In 2020, Tableau unveiled a new way to combine multiple tables of data in Tableau Desktop: a Relationship. Billed as "smart joins", relationships provide the capability to merge multiple tables of data horizontally on shared dimensions (like a join). However, they don't duplicate data when the tables being combined have different levels of granularity.

So, are they better than joins? Not always! Relationships can reduce data duplication and improve performance, but a data set that is generated from a relationship might present blind spots if the data isn't perfectly clean.

I was recently working on a project for a client that had a lot of dimension filters - anywhere from 5-10 on each dashboard. This allowed us to drilldown to the level of information we needed!

But a problem arose when we wanted to reset the filters and select new values. How could we do that? Did we really need to reset each filter individually? That’s so inefficient!

Do you ever have a data set with US State names and it would be really handy to save some space in your visualizations by displaying state abbreviations instead of full state names? Or how about a situation where you want to display country abbreviations instead of the full country name? This happens to me often!

It feels like every year or two I run across a need for this and end up writing the same calculation from scratch over and over again. No more! This video shows how to write calculations which convert state and country names to their abbreviations and provides a Tableau Workbook with the calculations you can copy and paste and never have to write them from scratch again!

Does your data set have multiple date fields? For instance, maybe you have both an "Order Date" and a "Delivery Date". Or maybe you have both a "Hire Date" and a "Termination Date".

Sometimes, you need to filter on both date fields at once to answer questions like, "How many people were hired and how many people were terminated in the Marketing Department this year?"

Sometimes, you have too many values in a dimension on the rows shelf in your Tableau worksheet and end up with a scroll bar. Annoying, right? Scroll bars mean your users are less likely to see the data at the bottom of your worksheet because it it out of sight and out of mind.

One idea I've heard discussed is, what if you could break the data into multiple columns? For example, instead of displaying 50 states as a single column of 50 rows, could you display it at 2 columns or 25 rows?

We're looking at Tree Maps this week! If you're trying to show breakdowns of the whole in Tableau, you need to have Tree Maps in your tool kit.

Tree Maps are amazing because:

  1. They're particularly good at representing data with long tails. 

  2. They can represent data in a hierarchical structure (we can build Tree Maps within Tree Maps)!

  3. They're space-efficient, and allow us to visualize many dimensions or measures in one view. 

Tableau Prep is a powerful data preparation tool which allows you to do complex data transformation in a "no code" environment. No need to learn Python, SQL or R to shape your data.

While Tableau Prep has come a long ways in its first five years of existence, it's still a young tool with limitations. For example, it's not strong at crafting multi-row formulas (e.g. referencing the value from the previous row in the next row).

Dual Axis charts are one of the most versatile chart types in Tableau. Technically, this is just a view that houses two measures with their independent axes.

But, the beauty of Dual Axis charts is found in the creative ways we can format them. We can use these charts to achieve all sorts of crazy “illusions”… like showing two different labels for a single bar, or creating rounded bars, or making donut charts!

Users love filtering and sorting options in Tableau dashboards, right?

Odds are, you know you can use a parameter to change the top number of values displaying in a worksheet. However, did you know you can also use a parameter to change whether it's the Top 10 or Bottom 10 values displaying? Did you know that same parameter can control whether the sort order is ascending or descending? It's true!

In the past, swapping worksheets in a Tableau dashboard has been possible but frustrating. Using a parameter, we could swap out worksheets, but it was really difficult to also swap out any filters, parameters, or legends attached to those worksheets.

Now, with Tableau’s Dynamic Zone Visibility, that’s all changed! Dynamic Zone Visibility allows us to switch between multiple connected elements on a dashboard, from single worksheets, to multiple worksheets; from a certain set of parameters to another; or from one worksheet with its legends, filters, and parameters, to another.

Many organizations report against a fiscal calendar. If your organization's fiscal year doesn't start in January and you've tried to display values against fiscal periods in Tableau, you probably know exactly what I'm talking about.

When fiscal date periods are being compared, it's usually Fiscal Years or Fiscal Quarters. Let's imagine you are asked to build a chart which compares business segment values from the current Fiscal Quarter to Date versus the same period last year. Where would you start?

A few weeks ago, we took a look at how to build donut charts in Tableau. Building a donut chart is similar to building many other views in Tableau. It’s simple to set up, but how do you get it looking really great?

This question comes up in my classes. Often, it’s the way we format our charts that has the biggest impact on our end users. So, while this week’s video pertains to donut charts, I hope that it holds some tips and tricks for adding layers of interest into our other views and charts too!