Architecting Speed. Making Racing Data Useful (Kevin Richardson, ProfsoUX-2018)
* Kevin Richardson
There is a lot of data out there. There is also a lot of discussion about how large data sets hold the promise of business growth, large scale innovation, and, perhaps most importantly, insight. Traditional businesses, however, are not the only ones relying on very large data sets. Motorsport racing generates enormous amounts of dynamically changing data in the pursuit of speed. At the professional level, teams collect more than 3 billion data points during a single 45-minute race. Even amateurs are collecting >1 million data points with minimal investment. Unfortunately, complex dashboards and data visualizations are the norm, leaving the problem of translating the data into actionable information to the racers (or their engineers). This “solution” of relying on end-users to translate data into actionable information highlights two important problems.
Problem One. People neither act on, nor infer insight from, data directly. Data on its own is useless. It requires someone to translate it into information. Information is actionable. Data is not.
Problem Two. Businesses (racing included) do not recognize problem one. Focusing on converting data into charts and graphs (re: dashboards) leaves users with the unenviable task of trying to translate it into a form that is both contextually relevant and actionable.
My presentation demonstrates how the design process was applied to the problem of making large data sets found in motorsports racing useful to the racers and engineers of this unique business. Through the creation of innovative racing software, I demonstrate how design bridges the gap between the technical challenge of data collection and the human challenge of data interpretation.