Kirk Munroe: Tableau Next Part 3: Metrics, Visualizations, & Dashboards

Kevin and I are pleased to welcome back a regular contributor, Kirk Munroe. Kirk lives in Halifax, Nova Scotia, Canada and is a business analytics and performance management expert. He is currently one of the owners and principal consultants at Paint with Data, a visual analytics consulting firm, along with his business partner and wife, Candice Munroe, a Tableau User Group Ambassador and former board member for Viz for Social Good.

 

Kirk is also the author of Data Modeling with Tableau, an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. The book details the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. It also explores the components of Tableau Server and Cloud that make data modeling more robust, secure, and performant.

 

This is the third in a three (or maybe four)-part series about Tableau Next, in which Kirk is going to introduce us to Tableau Next and try to remove some of the confusion that exists in the Tableau community.

 

Finally, we are back for part 3 of 3 of the Tableau Next series–sorry, I fell a little short on my prediction of it coming “shortly” after part 2! Tableau Next is changing so quickly, and becoming so AI-first, that it is hard to keep up.

 

With that in mind, let me finish Part 3 and then look at a post on all things AI in Tableau to keep this moving along!

 

Creating a Metric

Picking up where we left off in part 2 of this series, let’s look at Metrics in Tableau Next. Metrics are very similar to what Tableau Pulse is to Tableau Cloud. The thing that I like about Metrics is that you build them IN the semantic model and not ON TOP of the data model, like you do with Pulse.

Let’s take a look:

 

From within our semantic model, we can create metrics, calculated fields (which we looked at in part 2), parameters, dimension hierarchies (just like in Tableau classic), logical views (think joins to create a single logical table in Tableau classic), and add new data objects (tables).

 

Note: We are only going to look at metrics in this post, but I do want to point out that you can create parameters within the semantic model in Tableau Next. In Tableau classic, we don’t have this option–parameters are always in the workbook. In some ways, the parameter at the workbook level is great because it works across data sources in the workbook. The downside is that you can’t create calculations in the data source that reference parameters that aren’t there.

 

Let’s look at a new metric:

 

The first step is details:

 

Step 2 is how to calculate the metric and how it behaves:

There are some nice features built in here like Up is Good or Bad and how it is intended to be aggregated. Metrics live as first class citizens on dashboards in Tableau Next and can be embedded directly on Salesforce pages. In addition to thinking of these as Pulse metrics, you can also think about them as KPI cards. Think about how much work these are to create in Tableau–calculations, often multiple sheets, calculations for color on above and below target, etc. Nice to be a first class object.

Step 3 is adding the dimensions you want the consumers of your metric to be able to slice-and-dice it by… nice to be able to explicitly exclude dimensions that aren’t relevant to your metric:

The fourth and final step is to let the metric know the lowest level of grain of the metric (what is the record level identifier) and what trends and contributors you want Tableau Next to find underlying in your data, and whether you want this feature turned on or off. If you have used Pulse or Explain Data in the past, these options should be familiar. If not, I definitely recommend exploring them!

There it is. A metric created. We will get back to it in the next section.

 

Visualizations

After 2.5 posts, we get to the part you were probably waiting for… creating visualizations (sheets) and dashboards (ummm… dashboards)!

 

The equivalent of creating a worksheet in Tableau is called a visualization in Tableau Next. Creating a new viz in Tableau Next drops us in a very similar experience to the one we are used to:

Let’s say that I want to know sales by title. I just drag-and-drop (or double-click) and just like Tableau classic… a bar chart!

The one big difference is that the concept of a workbook doesn’t exist in Next. These visualizations get saved to a workspace, which is pretty similar to a project in Tableau Server/Cloud. I think this makes sense given that there is not a Tableau Desktop for Tableau Next (at least at the time of writing). In such a context, does the concept of a workbook really serve a purpose?

The other thing I should mention is that there are some charts in Tableau Next that aren’t native in Tableau classic, like waterfall and donut. There are also charts missing like maps. I think both products are working toward parity toward each other with Tableau Next adding new chart types monthly and native extensions, like the new radial chart, being added to Tableau classic regularly too.

There really isn’t much more to add here. The feature sets are very close but not identical. Trying to explain every difference is hard and constantly changing. I recommend you play with it for yourself, if you have time!

Dashboards
The other key artifact is dashboards, of course! The dashboard creation process is quite different in Tableau Next. When we create a new dashboard, we get some layouts to choose from, or we can start with a blank canvas.

I’m going to start from scratch. This is an experience that has to be tried to be truly understood. What I will say is that it is a bit of a hybrid experience of floating and fixed. For those coming from a Power BI background, the experience is more like what you would be used to than people who have only used Tableau.

As I mentioned earlier, you can add both metrics and visualizations to dashboards. Everything you add is drag-and-drop. One nice feature is that you can add a new visualization and create it on the fly, without needing to create it separately first. Although this is a little thing, when I train new users to Tableau who come from a Power BI background, it seems like this is their default behaviour.

You can see this when clicking on “Add Visualization”. You can pick an existing visualization or click on Create New Visualization in the bottom left hand side of the dialog box.

Here is a simple dashboard with the metric and visualisation we created earlier:

Again, I don’t think it is worth getting into all the features here as it has to be used to be fully understood. What I will say is that you can make a dashboard very similar to a dashboard in Tableau, including separate tabs. However, where building visualizations will feel very familiar, building dashboards is going to feel very foreign.

 

I’m honestly not sure which I like better. Let’s be honest, building charts (sheets) in Tableau is fun; working with dashboards can be tedious. I do find the Tableau Next experience very foreign, but I think I might find it more intuitive than Tableau classic if I was being exposed to both for the first time.

 

That’s all I have for you today. What I’ve shared in these three posts is really the core of what Tableau Next is (less the very important AI-first features). I hope these posts have helped users of Tableau Classic to understand what Next is and how it’s different. And, armed with this knowledge, I hope you go out and give it a try for yourself. I’d love to know what you think.

 

I hope to write a part 4 soon, which would focus on the AI features across both Tableau platforms, so hopefully I’ll see you soon!

 

Kirk Munroe, June 1, 2026