January 12, 2024

What you can learn from the NBA about the 6 stages of data analysis

The NBA's data evolution showcases iterative analysis and Azure partnerships for better coaching decisions. Their cloud move highlights scalable data infrastructure's importance in unlocking AI potential for businesses.

The NBA was formed in 1946, marking the beginning of basketball as we know it today. Jump to 2023, and the 70-year-old organization is undergoing its next evolution—this time into the world of data analytics and AI.

With AI predicted to contribute a staggering $15.7 trillion to the global economy by 2030, the NBA isn’t the only one looking to take advantage. A recent Gartner poll showed a 45% increase in AI spending from company executives. Yet, when it comes to data-driven decision-making, it can still be a struggle to get it right.

That’s why the six stages of data analysis—ask, prepare, process, analyze, share, act—are best envisioned as a feedback cycle rather than a linear process. One round informs the next, and how you act will decide what you get to ask next time around, just like AI analytics helping NBA coaches better understand court play and strategy between games.

So, what can the NBA teach us about data analysis and how to unlock the value of AI? Let’s explore.

From Ask to Act: You’ve Got To Be in it To Win It

It wasn’t until 2020 and the completion of the NBA’s migration to the cloud, that they were fully able to deploy the benefits of digital transformation. Only by partnering with Microsoft Azure could the league launch NBA CourtOptix, a real-time information platform with greater statistical insight into every shot, pass, and play that generates roughly 10 million data points per game, to be shared with teams and fans alike.

With so much more data available, the possibilities for the ‘ask’ stage are greatly expanded. From these data points, coaches can look at traditional metrics like points or rebounds while also delving into more advanced statistics: On/off court plus-minus ratings, shooting efficiency under pressure, and defensive impact. By migrating to the cloud and investing in the ‘prepare’ and ‘process’ stages of data analysis, the NBA has armed coaches with more valuable data-driven insight into how to act next.

For example, take one hypothetical scenario: It’s late in the regular season, and the fictional Austin Supersonics’ coaches are looking to gain a competitive edge. Using data from recent games, the coaches notice a player who’s usually on the bench with an unusually high plus-minus rating, meaning his team outscores the opponent whenever he’s on the court. Despite his low individual scoring average, the team seems to perform better when he’s playing. Thanks to this information, the coaches decide to tweak their rotation strategy, giving this player more minutes and using this effective lineup in crucial game moments.

In this instance, the NBA is illustrating how advanced analytics can help take control of the data available for maximum impact on decision-making at the ‘act’ stage of data analysis.

Unlock Value at Every Stage: Maximize What You Have Available

When it comes to AI, the problem isn’t that companies are hesitant to spend; it’s that almost 85% of AI projects fail to deliver. Companies that do not invest in their data maturity, with healthy pipelines accessible to trained staff, are unlikely to see the successful implementation of their AI strategies.

It wasn’t until the migration to the cloud that the NBA could fully capitalize on the data they had available. As Sydney Sarachek, Director, Stats Technology Product Development at the NBA, said: “Being able to spin up more compute when we need it during games is crucial. We can leverage Azure’s compute and best-in-class machine-learning capabilities without investing in those same resources 24/7.” It’s the cloud’s scalability that is the real cost saver for the NBA and demonstrates the value of having secure and scalable data pipelines in place.

For businesses, out-of-date legacy systems can also be overly costly and create stifled workflows as they rely heavily on IT departments to access data. The cloud enables businesses to adopt a decentralized approach and modularity to scale operations. As such, business intelligence is improved, and the analyze, share, and act stages of data analysis can happen faster as business teams have real-time access to data on their own, without bottlenecks formed by IT departments.

Like in other sports, basketball game statistics have always been big news. However, it’s only now that fans can access the same data as teams. As a result of the cloud migration, the NBA has unleashed the possibility for the ‘share’ stage of data analysis to improve the fan experience and keep them watching.

From ‘ask’ to ‘act,’ the NBA recognized the need to move to the cloud and expand its data analysis capabilities to successfully integrate feedback into the decision-making processes. Without a doubt, it’s a move all businesses preparing for AI should prioritize if they want to unlock the value of data analysis at every stage.

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