How data-led insights defeat competitors and fight climate change

Tight city circuits, rapidly changing course conditions, and 24 drivers with an eye on the finish line – and their battery power.

Formula E is motor racing with a purpose. The world's first all-electric, single-seater championship combines an exhilarating fast-paced contest with a social mission that unites teams, partners, and fans around the globe. And for Envision Racing, data, analytics, and the Race Against Climate Change are at the heart of the team's work.

Motor racing has relied on performance-based analytics for years. But Envision Racing is taking the combination of data, advanced technologies, and human intelligence to a deeper level.

The team is enhancing its motorsport know-how with Genpact's digital technologies and data skills to power a new breed of intelligence and decision-making. This approach not only helps the team win points on the track but also targets and attracts new fans and pursues its bigger goal: the Race Against Climate Change.

The foundations of the partnership between Genpact and Envision Racing go further than simply implementing technologies, such as artificial intelligence (AI). The Envision team already had a strong talent base. "More than half my engineers are software and data experts," says MD and CTO Sylvain Filippi. "They don't touch the cars. All they do is look at performance data to manage energy and make our cars go faster."

By bringing together our skills in digital transformation with the team's racing knowledge, we're unlocking augmented intelligence. And through the strength of our relationship, we've nurtured trust between the teams and trust in the insights our solutions deliver.

We're helping the team make rapid, data-driven decisions. We're creating the world's first instinctive racing team.

Our partnership began in late 2018 with a goal to help the team sharpen its performance with data. The work and projects we focus on are driven by the team's objective. "Ultimately, it's all about lap time," says Gil Abrantes, strategy engineer at Envision Racing. "We focus our time on where we can impact it."

Heading into season five, we quickly helped the team address a new challenge. Formula E regulations had just shifted to a timed race of 45 minutes plus one lap, and the key to success lay in a blend of data, AI, racing expertise, and engineering knowledge.

To manage the speed and energy consumption of its battery-powered cars, Envision Racing's engineers had to accurately predict the number of laps in a race.

This is where we stepped in. We built an AI-based scenario engine, the Lap Estimate Optimizer (LEO), to inject greater precision into the team's lap-count intelligence and guide the team's racing strategies that season. LEO's algorithms assessed thousands of potential scenarios that could affect a race, such as overtakes, accidents, or sudden rain or hail, to understand how many laps there would be in a race.

With more precise predictions – especially when there were changing track conditions as there were in Paris when the drivers tackled rain, sunshine, and hail all in one race – engineers and drivers gained the insight they needed to balance car speed and energy consumption for superior performance.

By extracting competitive advantage from data, LEO solidified the partnership between Genpact and the team.

We then brought our analytics skills to the team's simulator, where drivers spend countless hours practicing for each race to perfect their lap times.

"The simulator plays a big role for me," says driver Robin Frijns. "It's all about preparing for the race weekend and getting the best out of the car's powertrain. The simulator has all the information on corner speeds and more so we can recreate the car, track, and conditions very close to reality."

The simulator hones the driver's performance and generates essential engineering and race-preparation data. But manually extracting useful insights from the enormous volumes of data was time-consuming and inefficient.

Cracking the data challenge

We built Augmented Race Intelligence to quickly generate customized reports and data visualization for engineers and drivers so they can act on the insight based on their racing knowledge. Now the team can compare and build on each driver's performance to improve racing strategies and speed, perfect braking points, and secure an edge over the competition.

"With Genpact's platform, it's easy for us to see where we lose time and where we can gain time so we can adapt our lines and driving style to the track," says Frijns.

Augmented Race Intelligence is tailor-made for the time-pressured racetrack environment, especially as schedules had to change due to COVID-19 restrictions, often forcing multiple races into a single weekend. Speed of analysis means that the team can apply the lessons from one race to the next race in less than 24 hours.

This analysis used to take days. Now Envision Racing can get new insight in a fraction of the time. "Within hours of each race, Genpact's advanced analytics skills and technology help us unlock valuable information that would otherwise have been out of reach," says Abrantes.

As our relationship with the team evolved and its faith in our capabilities grew, our work ventured beyond the racetrack.

Experience-led businesses – including motorsport teams – create a competitive advantage by looking at their organizations from the outside in through the lens of the customer or fan. For Envision Racing, attracting a growing base of loyal fans around the world is just as important as winning races. And – once again – data-led decision-making is at its core.

By helping the team develop a deeper understanding of its current and potential fans, it could serve them better, nurture affinity, enhance the fan experience, and identify untapped opportunities.

Through Rightpoint, a Genpact company and leader in experience, we rolled out an experience-led research approach with Envision Racing's marketing team. By analyzing a large global dataset, we created detailed personas that revealed fans' media-consumption habits, social-media behavior, and brand preferences. This insight validated some of the team's hypotheses about its audiences and spotted ways to reach new people – for example, in Asia Pacific.

We distilled all of our findings into quick-reference tools the team uses to guide marketing decisions. "The insights we uncovered make it much easier for us to develop more personalized experiences for a variety of fans," says Daniel Matson, head of marketing at Envision Racing. “And, as every decision we make is now backed by data-driven insights, we can act with confidence."

We then turned from the team's fans to the competition.

The radio exchanges between drivers and their engineers on energy status, strategy, or car issues are treasure troves of insight, even if they're short and often in code. We built the Radio Analytics Engine (RAE) a platform that quickly processes each recorded radio stream from a race into relevant clips. It gives the team insights into how rivals manage energy or when they might use attack mode – an extra boost of energy available to all drivers. And with natural-language processing, we are automating how RAE sorts clips, so the team gets even faster access to vital competitive knowledge during a race.

But to maintain speed, the technology architecture was critical. As the Formula E championship moves from city to city, internet speeds cannot be guaranteed. To avoid the risk of slow data processing, we didn't build RAE in the cloud. With RAE on-premise and on the team's servers, engineers and drivers run their analysis trackside, trouble-free.

As Abrantes says, "We wouldn't be able to pull such valuable insight from such high volumes of unstructured data at speed and scale without Genpact's skills with digital technologies and data."

While podium places elevate Envision Racing up the Formula E rankings, the team is also battling to raise awareness of the most important race on Earth: the Race Against Climate Change. In 2020, Envision Racing demonstrated how it lives its purpose by becoming the first certified carbon-neutral team on the grid. But reporting on its emissions data took time and was error-prone until we introduced automation, helping the team collate and analyze data faster to maintain its coveted status.

We're now taking this work a step further by creating a carbon calculator so each team member can make emissions-conscious travel decisions throughout the year. Envision Racing truly is the greenest team on the greenest grid.

The success of our partnership relies on robust foundations.

Clear governance provides transparency into Envision Racing's many projects, requests, and updates. We use development platform GitHub to collaborate. And we run daily meetings, testing, and open communications across time zones.

Our work doesn't end when we finish a solution. Technology is the easy part. It's transformation that's hard. We use our knowledge of organizational change and human behavior to embed new technologies and approaches into the team's daily work for the long term.

For example, previously, the drivers would rely more heavily on gut feelings when racing. But by responding to both the team's and individuals' goals, we could weave the dashboards and insights from Augmented Race Intelligence into existing strategy discussions between drivers and engineers ahead of each race. And as trust in the solution's findings grew, the team no longer felt the need to test and validate the findings. Now, engineers and drivers rely more heavily on the combination of their intuition with our data-led insights when making race-day decisions.

Organizations across industries as well as sports teams can build on what we've learned together:

1. Access to real-time insights and decision-making are crucial for Formula E teams. For companies, having on-demand insight from contact centers or points of sale during a campaign will dramatically improve the customer experience. But first, you must eliminate any latency between data generation, decision-making, and action. Start by identifying and removing data bottlenecks and streamlining your data architecture

2. Take a loosely coupled approach to system design to allow for future integration with analytics and machine learning. This approach gives you room to experiment and make improvements to meet changing needs

3. Data-led insights must be precise, dependable, and repeatable. Test your models across all scenarios and datasets to generate reliable results at speed

4. Develop strong partnerships internally and with third parties by:

• Enabling swift communications, acting on deliverables quickly, and addressing issues openly to find fast solutions
• Building trust in data and insight with ethical models that eliminate bias

5. When making the shift to a data-led business, make change management a constant theme – not a one-off activity. Keep updating metrics and establish KPIs to measure the adoption of analytics tools

6. Expose your team to new areas of expertise and encourage them to follow current trends and techniques in distributed computing. Consider establishing a center of excellence to build a critical mass of skilled resources. Teams with broader skill sets are more productive, have faster turnaround times, and create a competitive edge

Envision Racing has a clear mission: to succeed on the racetrack and accelerate action against climate change. Our partnership provides the digital technologies, analytics, and experience that enable the team to make the right rapid decisions when they count. This is instinctive racing.