Online tableau training, tableau tips, & video tutorials



Tableau Classification: Measure vs. Dimension



Ryan Sleeper

Understand the foundation for slicing and dicing fields

One of the critical ways Tableau classifies fields is as dimensions and measures. Learn what makes these classifications different and how you can use them to speed up your analyses.

Hi, this is Ryan with Playfair Data TV. And in this video, I’m going to discuss the first major way that Tableau classifies each field in your data source. And that is as a measure or a dimension.

By default, Tableau classifies any quantitative field, so any number, as a measure. That’s the definition from Tableau’s own knowledge base. These measures are dependent because they really tell us nothing on their own. But looking at this bar chart right now, and it’s $2.3 million. I happen to know that the measure is sales, and this is coming from the sample superstore dataset, by the way.

But unless I know all the exact parameters of that data source, that number doesn’t mean too much to me. I’d have to know exactly what number I was looking at, what was the date range, what were the dimensions or the categories involved to get to that $2.3 million. I don’t know any of those things right now, so that number really means nothing on its own. It’s dependent on the context of a dimension, which we’ll talk about in just a second.

I like to use the joke that if that was my retirement account right now, I’d be feeling pretty good about it. But if last quarter, that number was $8 million, I’d be feeling pretty bad about it. I just don’t know.

Dimension, which is the other side of that coin, is any field that’s qualitative. So anything that’s a string or text-based, by default, Tableau is going to classify those as dimensions. Dimensions are independent because they do have some inherent information that comes with them. In this bar chart, we’re now looking at sales by a dimension called category, and within that category there are three what are called dimension members. There is furniture, office supplies, and technology.

Those are independent because they mean something on their own. At some point somebody in our business had to say these are the products that are in the furniture category, these are the products in the office supplies category, and these are the products in the technology category.

So while dimensions are independent and come with some information, these things work best when they’re combined together. You would start with a number like sales, and then you would break it down by a dimension called category. And when you combine these two things together, that’s when you can really start to see the insights emerge. So in this example, we can see now that technology is leading the way, furniture and office supplies are kind of neck and neck, furniture is second, and office supplies is third.

I’ve got several rules of thumb when it comes to Tableau. And this first one is really the cornerstone of how I think about building everything in Tableau. But in general, measures are the numbers, and the dimensions are what we slice and dice those numbers by in order to get insight. It’s going to be very important in all of your analyses, and you’ll see this as a recurring theme on Playfair Data TV. I always start with my numbers, and then I add context through dimensions in order to start to get insight in my analyses.

This has been Ryan with Playfair Data TV – thanks for watching!