As data availability becomes cheaper and easier to access, companies often assume that more data leads to better performance. This can be a costly assumption in terms of technology investment, organisational priorities and human capacity.
Formula 1 has long been a pinnacle of high performance, innovation and data-driven decision-making. So what can we learn and apply from this world that will help you accelerate your performance and optimise your data?
Why is F1 applicable to my organisation?
Whilst the world of F1 can be seen as vastly different from a lot of organisations, several parallels make it an applicable and rich ground for learning, namely:
Like an F1 team, we all have competition that we are ‘on the track’ with. Learning from your competition whilst also staying true to your strategic focus is a practice that has been honed by F1 teams over the years.
Formula 1 is an innovative sport, not only in obvious areas such as technology and car design but also in leadership, teamwork and decision-making. The capabilities, systems and approaches for innovation used in F1 are equally applicable in any industry.
We all operate within our regulatory restrictions, which are often being changed, reviewed and updated. The scale and pace of regulatory change in F1 are such that teams have learned over the years to embrace those changes and ultimately use them for competitive advantage.
4- Global Logistics
Moving an F1 race team around the globe for the 20-plus races across multiple continents over nine months of the year is a major logistical undertaking. Even small organisations tend to have a large global footprint of suppliers, customers and stakeholders, and there are always lessons to be learned in managing this.
5- Data maturity
We are all on our data maturity journey. Learning how an organisation can move from low levels of availability and accuracy of data that is purely descriptive to one where the data strategy is focused, trusted and supports complex predictive analytics can have a huge impact on performance.
Big Data, or Small Data/ Big Insights?
Across a race weekend, the amount of data generated by an F1 car is over a terabyte. So does this mean that more data equates to better performance? On the contrary. Instead of taking a Big Data approach, where large volumes of data are analysed to identify trends, patterns and correlations, F1 teams are looking for the smallest possible data set to support their needs.
The reasoning for this is that data production necessitates a sensor, which comes at the cost of weight and hurts performance. When we translate this into the wider context for the people in our companies, the ‘weight’ of additional data and information can hurt their ability to make the best decisions, which in turn affects performance. So how can we learn from the F1 approach to data generation? One crucial way is to use the RAPID Performance framework, which puts data at the bottom of the list when looking to make data-driven decisions:
- RESULTS – be clear on what you want to achieve and how you want want to achieve it (e.g. Championship points, whilst diving a sustainability agenda)
- ACTIONS – understand the key actions at your disposal that have the most significant impact on your Results (e.g. deciding when to pit your car in a race)
- PEOPLE – be clear on who the right people are to make decisions on your Actions and understand what drives them and how to support them (e.g. strategists, mechanics)
- INSIGHTS – decide what Insights your People need in order to support their decision-making (e.g. what is the impact of the decision to pit the car now, or later?)
- DATA – what is the smallest possible Data set required in order to derive the Insights that my People need? (e.g. position in race, tyre wear, competitor position, weather)
Learn more about the RAPID Performance framework, and other performance insights from the world of Formula 1, at www.paulteasdale.co.uk