The tied-on-points World Championship finale

This weekend Verstappen and Hamilton will battle head-to-head for the formula one World Championship. It’s going to be a battle on team strategy, car, and driver performance. A battle fuelled with smart big data strategies and zillions of data from sensors. Formula One is one of the most competitive and technology-intensive sports in the world. Data and analytics have become increasingly crucial for F1 teams because of how the sport has evolved over the years

Big data, machine learning & AI are this championship’s game-changers

Cars are supplying huge amounts of data through hundreds of channels, and it’s up to the teams to make sense of the volume, variety, and velocity of this data for the ultimate competitive advantage. The teams have implemented an advanced digital foundation with data analytics at its core. They use sophisticated data collection methods and the most advanced artificial intelligence and machine learning techniques coupled with a meticulous strategy, a holistic and unique approach to teamwork, and a data-driven culture for a sustained competitive advantage. Teams sift through billions of parameter combinations to find the fastest possible setup for that track, that day, that car, that driver. A performance group in the teams combines data sets to rebuild the racing cars every week for the best performance on the specific circuit. Moreover, with new cost cap regulations, extracting the maximum performance per dollar spent has become more important than ever in Formula One. Cost visualization with tree maps with zoom-in functionalities help the teams to better plan budgets, predict changing costs, and maintain regulatory compliance. Everything is fuelled by data! At racing day hundreds of sensors provide thousands of data points, from tire pressure to fuel burn efficiency, in real-time that are then analysed by race engineers onsite. The back rooms in the Red Bull & Mercedes racing garages at the Grand Prix look more like a NASA mission control room than it does a garage. There are more computer monitors than eyeballs in the room. Live race data is also transferred in under a quarter of a second through thousands of miles of fibre optic cables to other fully staffed operations rooms at the team’s headquarters in the United Kingdom. It’s going to be a high-performance battle which will be won by the smartest team.

Application of high-performance alternative data in business is lagging behind

Many companies have made great strides in collecting and utilizing data from their own activities. So far, though, comparatively few have realized the full potential of linking internal data with alternative data as the F1 teams do. Market research agency IDC found that the amount of data created and stored by humanity, every single day, is on track to multiply by 10 in 2025. This mind-boggling explosion in the Global Datasphere and advances in artificial intelligence & machine learning make data-driven business strategies and insights ever more powerful. Research agency Forrester found that 56% of corporate decision makers say their firm is expanding their ability to source external data. 21% of them says to do it in the next 12 months. Overlooking such external data is a missed opportunity. Organizations that stay abreast of the expanding external-data ecosystem and successfully integrate a broad spectrum of alternative data into their operations can outperform other companies by unlocking improvements in growth, productivity, and risk management. The COVID-19 crisis provides an example of just how relevant alternative data can be. In a few months, consumer purchasing habits, activities, and digital behavior changed dramatically, making pre-existing consumer research, forecasts, and predictive models obsolete. Moreover, as organizations struggled to understand these changing patterns, they discovered little of use in their internal data. Meanwhile, a wealth of external data could—and still can—help organizations plan and respond at a granular, real time level.

Alternative data success cases

Just like the F1 teams, financial institutions apply big data to make more informed investment or credit decisions. This, often outside the company walls, generated data is a subset of big data and often referred to as alternative data. Examples of alternative data sets include credit card transaction data, mobile device data, IoT sensor data, satellite imagery, social media sentiment, product reviews, weather data, web traffic, app usage and ESG (environmental, social and corporate governance) data. Hedge funds and other investors pioneer alternative data for over a decade. They augment conventional data sources like SEC filings and quarterly financial statements with newer, sometimes wildly outside-the-box data. Those streams now include everything from credit card transaction data and web-scraped social media to satellite imagery and IoT sensors. Investors apply natural language processing (NLP) to ingest alternative data to gain granular insights in their portfolio’s performance. Smart builders also pioneer alternative data. With all kinds of sensors, they collect ‘building & neighbourhood-performance’ data to optimize investment decisions and energy consumption. My personal interest in alternative data comes from the fact that I’ve also been pioneering alternative data in the platforms I have been building over the past decade. I used psychometric data, social media data, mobile & geo-location data, browser events and biometry for granular predictive insights that advance credit decisioning and customer activation across 13 geographies. Also, I passionately enjoyed developing a bespoke field-hockey high performance model combining streams of data on individual player’s skills, skills effectiveness, tactical performance, endurance, and motivation for a top 3 Dutch field hockey club. All-in-all I have become increasingly passionate about alternative data.

Challenges with applying alternative data

While researching and playing with alternative data I learned that, although alternative-data sources offer immense potential, they also present several practical challenges. To start, simply understanding the business problem properly and defining the right use cases in this new brave world is already challenging. A lot of organizations I know don’t design data-architectures to feed and scale desired high value uses cases but are less performing just analysing the data they’ve got. Also, simply gaining a basic understanding of what’s available fitting your use case data-architectures requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Thousands of data products can be obtained through a multitude of channels—including your customers, their intelligent devices, data brokers, data aggregators, and analytics platforms—and the number grows every day. Analysing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalization of external data often requires updates to the organization’s existing data environment, including changes to systems and infrastructure. Change legacy or rebuilt decisions are to be made and the right software and cloud partners are to be chosen. Companies also need to remain cognizant of privacy concerns and consumer scrutiny when they use some types of external data. Pioneering alternative data over the past decade I learned these challenges are considerable but surmountable.

So, what can high-performance leaders learn from this?

What Formula One and the other success cases teach us is that harnessing the power of alternative data always is use case driven. Big data quality or competence building programs usually are a waste of money. Focused and agile development of impactful use cases drive high performance value. The second learning of the success cases is to start fast but simple and grow iteratively. As you step in a world in which you don’t know what you don’t know, fast learning and flexible development are key for success. Building learning systems is a third insight from the success cases. The F1 teams transformed their cars into learning systems, smart builders are changing their real estate into learning systems and in most high performing internet platforms the platform usually is the learning system. The key to competition in this data-driven world is to learn and adapt faster than your competitors. This weekend we will witness an ultimate high-performance battle fuelled by alternative data intelligence. Usually I would say, “may the smartest win”….however this Sunday I’m Dutch!