England’s World Cup exit will have the RFU poring over the team’s statistics to uncover what went wrong. But we can all learn from the defeat – especially when it comes to showing how data analytics can make the difference when unlocked to its full potential
And so, the inquest begins. With England crashing so spectacularly out of their own World Cup, the RFU will soon be picking over the bones of the botched campaign to work out what went wrong – and who’s for the chop.
Their task will be made considerably easier by massive amounts of data which tell the whole story of every player’s contribution to the team, from heart-rate to metres made. These may be uncomfortable weeks for some England players, knowing that their performances will be under the microscope.
Of course, the RFU is welcome to draw on our own World Cup data visualisation, which lets you pit teams and players head-to-head to see how they have performed historically and assess win ratios for individual players.
Sport has always had a good relationship with data. Players have their own personal objectives for tries, goals and runs; coaches analyse performance on the training pitch; commentators use stats to add colour and context during games; and an army of statisticians can be found in any pub on match day.
But the nature of analysis in sport is changing. It’s no longer the sole preserve of sports analysts and managers: now everyone from medical staff to groundsmen are using the power of data to make better decisions.
And it’s not just sport where this is happening. In a whole host of industries, non-technical people are starting to harness the power of data to help them in their jobs. Marketers, journalists, clinicians, academics, architects, environmentalists: the list is pretty much endless. One of the results of this trend is that traditional job roles are changing, along with the skill sets that employers seek from new recruits.
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But not everyone is doing this right. Too often, the job of wading through ‘big data’ is left to a special few who have the tools and the training to tease out insights. However, this type of thinking limits the impact that information can have on an organisation.
Rugby gives us a good illustration of this issue. It’s clear that some decisions, such as adapting tactics for an upcoming match, do need to be based on thorough analysis of multiple different data sets, and so require deep-level, expert analysis.
But with a player injured, and just ten minutes left on the clock, a coach needs to know instantly who is the best player to bring on, given the context of the match. The point about data-driven decision making is that it loses much of its value if it is not delivered in a timely (and sometimes instant) manner.
If you restrict analytics to a team of trained analysts, there’s no way that you can access the insight you need in time for it to be effective. In short, the person who really needs the data is the one making the decision. They cannot wait to be spoon-fed answers to specific questions: they need to be able to discover the answers immediately. What’s true for a rugby coach also applies to any job where time is of the essence.
It seems that we stand at an impasse. We have the tools to derive powerful insight, but unless we spend much time and money on training everybody to use complicated Business Intelligence tools, they won’t have the skills to use them.
That might have been true a year or two ago, but the situation is changing fast. Today, you don’t need a degree in data science to analyse information effectively, as there is a burgeoning industry for easy-to-use solutions for turning data into deep and actionable insight. These can range from social media dashboards, to tools that enable anyone to build and visualisations using data from any source, whether it’s internal sales figures, the web, or social media.
The point is that these technologies and platforms are designed for the lay user: the marketer or coach who doesn’t have time to learn a new skill, but needs answers immediately. It is with tools such as these that businesses (and sports teams) of all kinds can begin moving data analytics out of the back office, and put it into the hands of people who understand the context, and who make the decisions.
While these technologies are obviously a prerequisite, organisations won’t begin unlocking the full benefits of their data unless they put the right processes and best practices in place. These will vary from company to company, but the most successful teams will be those that build a culture of data within their organisation, where every conversation, meeting or decision has some kind of empirical foundation; and where information informs and guides everything you do.
The end result can only be a better way of working, where every decision is backed up by data. Unfortunately, all the analysis in the world couldn’t quite help England when it came up against such superior opposition as did at Twickenham. But every organisation needs an edge. In a close campaign, data could very well tip the balance.
