Sometimes the curse of knowledge kicks in, and we don’t remember to communicate in a language understood by people with a variety of experiences and perspectives.
At the intersection of Sports, Tech and Data, this happens a lot.
It’s critical to meet people on their level, something I’ve experienced many times.
When I was coaching at Florida State University, there were four main (sport) coaches I reported to. One wanted every grain of data with the most complex charts so that she could learn every contextual nuance available. Another preferred only scatter plots (no matter what was being presented) while a third wanted only bar graphs. Lastly, we had a coach who wanted no graphs or figures, ever. Simply binary reporting such as better/worse, more/less, high risk/low risk. It was my job to present our insights in ways which they would understand.
Some want to know the “what”, others need the “why” and “how” to buy-in. Figuring this out is critical, and upskilling our colleagues in data literacy is a hugely valuable part of any organization’s maturation, as Eva Murray writes about. Design data processes in ways that are accessible for stakeholders with different skill sets, perspectives, and needs. EQ matters.
Once data has been processed into insights it’s important to consider the impact it can have on organizational dynamics. Insights should be presented as actionable information and guidance towards informed decision-making, not as a strict gospel.
Credibility drives change, so manage expectations and focus on low-hanging fruit. You can’t go wrong with high standards of data quality, model performance, accuracy + explanation, and education around statistical methods.
These dynamics can make or break how data is or is not used. Go forward wisely!
