As a bit of an aside from the usual cold stuff, I’ve also gotten very interested recently in applying some scientific skills to streamlining processes in business settings. This led to a collaboration with the Sheffield-based business Gradconsult. They are contracted by Sheffield City Council to deliver a regional recruitment scheme called RISE SCR. RISE aims to grow the economy of the Sheffield city region by retaining graduate talent, while helping local companies find the right people to join their teams. This scheme has now gone through 12 cycles, growing each time.
The monthly reporting process had become long and arduous, with loads of data needing to be recorded for each individual business, application and applicant. This was taking the team several days every month and in the private sector, time = money. Automating the monthly reporting process frees up their time and enables them to deliver a better service to their RISE clients.
My first thought was to set to work in Python, naively thinking that an open source, free pseudocoding language was ideal for this purpose – I could almost see the pandas dataframes assembling in my mind. But it quickly became apparent that this was entirely the wrong road to go down. For a product to be useful to the business it has to a) be familiar, b) work on the company’s existing software, c) require no new specialist knowledge, d) require minimal interaction or modification, e) be transferable and immediately accessible to different team members and partners, f) be aesthetic and clear.
So, it was back to Excel. I admit I have completely binned Excel in favour of Matlab or Python over the past few years. But, it is industry standard for a reason – mostly self-perpetuating ubiquity, but also because it is a very simple interface. However, some things that would have been easy to code were a bit cumbersome in Excel, like finding unique entries in a dataset that satisfy several criteria.