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Data is at the heart of success in industries ranging from sports to product development. But is this reliance on data-driven decisions pushing us into a creative corner? Let’s look at how different industries use data analytics and what the outcomes have been.

Data in Premier League Football

As an Arsenal fan, I’ve experienced the ups and downs of supporting a team that have struggled for over 15 years before finally returning to challenge for the Premier League. Much of this resurgence can be credited to Arsenal’s use of data analytics. Under Mikel Arteta, Arsenal has embraced a data-driven approach, supported by a large team of data scientists and a bespoke analytics platform.

If you follow the sport, you’ve likely noticed a slew of new statistics; expected goals (xG), expected assists (xA), average player positions, progressive passes, field tilt, and more. These stats, which barely existed five years ago, now play a crucial role in how the game is analysed and played.

Companies like Opta collect over 3,000 data points per match using cameras, wearable technology, and even sensors in the footballs. This vast database helps clubs worldwide make informed decisions on player acquisitions, tactics, training plans, and even in-game substitutions. But what exactly do football teams do with all this data?

Minimising Risk

Arsenal has used this previously unavailable data to craft a strategy that minimises risk while maintaining a goal threat. Our tactics focus on preventing high xG situations for opponents and ensuring our players only take shots when the xG percentage is in our favour. Manchester City, our main rival, employs a similar approach, and increasingly, other teams are following suit.

As data has become integral to game plans, managers have urged players to take fewer risks. One example of this is to tell players to only shoot from positions with a high conversion rate. Over the past decade, the average distance of shots on goal has decreased by 1.5 yards, while the shot conversion rate has increased by around two percent. This trend shows no signs of slowing down.

But is this data-driven, risk-averse strategy the best way to play? Is it good for the viewer? There seems to be fewer spectacular long-range goals, with top strikers like Erling Haaland and Harry Kane scoring predominantly from tap-ins and short-range headers. As bigger teams dominate games with these strategies, one-sided matches have become more common. Manchester City has now won four Premier League titles in a row using this approach, and it wouldn’t surprise many if they won the next 4 as well.

And the NBA?

A similar trend is evident in other sports. Data science became widely used in the NBA around 15 years ago, and since then, the game has changed dramatically. The frequency and location of shots have shifted, with 3-point attempts nearly doubling over the past decade.

Year / TeamAverage 3 Pointers Attempted Per Game
2000 League Average15
2009 League Average17
2018 League Average29
2023 Boston Celtics (League Winners)42
NBA 3PA Over the years

In 2000, the league average was around 15 three-point attempts per game. By 2023, the Boston Celtics, who won the league, averaged 42 attempts per game. Even the lowest number of 3-point attempts recorded in this season’s NBA was 30. As teams increasingly rely on data to guide their shot selection, the game is becoming more predictable and less varied. The data shows the teams that take more long shots are more successful therefore all teams are now taking more long shots.

What about Music?

The music industry too has changed with the increased usage of data. Record labels use analytics to identify potential hits, often looking at what has been successful in the past and replicating it. Streaming algorithms then push these songs to listeners, reinforcing their popularity and leading to a cycle of increasingly generic music.

Even artists who started with more unique sounds, like Ed Sheeran or Coldplay, have seen their music evolve into something more mainstream and formulaic over time, driven by the data-driven demands of record labels.

The UK’s Top 40 Chart today reflects this trend, with little variation in music genres and multiple entries by the same artists each week. At the time of writing, the chart includes three songs by Chappell Roan, three by Charlie XCX, two by Billie Eilish, two by Post Malone, and two by Teddy Swims. As music becomes more generic, fewer artists get the spotlight, and those who do often end up producing similarly generic tracks.

So what does this all mean?

We’re stuck in a cycle that leads to generic and ‘safe’ solutions, rather than the best or most innovative ones. Looking back at football, is this data-driven, risk-averse approach really the best way to play? Probably not. But because it’s proven successful, data points others toward adopting it. As more teams follow this method and abandon other tactics or new developments, the diversity of tactical data shrinks, narrowing the scope of possible strategies.

This creates a feedback loop where innovation is stifled because data increasingly favours a narrow set of ideas. In Workplace Technology / IT, the same dynamic applies. Companies rely heavily on analytics to determine which tools to adopt, how to optimise workflows, and how to shape company culture. This data-driven approach can lead to efficiency gains, but it also risks creating a homogenised workplace where creativity and innovation are stifled.

Technological impact

This trend extends to the technologies we use in IT as well. SaaS solutions have become the backbone of modern business, but many providers rely heavily on user data and feedback to shape their product roadmaps. While this ensures user-friendly products, it often results in incremental updates rather than ground-breaking innovations.

As a result, many SaaS tools offer similar features, interfaces, and functionalities, prioritising refinement over reinvention. Categories like project management, CRM, and collaboration tools are flooded with options that are virtually indistinguishable from one another.

Homogenisation of SaaS Solutions

Because these tools evolve based on the same data trends and user demands, features across platforms become homogenised. This limits choices for businesses and stifles the competitive drive that typically fuels innovation. Instead of bold, creative solutions that could disrupt the status quo, we get safer, predictable updates that cater to the majority but fail to inspire.

As businesses adopt these similar SaaS solutions, they inadvertently contribute to a cycle of conformity, where innovation is sacrificed for consistency and safety. This, in turn, reinforces the very trends that stifle creativity in both the products and the companies that use them.

Summary

The data that once promised to unlock new possibilities now limits creativity and diversity of thought. We may optimise for short-term gains, but at the cost of long-term innovation, potentially missing out on transformative strategies that don’t yet have the data to back them up.

In essence, while data is a valuable tool, over-reliance on it can lead to a stagnation of ideas, where the pursuit of ‘safe’ choices blinds us to the possibility of something truly innovative. Don’t always follow the data. Try new things on a small scale, support start-ups approaching problems in a new way, and never stop being creative!

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