In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify. Prior to joining Spotify, she led data teams at the NY Times and at Apple (iTunes). Having led teams at three different companies, I wanted to hear her thoughts on digital transformation, and I wanted to know how she approaches the challenge of building, managing, and nurturing data teams.

I also wanted to learn more about what goes into building a recommender system for a popular consumer service like Spotify. Engagement should clearly be the most important metric, but there are other considerations, such as introducing users to new or “long tail” content.

Machine Learning at Spotify: You Are What You Stream

Originally from Glasgow, Scotland, and now residing in Oakland, CA, Ed Marco's twenty-five year journey in underground music has brought him rewards beyond his expectations, traveling the world many times over with his record bag.

The Bay Area resident and Foundation Artists founder began his career with his first residency at The Pheonix Club in November ’96. Through the late nineties, quickly became a regular at Glasgow’s after-hours warehouse parties, bringing his signature sound of upbeat, techy, funk to the clubbers of Scotland. In 2000 Ed relocated to New York City where he held a residency at the Village Lantern, officially launching his international career and extending his reach to many different parts of the globe. 

In 2008, he launched Fiasco Events, hosting monthly parties in Glasgow. Over the next few years, the Fiasco brand grew internationally, and by 2013, Ed had hosted over 100 events in Glasgow, London, Ibiza, Amsterdam, NY, Chicago, SF, Miami, Amsterdam, and many other cities around the globe.

Ed’s renowned capability to take an audience from listener to sweaty breathless dancer has earned him a faithful international fan base from Ibiza to Chicago, and Miami back to the UK.
Through their journey, the listener is taken on a rewarding voyage from Down-tempo to Disco to roller-boogie skate jams that glide smoothly through the realms of House.

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Machine Learning at Spotify: You Are What You Stream

O'Reilly Radar