Effective data dashboard design is an art. It requires meticulous attention to detail. It includes visualizing the right data, displaying the appropriate level of granularity, designing an efficient layout, and making a number of minute design decisions so the data hits your user "just right".
If you're looking for a checklist of design elements to review before your next dashboard rollout, you've came to the right place! In this video, we discuss the following 15 design best practices:
Do you want to use Tableau to look at running headcount over time? For example, you work with employee level data which has Start Dates and End Dates and you'd like to be able to see how the total active headcount at the company has changed over the last 8 quarters.
That's something Tableau can do, but it isn't going to work out of the box. It's going to take some creative data structuring and calculations to get things working.
A reference line is a useful worksheet element. It’s a simple tool - just a line based on a single value that we want to reference in our views.
But, did you know that you can change the value of a reference line using a parameter?
An adjustable reference line turns your static reference line into an interactive tool that you and the end-users of your dashboards can use in a variety of ways. You might run basic forecasts, explore outcomes using 'what if" scenarios, or change a comparison threshold.
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:
They're particularly good at representing data with long tails.
They can represent data in a hierarchical structure (we can build Tree Maps within Tree Maps)!
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!
Did you know you can reference Tableau Sets in calculations? Most Tableau users don’t.
Referencing a set in a calculation provides you the ability to filter a calculation, rather than an entire worksheet, based on end-user selection.
Why is that valuable?
Have you ever wanted to display your data in a map format but aren’t quite sure where to start? Or do you want to learn how to get the most of Tableau’s default mapping features?
You don’t need a lot of intricate geographical data to start building maps in Tableau!
Table Calculations are a family of functions in Tableau Desktop which allow users to reference data at the aggregated, data point (mark) level. They are a powerful but often misunderstood component of the Tableau ecosphere.
Some of the most common applications of Table Calculations include calculating Percent of Total, Percent Difference and Rank.
Unions are an incredibly powerful tool in our data prep stage when working in Tableau Desktop. But how do you know when to use a Union and when to use a Join?
The general rule of thumb is: when you want to add rows of data to your data source, use a union!
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!
