By Matt Mansfield
22 January 2018
We live in a world where data is all around us – collected as we use our personal devices, as we create and sell in our workplaces, and by government through statutory reporting. Being able to see through the vast swathes of data is an ever-increasing concern for most organisations, yet surprisingly this is not just a recent phenomenon. Back in 1985, educator and technology theorist Neil Postman noted this trend and coined the term “information-action ratio” – the correlation between information people receive and the relevance to their actions/lives. His words seem somewhat prophetic in the age of big data:
“The tie between information and action has been severed. It comes indiscriminately, directed at no one in particular, disconnected from usefulness; we are glutted with information, drowning in information, have no control over it, don't know what to do with it".
This is where data analytics can provide solutions to cut through the background noise and provide insights and intelligence surrounding a system, process or organisation. Let’s take a lighter look at the capabilities of data analytics by using these powers to make a prediction about the upcoming Australian Open, celebrating its 107th edition in Melbourne this January.
The building blocks for any model is historical data, so the details of all previous winners in the Open era were analysed in order to determine which aspects had the greatest correlation. This resulted in a model of seven distinct features, with notable correlation between prior year record, age and surface percentage. When this was applied retroactively to the list of champions, we see the highest value matching Novak Djokovic’s near unbackable form leading into the 2016 edition, but the lowest value shows just how remarkable the victory of Mark Edmondson – the last Australian to win the Open – was in 1975 (with only seven professional career matches prior to the tournament!).
So how does our model perform with the current data for the Association of Tennis Professionals (ATP) Top 100? Unsurprisingly, given their continued dominance of men’s tennis, the top three remains the same with Djokovic (1), Nadal (2) and Federer (3) all sitting within two percentage points of one another, and head and shoulders above the competition.
Big movers in the Top 10 are Raonic (5)[+13], Nishikori (6)[+3] and Dimitrov (10)[+9]. As for the local contingent, Nick Kyrgios is primed for a big run with our model having him ranked 11th, up an impressive 24 places on his ATP ranking.
Data analytics can be of immeasurable assistance for organisations of any size. Regardless of the capabilities or maturity level of your own internal functions, tailored solutions can be provided to help your organisation across the full data analytics spectrum, including benchmarking, forecasting, reporting and internal process improvement. The key, making reference to Postman’s information-action ratio above, is to get the right information to the right people to make the right decision.
Our data analytics experts at PKF would be happy to discuss how your business can make the most of their data.