reused across visualisations and other assets
  • Tableau Next 'metrics' are effectively a re-implementation of Tableau Pulse, which causes naming confusion given the older deprecated Tableau metrics feature
  • When defining a metric you set a measure, optional filters, a time dimension (e.g. order date), additional dimensions users can filter, and record-level detail fields for drill-down and insights
  • The filter builder is currently flawed because it lets you type values that may never match your data, with no validation against the underlying column
  • To surface a metric on a dashboard you create the space first, then add the asset by selecting the semantic model and choosing the metric; caching issues may require a page refresh to see newly created metrics

This video shows how to define a metric at the semantic level in Tableau Next, and argues that this feature is really Tableau Pulse rebuilt under a confusing new name. Defining metrics before building visualisations means they can be reused across dashboards and other assets rather than rebuilt each time.

Working in a Tableau Next workspace on a superstore data model, having already built some visualisations without first defining any metrics.

The Breakdown
  1. Define metrics before visualisations 0:00

    You can define a metric at the semantic level and reuse it across visualisations and other assets, so it's better to set metrics up before you start building charts rather than after, as was done here.

  2. Metrics is really Tableau Pulse, renamed 0:39

    Tableau's old deprecated metrics feature was superseded by Tableau Pulse, and Tableau Next has now taken Pulse's capability and rebadged it as 'metrics' again, which causes real naming confusion if you know the product history.

  3. Pick a measure and apply optional filters 1:33

    When creating a metric, name it, add a description, then select the underlying measure; you can optionally filter it down to specific dimensional attributes, similar to filtering in Tableau Prep, if the metric should only apply to a subset of the data.

  4. Set a time dimension, then open up user filtering 3:01

    After the measure is chosen you set a time dimension (such as order date) to get the metric producing a number, then choose which additional dimensions end users are allowed to filter by, giving them flexibility without needing to build separate views.

  5. Configure record-level detail for drill-down 3:42

    You choose a record-level identifier field and a display name field so users can drill into individual records and get insights; think carefully about what grain that identifier actually represents, since it may not be as obvious as it looks.

  6. Enable trend, outlier and contribution insights 5:01

    You can switch on record-level outlier detection, current trend detection and contribution breakdowns as part of the metric's automated insight settings — these are independent toggles you can turn on as needed rather than a fixed sequence.

  7. Metrics live at semantic level, not in the asset list 6:41

    You can also create parameters at the semantic level here, which is useful because it means the parameter affects everything built on that semantic model rather than being scoped to one visualisation; however the metric itself won't appear in the workspace asset list once created, which is disorienting.

  8. Add the metric to a dashboard 8:51

    To surface a metric, create a dashboard space first, then use the icon that references things you've built, choose 'add metric', select the semantic model it belongs to, and pick your metric; if it doesn't show up anywhere try refreshing the page, since this can be a caching issue rather than a real absence.

Worth Knowing
  • The filter builder lets you type a value (like a made-up city name) with no validation against the actual column, so you can filter on something that will never match any data.
  • Newly created metrics can fail to appear in the workspace, homepage or search results purely due to caching — a page refresh may be needed before they show up.
  • There's currently no way to see actual comparison data (e.g. period-over-period) directly while building the metric, so you may not get a line chart until real dated data is present.
  • Record-level detail only lets you pick a single identifier field, so if your unique record is really a composite (e.g. order plus product), you'd need to pre-build that key elsewhere, such as at ingestion.
Use It When

Reach for this when you want a single, governed metric definition (measure, filters, time dimension, drillable detail) that multiple dashboards and visualisations can reuse, rather than redefining the same calculation in every view.

How this Rollup was made provenance & method

A Rollup is drafted by AI from the video's transcript, then reviewed and edited by Tim. Everything used to produce this one is listed below (the model, the exact prompt, and the source video), so the process is transparent and reproducible.

Transcription
On-device: NVIDIA Parakeet v3 for recent videos, OpenAI Whisper large-v3 for earlier ones. The transcript never leaves the machine or gets published.
Drafting
Claude Sonnet 5 in the cloud, from that transcript.
Prompt
The exact Rollup prompt (v2) , the full system prompt, unedited.
Source video
Watch on YouTube
Drafted
5 July 2026 at 09:38
Reviewed & edited
11 October 2026 at 00:00 · by Tim Ngwena

Model + prompt + video is everything you'd need to recreate a Rollup like this yourself. The one thing we don't share is the transcript.

Rights. The video and its transcript are the property of TN Media Ltd. Unauthorised use or download is prohibited. © TN Media Ltd.

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