It’s becoming increasingly well-known that, no matter what you want to improve, the first and most useful thing you can and should do is measure it. Objective measurements keep you honest and give you something to focus on.
A few years ago, being the unit test junkie I am, I wrote a data quality analyzer for my gender-bending index. Unfortunately, for a variety of reasons (it was a quick and dirty mess of a Python CGI script with no thought put into it), it was never made public.
That changed yesterday with my new rewrite of the Data Quality Analyzer to fit into the new GBIndex codebase. Anyone can now look at the current state of things, whether to heckle or to help by contributing data. (Unfortunately, I haven’t yet had time to implement a history so it can’t track change over time)
Thanks to the marvel of dogfooding and my interest in UI/UX design, the analyzer has the following convenience features:
- Tests that turn up no problems are hidden by default but can still be shown
- Each entry contains a miniature pie chart with an informative tooltip to clarify how serious each problem is.
- The miniature pie charts can be clicked to pull up details on the problematic entries
- As much as possible, data in the details view is hyperlinked for quick access
- Except for pulling up details (which will eventually get fixed), every link can be middle-clicked.
There’s more to be done to streamline things, but that’ll require some other parts (like the new UI for submitting corrections and the new code for managing XMLHttpRequests and history) to be written first.
…and, as I expected, as soon as I got the analyzer working with the new database schema, I ended up taking some time to procrastinate coursework by doing some cleanup on the GBIndex’s data.
Play around with it if you want, have fun, and feel free to suggest new things it can check for.