Data Science for Agile Strategy: From Formula 1 to the Boardroom

Business & Industry
Location: King's Suite Level: Non-technical
Average rating: ****.
(4.00, 2 ratings)

Formula One is arguably the pinnacle of motorsport, played out by 12 teams in 20 races in 19 countries per year with development budgets of leading teams reaching $300m pa and a global audience of 165m per race, 2nd only to the Olympics and the World Cup.

In this hyper-competitive environment differences are measured in hundredths of seconds and how you use data to inform decisions can win or lose you races. We demonstrate how race strategy uses telematics (some 1,700 data channels), live timing and GPS data to track circuit position, and even broadcast TV to uncover intelligence on your competitors setups to plan, track and recalibrate strategy throughout a race in real time.

We’ll show how the techniques and principles used to underpin Race Strategy have translated successfully to industry. These include:
1. Acquiring relevant data
2. Recognising hidden patterns
3. Leveraging insights in real time
4. Continuously evolving your strategy
5. Using data to reshaping your competitive landscape

We’ll give examples of how these have created advantage in strategy development, product direction and organisation design.

We’ll share our tips and manifesto for corporate leaders to get started:
1. It’s not just about the analytics, the ‘head’ and ‘tail’ of clarifying the strategic intent and creating a (often visual) narrative of the analytics are telling are critical for capturing advantage,
2. Avoid grand schemes and start with want you have, embracing both ‘hard’ (i.e. facts) and ‘soft’ (i.e. garbage) data. Most strategic decision making is forward facing and therefore inherently uncertain, so how you gracefully deal with poor data is a source of advantage.
3. Create fast feedback loops; firms are recognising that strategy and execution are not sequential, but instead run in parallel. So they to get smart in picking up signals, interpreting them and reacting – feedback at strategy, product and organisation level drive this capability to learn and adapt.
4. Be temporal, treat all data and numbers as timeseries so that they can be flexed, stressed, updated as events unfold around you. The plan will be wrong, so the ability to continuously search for new scenarios and adapt your strategy gives a real edge (and a very different culture).
5. Strive for the human touch, we’ve found visualisation and the design of how decision-makers consume intelligence is supremely powerful.

Photo of Simon Williams

Simon Williams


Chief Executive and Co-Founder of QuantumBlack, a Data Science agency that blends strategy, analytics and creative to help firms derive insight from data to make faster, smarter decisions.

Client projects include Boeing to improve yield in strategic R&D investment, McKinsey & Co in developing cutting-edge business analytics, Microsoft in creating new visual language for biological research and several Formula 1 racing teams building race strategy engines.

Prior to QuantumBlack led several data driven start-ups including SmithBayes, a spin-out from McLaren the Formula 1 racing team. Started career in real-time trading systems at Reuters and ABN AMRO.

Photo of Jacomo Corbo

Jacomo Corbo


Jacomo Corbo is the Chief Scientist for QuantumBlack, a Data Science agency that helps clients meet the analysis challenges of big data to make better decisions.

Corbo is also the Canada Research Chair in Information and Performance Management at the University of Ottawa and a Wharton Clayright Scholar at the University of Pennsylvania’s Wharton School of Business. His research has been funded by grants from the National Research Council, the Alfred P. Sloan Foundation, the Wharton Mack Center for Technological Innovation, the Wharton Customer Analytics Initiative, as well as by companies such as GE Finance and IBM.

Between January 2006 and June 2008, Corbo also served as Race Strategist and subsequently as Chief Race Strategist for the Renault F1 Team Ltd.

Corbo holds a Ph.D. from the Department of Computer Science at Harvard University, an S.M. in Applied Mathematics from the Harvard Graduate School of Engineering and Applied Sciences, and a B.Eng. in Electrical Engineering from McGill University.


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