At Tableau we work with a of lot of journalists who are telling stories with data. These visualizations often go through editors and designers, who focus on accuracy, consistency with the site styleguide, and other traditional checks. But how do you test the data? How do you test the visualization? How do you test the story?
When a user finds obviously incorrect data, or blows up the view by getting an ugly or not useful result, it hurts the credibility of the whole app.
We build quite a lot of visualizations here at Tableau and we test them extensively before we make them public. One of the main forms of testing is what we call interaction testing. Here’s your checklist for that.
Tableau Interactivity Checklist
What to do
- Test each filter: make several selections. See if the results make sense.
- Test all filters: make selections on every filter on the view, all at once. Is it too easy to get into a corner where no data appears? If so, consider removing one or more filters.
- Don’t make filters a treasure hunt for your users. If you have any of wildcard filters, then search for names, states, etc. Make sure users can easily get to results. If there are many variations on the terms that could be searched (i.e. “police,” “cops,” “LAPD,” etc) then you want to use a compact (drop-down) list instead.
- If you're using a view as a filter, select several times on the filtering view and make sure the results are correct and understandable.
- Deselection: make sure users can deselect any filters you've set up. You may need to add a note in text or titles to tell users to click into white space to deselect.
- Check highlighting. Does whatever is highlighted makes sense? Highlighting should enhance understanding, not just be a fun effect. If it doesn't, change or turn off highlighting.
- Check url actions: click on several data points to launch the action. Does it point to the right web page?
What to look for
- Does the view make sense for a reasonable set of choices? (i.e., find my town, find my senator)
- Look at the edge cases: select all, select none, or any other extremes-- see if they make sense
- Find any outliers. This is in fact important—when you find outliers, determine if they’re valid or if they are bad data. If they are bad data, exclude that data from the visualization.
This simple example shows student population at a university by major and degree. The filters on the right are helpful, but it's very easy to get into cases where no results are returned, and it may not be obvious to the user why.
In particular, putting degree type and major filters on the same view is problematic: Choosing "BM" and "Cultural Studies" will always fail to produce results, as will many combinations of degree and major. If this view is used by college administrators, they may be able to figure it out. If it's for parents and prospective students, it could be a frustrating experience.
Another variation of a common filter fail is to give users a search box where it is not obvious what values are possible. Here we've added individual majors to the area of study and added a search box for major.
Searching for "engineering" is satisfying: you see a number of engineering degrees in the results. However, searching for "writing" yields an empty viz. Why? Does the university not offer degrees in creative writing or business writing? Or are those majors called something different? There's no way to know.
Next let's look at a way to add interactivity here that works. Using a slider means you always get results. Adding a second filter constrained to only relevant values (see this article if you don't know what that means) yields a nice experience.
There are many ways to fail with filters. The simple examples here show only two, but if you use the Interactivity Checklist you'll avoid most interactivity problems.