Imagine, for a moment, that you’ve been given a task to analyse a dataset inside sixty minutes and share your results. How far do you think you would get in that time? It’s a question I had cause to reflect on recently, after running a workshop. In the workshop, I gave people a dataset and one hour to do something cool. Their results were astounding. To understand why they were so impressive, let me provide a little context by considering how many of us work with data.
First, let me dispel a myth. Contrary to popular opinion, if you are using spreadsheets or traditional BI tools, it is quite possible to build beautiful charts. Unfortunately, each view of your data takes considerable time to build. Do you have that time to spare in your working life? What if the chart you take 10 minutes to build doesn’t answer your question? What if it inspires a new question? You have to go back and start again.
What if you could explore your data at the speed of thought instead? What if each mouse click changed the view instantly? This is what we call visual analytics: it allows you to find insight in your data at speeds unimaginable just a few years ago.
You’re probably wondering how all of this relates to the workshop I mentioned earlier. Well, during the session, we gave our teams a list of every UK Number one album since 1956, downloaded from Wikipedia. The instructions they were given were to analyse the data and publish something interesting within one hour.
Did they deliver? Oh, boy, yes, and in ways that made my jaw drop. Each entry was different. The winning team analysed albums that had been to number one more than once, revealing perennially popular music, and the effects of sales on a musician’s death. Another team came up with an album explorer that found out which album was number one on your birthday.
One team created a visually gorgeous dashboard, sure to engage anyone. A further team came up with a predictive model based around the likelihood of any album title to get to number one. What was truly amazing was that they did this in one hour. Sixty minutes!
Unfortunately, many people are stuck with tools that are cumbersome or too hard to use. It often takes more than an hour just to connect to data. The simple lesson I’ve learnt from the recent session is that although some tools can make amazing charts, they are often unnecessarily complicated. With some, you need to fill in five steps in a property wizard just to draw a chart. In others, you are required to write custom scripts before you can start drawing anything.
The question we need to ask is whether we are using the right tools to answer questions quickly and in the most efficient way? If not, then perhaps it’s time to ditch these time hogs and focus on analytic tools that save you time instead!