My first role as a data analyst was back in 2003 a time when data was perceived to be a dark art, after several attempts at explaining what I did to friends and family, I resorted to just saying that I worked in computing.
At the time peers told me about books by Drayton Bird a marketer who had used data to aid in direct marketing to consumers. I remember learning SAS (an analytical language), making Excel my best friend when trying to demonstrate outputs and being taught how to build data regression models to predict consumer behaviour.
If I look to where we are now so much has changed. The software tools in use when I started were monopolised by a couple of providers, now we have multiple tools to analyse, present, cleanse and aggregate data. The term “Big Data” has come and gone as computational hardware and the cloud has meant there is nothing really considered that big. Even the job titles have changed, what was once called a data analyst, an insight analyst or a data modeller, has now become a data scientist.
What hasn’t changed is people’s appetite to use data to continuously learn more. The volume of data being consumed is forever increasing, everything from what you bought or watched, to where you went is now available. Yet senior executives and decision makers are still craving the answer to the “So what” questions. “So, what” is the data telling me? “So what” decisions should we make?
The embracing of data analytics probably started a little later in Sport than in Retail and Finance. It acquired a fresh impetus by Billy Beane’s “Moneyball” story, that was given mainstream publicity by the film adaptation in 2011. As much as it helped highlight the importance of data in Sport, it also led to some scepticism that all decisions would be made by numbers. I don’t believe this will ever be the case.
Fast forwarding to 2020 and the world of sports is now a rich playing field for data, everything from a player’s training, fitness levels, nutrition through to their match day performance is being analysed. The use of video footage to support these data findings has been key in allowing coaches to make impactful decision and communicate these to the players. Many athletes, clubs and federations now have their own data scientists to aid them with their performance, and those that don’t have a team of performance analysts doing the job. As the sporting data journey continues more data sources will be found, and as sports is all about competition, everybody will be looking at how they have an edge or can take advantage of their opponent(s).
The data landscape may now be completely different to when I first started my career. However, the majority of the questions I asked myself 17 years ago are still as relevant today.
What is the question you are trying to answer?
Do you have a hypothesis, what is it and why?
What pieces of data allow you to attempt to answer this question?
What pieces of data are you missing?
Is the data actually showing anything of value (it’s always better to show nothing than fall foul of the trap of producing analysis when there is no meaningful insight)?
Do you have a sample size large enough to come to a meaningful conclusion?
What caveats have been assumed to come to your findings?
Was the conclusion what you had predicted?
Data still remains nothing without insight. Having more and more data for data’s sake is meaningless. How you interpret and synthesis data into actionable information, is and will always be the key to answering, “So what?”
To find out more about how Sports X Consulting can help you on your own data journey please e-mail me email@example.com