I had this excellent question from a subscriber about what makes great data analysis in motorsports:
“What should we be looking for when using data? What should we aim to do? How do we make sense of little data? … What makes great data analysis?“
In fact, it is several related questions. Rather than directly answering with an opinion, instead I thought it would be more valuable to you if I began unpacking this so you can consider what is important. Using an example from Olympic sports, the suggestion is to first be clear on your goals and only then look at how data can help you.
Perceptions Of Great Data Analysis
One underlying issue is in our own perceptions and expectations of data.
If you are a “numbers” person, you get used to a level of certainty with your work i.e. 2+2 does equal 4.
Subjectivity though is imprecise and unsettling.
Unfortunately this can mean “great data analysis” for one person is not the same as “great data analysis” for someone else.
Of course, you want to get everything you can from the data – it is a challenge, it is solving a puzzle that you know has an answer. If you can do this then surely that is great? But if someone isn’t happy then what do you do?
Perhaps look at your approaches …
Try starting, not with the data or technology but by applying what some people call critical thinking.
From the ancient Greek philosopher, Socrates:
“He established the importance of asking deep questions that probe profoundly into thinking before we accept ideas as worthy of belief.
In other words, apply the same rigour to your problem definitions that you do to your problem solutions.
By apply critical thinking to you determine what would be valuable to know and when. In motorsports, I’m thinking about giving driver feedback or forming a driving plan for a track.
You can then consider how to use data, analysis and models, to help you.
Too often it is “What can we do with this data?”, rather than, “What is really important to know right now?”
Intelligence Naive Questions
To do this in practice, I recommend taking a take a consultative approach. Consultants, love them or loath them, are really very good at this. We can learn from their practice.
I personally call this asking “Intelligent Naive” questions.
- Do you really understand how things work?
- Can you clearly define what success looks like for the driver?
- Have you any idea what sensitivities go into this?
- What happens if we don’t do this?
What It Takes To Win
To give you an example from professional sports. They talk about a model of “What It Takes To Win.“
You can see an example of the British Judo teams WITTW model in the image below:
This links everything you’re doing together logically.
What Does It Take To Beat The Opposition?
In Judo, they need
- Proficiency in fundamental technical and tactical skills.
- To be able to apply this in competition.
- To pass a certain specific physical profile.
- Good nutrition.
- Good body composition is important.
- To be strong in the mind.
Like a racing driver really ?
They’ve then broken each of these legs down into specifics for Judo. Then broken that down further into things they can train and measure against. Every layer is linked.
With What It Takes To Win you are combining goals, unique experience and knowledge into something actionable and clear.
It is these second-order answers you want to focus your own Intelligent Naive questioning around. Once these are clear, you’ve a robust basis for your data and reporting.
Deliver on this and there is no question your work will be described as great data analysis – even if it is delivering news people don’t want to hear … “You’re slow, here, here and here … !” ?
In sport, once we understand (and agree on this!) we can plan all our activities accordingly – all our training, strategy and talent development aims become clear.
This is advanced stuff but has the most potential for high value as its more closely linked to a meaningful outcome.
For now, the take away is this. I’ve used the WITTW model together with a consultative “Intelligent Naive” questioning approach to help teams use data analysis to improve performance.
Great data analysis is always linked to high organisational value and, in my experience, this approach ensures you achieve both.
Hopefully you found it interesting. Sign up below and let me know.