One of the most surprising music industry trends in recent years, and one that many casual listeners might not be aware of, is the overwhelming, all-consuming influence of Big Data on making important predictions—about trends, popularity, and even major awards shows.

To a music fan streaming their favourite album on Spotify, data analytics might not seem all that important, or even particularly interesting. To those in the know, however, there are few business strategies that provide such crucial and relevant information about current and future trends.

The use of data in consumer marketing is nothing new, of course; the music business has been driven by careful review, educated predictions, and heavily publicized trends since the very early days. Long before computers even existed, labels took note of what had seemed to work in the past and applied it to new artists, promoting them in a very specific and commodified way to ensure maximum profit.

Whether this is an ethical practice is a discussion for another time. Regardless, no matter what business you’re in, statistical analysis works. It’s not artistic, it’s not interpretive—it’s just straight-forward, old-fashioned number-crunching, perhaps an appalling idea for many musicians who dream that the industry is supportive of the arts and based around authentic creative expression—rather than simple customer preference.

What is Big Data, exactly? It refers to a very old, tried-and-true process of examining and analyzing very large amounts of information. Data analytics experts mine massive swarms of data for subtle hidden patterns, seek out unlikely and surprising correlations, and make educated guesses about the future of the global marketplace.

It might actually be the most subversive strategy for the music business to implement right now, at a time when sales don’t seem to be motivated by artistic appreciation alone. Consider Spotify’s “Related Artists” section, which connects certain artists that tend to attract similar fanbases, and expose listeners to brand new music that might fit their tastes. A business intelligence application known as Hadoop BI, and a workflow management platform called Luigi, are the two guiding forces behind real-time data analysis on Spotify.

You’ve probably also noticed the trend being utilized en masse on other major social networks. Twitter recommends users you may enjoy following based on your previous interactions and engagements with individuals and brands. Facebook has always used the “People You May Know” feature to encourage you to friend people who you share social connections with.

Big Data has revolutionized virtually every industry. Even small business owners are finding it to be advantageous in the technological era. However, few companies are using it more to their advantage than Spotify.

Being able to make predictions about consumer likes and dislikes is advantageous to the continuing success of the long-suffering music industry, which is currently undergoing an optimistic reinvention thanks to the 24 million Spotify users. Labels, publishers, and distributors are at the mercy of data, since it allows them to predict emerging trends and capitalize on them.

In 2016, when the music industry is only beginning to start making significant money again—thanks to Spotify’s unique data-driven platform—number-crunching and predictive, consumer-driven information is more useful than ever, but what many people don’t know is that real-time analytics do more than simply tailor user’s listening preferences and recommend cool new artists to people streaming on their mobile phones or laptops.

Big Data goes one step further and promotes real-world engagement with musical artists. Spotify permits experts to make highly accurate predictions about how music fans engage with their favourite artists outside of their bedroom.

Festivals book artists that they know are popular on online streaming platforms; they feel certain that music fans will pay money to attend festivals with lineups that feel personal. Extremely large and highly anticipated annual festivals like Coachella are even more highly curated than most, and a lot of expense goes into planning them. You can be certain that Big Data plays a significant role in deciding who will play the main stage in April 2017.

If you’ve ever read a festival lineup and seen so many of your favourite artists’ names on the roster that it felt like they had specifically designed the festival for you, it might be because, in a way, they did—or, more accurately, they reviewed the largest number of people’s tastes and compiled a lineup that would make them the most money.

Which brings us back to the Grammy Awards. Spotify is capable of making predictions about everything from festival headliners to concert attendance, so it really shouldn’t come as much of a shock that Spotify was successfully able to predict the winners of four out of six Grammy Award categories.