On Friday Martin, Darren and I were discussing the ThoughtWorks graph that I was working on earlier in the year and Martin pointed out that an interesting aspect of this type of work is that the data you want to work with isn’t easily available.
You therefore need to find a way to scrap the data together to make some headway and then maybe at a later stage once some progress has been made it will become easier to replace that with a cleaner solution.
In this case I became curious about exploring the relationships between people in ThoughtWorks but there aren’t any APIs on our internal systems so I had to find another way to get the data that I wanted.
The obvious way to do that was to get a copy of the database used by our internal staffing system but I didn’t know anybody who worked on that team and trying to get the data that way would therefore be slow and lose my initial enthusiasm.
My only alternative was to go via our staffing application and derive the data that way.
I ended up writing some Selenium scripts to crawl the application for people, projects and clients and save that data to JSON files which I later parsed to build up the graph.
The other bit of data that I was curious about was the sponsor relationships inside the company which is kept in a Google spreadsheet.
I wasn’t allowed access to that spreadsheet until I was able to show what I was going to use the data for so I first needed to put together something using the other data I’d screen scrapped.
Once I did get the spreadsheet I spent around 3 hours cleaning the data so I could integrate it with the other data I had.
This involved fixing misspelt names and updating the spreadsheets where I knew that the data was out of date – it’s certainly not very glamorous work but it helped me to get to a visualisation which I wrote about in an earlier post.
I haven’t done a lot of work in this area but I wouldn’t be surprised if it’s common that we have to use relatively guerilla tactics like the above to get us up and running.