Data is everything in the music business. It can tell you all you need to know, but it too often stays silent. Fragmented across file formats and platforms, music pros don't know precisely what they have, what it's worth, what they're owed, and how it connects to everything else. 

Spindl (spindl.co), an LA-based music data startup, has perfected the art of getting data to talk at scale, revealing new potential revenue, faulty or incomplete information, and the real value of huge catalogs. Its data wrangling methods let Spindl offer equally powerful tools for publishing as well as recording data, setting it apart from other platforms.

Music, with its two sides of copyrights and widely scattered stakeholders, has long suffered from an acute inability to manage and analyze the terabytes of data flowing from usage across the internet and around the world. This interferes with the swift and accurate payment of songwriters, producers, and artists. Though everyone agrees the system needs to improve, every company has its own approach and format for dealing with the data flood. 

Just emerging from stealth mode, Spindl promises to channel the flood, offering simple but potent ways to unveil data's hidden secrets. It works in minutes, not weeks, and catches patterns humans struggle to find, empowering music professionals in a wide range of roles to do more with data. The service has won over major investors and executives in music and media, and run pilots that valuated catalogs, found inaccurate metadata, and uncovered unclaimed revenue for large rights holders. Now it's ready to offer its groundbreaking technology to more music industry customers.

Spindl sprang from the experiences of Jon Wienner, who got his start as a young DJ jetting to gigs around the US, while attending NYU. Wienner's music career took him to LA, where he dived into songwriting and production, signing with publisher Warner Chappell and working with artists such as Justin Bieber, Selena Gomez, J. Balvin, Demi Lovato, Zara Larsson, and Dominic Fike. 

Things were blowing up for Wienner. Then came Spring 2020. The music industry ground to a halt for several months, and Wienner, with more time on his hands than he wanted, began to take a closer look at his royalty statements. "I really dug into them, but they didn't make any sense," he recalls. "I wanted to know why the whole royalty process sucked, but no one could answer that question."

Wienner set out to answer it, eventually deciding to try and tackle some of the most painful points in data and payments. He crossed paths with Andrew Krueger and Matt Moody, two software engineers and entrepreneurs who had previously sold a fintech startup. Together, they decided to apply data fusion and inference models to music data. The results were exciting. "Every single organization handles data differently and may have a slightly different format," explains Wienner. "We built a way to make it so that none of that mattered."

Wienner jokingly compares Spindl's process to that of a wood chipper, but one that keeps the entire forest in mind. Spindl's technology can ingest nearly any data source, whether file or database, and extract relevant data points. Preserving the context, it connects the dots with minimal input or reformatting thanks to AI, regardless of format or language. Spindl then lets users interact with this data via a semantic interface, allowing even a non-technical user to easily extract what they're looking for.

Spindl can also identify a composition based on audio similarities and lyrics, even when lyrics are in a language different from the original composition, making it possible to discover new versions. "While it's been possible to find a recording and attribute it for some time, publishing hasn't benefited from that technology as much. Our technology fixes that," notes Wienner. "Lyrics are key, because it doesn't matter if you change a chord or the key of the song, or if there's background noise, the lyrics usually match. That's allowed us to find compositions everywhere they might pop up, including in UGC or social posts."

This means a team can finally hear what their data has to say. They can properly attribute a work or match a recording with a composition. They can discover previously unknown derivative works. They can claim their royalties efficiently and get all the data holes plugged. This can save hundreds of work hours and millions of dollars. 

"Spindl is here to make sure people don't have to dedicate their entire workdays to manual processes and painstaking, tedious tasks, when they could be thinking strategically or getting more creative," reflects Wienner. "We want to put more power into everyone's hands, so they see insights in the data they already have. These insights and the decisions they inspire promise to grow the entire industry."