Will Big Data Trump Big Ears

Historical Note: Originally published in 2018, this essay asked whether big data would eventually rival human instinct in artist discovery. Eight years later, predictive analytics, streaming data, social signals, and AI-assisted tools have become part of modern A&R. The original argument has been preserved below, with new 2026 updates examining what the industry learned—and what data still struggles to understand.

The music industry is filled with talented creators. There is even more talent lurking in high school hallways and the furthest reaches of the World Wide Web. The music industry has made it its point to discover and profit from each individual that can carry a tune. However, they cannot get to them all and surely cannot predict which song will rock the Billboard. Artists like Drake and Rihanna make it look simple, but it’s not. The human ear is limited to their owner’s bias, but big data isn’t.

Big Data is on the way

There are several emerging tech companies gathering and analyzing large amounts of data to predict the next superstar and billboard smash. These companies promise to provide industry A&R’s, playlist curators, and music supervisors with the data they need to do just that. But how accurate can this data be? Could it really project the next breakout sensation?

One company called Hitwizard compares sound arguments against airplay data sourced from Dutch radio and the local Spotify charts. There are numerous studies revealing that people like songs that sound familiar. Hitwizard claims their AI has a 66 percent chance of predicting the next hit, which to me seems like a fair estimation from the data I have gathered. It has also been noted that Hitwizard has a 93% chance of predicting a flop. Yikes.

TuneGo, another emerging company in the field, factors in artists’ online activity, fan engagement, and their industry response when determining their chances of making it mainstream.

2026 Update: Prediction Has Become Far More Sophisticated

When this article was originally published, predictive music analytics was still an emerging field. Today, companies analyze streaming growth, playlist activity, social engagement, audience development, and millions of artist profiles to identify potential breakout talent.

In 2024, Chartmetric introduced a predictive artist-discovery system built from signals across more than 10 million artist careers, assigning artists daily scores based on indicators associated with future success. What sounded experimental in 2018 has increasingly become standard infrastructure within the modern music business.

Big Data is already in use

Spotify, a data-driven streaming service that recently went public, owes most of its success to big data. Their playlists are the most listened to in the world, making a spot on one highly coveted by artists. These playlists are curated with the help of big data. It has been reported that a relatively unknown artist can boost his or her streams from 50-100% by landing a song on one of their lists.

So obviously big data works. But like everything else, it’s not without flaw. New trends in music that would intrigue humans will escape big data predictions. Also, we as people do lean towards familiarity, but we evolve and change as we age and our children become the new buying power. This is how rap music took over as the new pop!

Music isn’t just numbers

In music, machines and data can assist, but people will always have a role to play. There are experiments going on as I write this that will have AI not only predict the next star but generate music for performers¹. Though I am a self-declared futurist, as a music enthusiast, cannot view this as something good for the industry or the audience. Music has an intangible, almost spiritual quality to it that will never be mimicked by machines. People feel people. We relate to one another. It is my theory that these emotions transcend the pen and the radio to penetrate directly to our souls. Music motivates, inspires, and gets us through life’s ups and downs, and that’s because real people have experienced it and shared their experiences through lyrics and delivery.

What do you think? Will Big data eventually replace Big ears? Leave a comment below and get the conversation started.

2026 Update: Data Didn’t Replace A&R. It Changed A&R.

Eight years later, perhaps the most interesting answer to this article’s original question is that big data didn’t eliminate “big ears.” Instead, it changed the job.

In 2026, Sony Music Nashville advertised for an A&R Analyst tasked with using data-driven methods, statistical insights, social activity, and predictive metrics to help discover both signed and unsigned artists. The modern A&R professional increasingly operates somewhere between tastemaker and analyst.

The question is no longer whether the music industry will use data to discover artists.

It already does.

The better question is how much influence that data should have over human judgment.

Eight Years Later, the Question Remains

Big data didn’t kill the human ear. It gave it another tool.

The music industry can now measure artist momentum with a sophistication that would have seemed extraordinary when this essay was originally published. But numbers still struggle with the same fundamental problem: they can tell us what audiences are doing without always explaining why something matters.

Perhaps the future of artist discovery doesn’t belong to data or instinct alone. It belongs to the people who understand how to use both.

This essay is part of The Intellectual History of Thrive Indie, a living collection tracing how Thrive Indie’s ideas about music, technology, culture, and artist careers have evolved over time.

1. 2026 Update: What was experimental when this essay was published is now part of the music industry’s reality. AI-generated music, synthetic artists, generative production tools, and machine-assisted songwriting have moved the debate beyond whether technology can predict successful music. We are now asking whether technology can create it. ↩︎