Hi everyone -
I'm trying to build a sort of data quality visualization in Tableau. I have a database table with ~60 dimensions, and I want to see a visualization that shows the percent of valid vs invalid entries in each dimension. What's considered valid is different for each column. I've gotten most of the way there, but there were a couple of things I wasn't able to figure out.
Below is a screen capture of what my implementation looks like. Each bar in the bar chart represents the percent of valid entries in a particular dimension. I added a calculated field for each dimension that computes the percent of valid entries for that particular dimension. Then I put "Measure Names" in the filter, and selected all those "percent valid" calculated fields. Then I graphed Measure Values against Measure Names -- and voila.
Here are the things that I was not able to figure out:
1. Since there are so many dimensions, I'd like to display the 5 or 6 most important ones by default, and then expose a filter to let the user choose which other ones they want to see. However, when I expose the Measure Names filter -- it doesn't seem like there's a way to show a subset of the dimensions. It just shows all of them -- which I definitely don't want. Is there a way to remove some names from this filter?
2. For each dimension, I'd like to be able to drill down to the underlying data individually for both the valid and the invalid entries. For invalid entries in particular, it would be really useful to see the underlying records and scan them for patterns. With this implementation, I can see the underlying data for the valid entries by right-clicking and choosing "View Data..." -- but I can't do that for the invalid entries. Is there a way to include both? What I really need here is a stacked bar graph with two colors (valid and invalid) -- but I can't seem to figure out how to make that work with this particular graph.
Thanks in advance for any help you can provide! I'm still pretty new to Tableau, so hopefully there's something easy that I'm missing here!