The University of Birmingham is a university in the UK, focused on encouraging “bold, independent thinking” from students. In the past, the business intelligence team at the University of Birmingham struggled with inflexible reports, making it difficult to incorporate feedback from colleagues. Compiling information for their research excellence framework—a crucial dataset for operations—used to take up to 20 people to analyze the data in Excel, produce statistics, and share the data with managers. Today, the university uses Tableau Desktop & Server to compile, visualize, and share important information across teams. The research excellence framework that used to take 20 people, now takes an hour. And they hope to make data even more accessible. The business intelligence team is working on a university key performance indicators dashboard built on Tableau Server, allowing more people to engage with reports on a daily basis.
Tableau: How does your team use Tableau? Rob Andrew, Head of Business Intelligence: One of the ways that we now use Tableau is, assuming that we've got the right data source and we've got the right data, we're able to produce visualizations very quickly from that or be able to make visualizations that can be adapted very quickly based on our user feedback. Tableau: What was the situation before Tableau? Rob: So in the past, we might take quite a lot of time to build reports and then publish those reports. And if you got feedback, it's probably quite difficult to go and change it because it had been built in such a specific way that they actually became quite inflexible. Tableau: And how has that changed? Rob: Whereas with Tableau now, we find that we can produce a visualization and adapt it very quickly and also the ability for us to be able to produce those visualizations in the first place is much more rapid. And it also means that we can change the way that sometimes we go about a process. Tableau: Do you have an example of a time where you sped up the process because of Tableau? Rob: So, one example that we found recently was we were getting some information about the research excellence framework for 2014, which is a key piece of information for universities. We got the results in December. So five years ago when we did that same exercise, we had a batch of 20 people geared up to be able to use Excel, to be able to download the data, to produce some initial statistics, and then went around the university with Excel spreadsheets trying to inform our managers about what was going on. And then we got the situation where actually as a result of the initial analysis that we provided, there was some request to get some additional measures and metrics produced in time for the next data results coming out. Now, if this had been five years ago, we'd have been not in a position to be able to produce that additional analysis. Whereas this time, by creating some extra calculations overnight, we're then able to load the data within an hour, the next day, and also produce the analysis straight away without the 20 people required to do that process. Tableau: What did that experience show you? Rob: So, for us, it was a real demonstration about the fact that the software tool allowed us to start doing rapid analysis in a way that we hadn't been able to do before.