Turning Sound Into Information: Warner Music Group's Kobi Abayomi

**Sam Ransbotham** (0:01)
We hear a lot about how companies can use AI to work more efficiently and improve profitability.
At Warner Music Group, the company achieves those goals by helping customers discover the music they like the most. Find out how AI can help bring music to your ears in today's episode.

**Kobi Abayomi** (0:17)
I'm Kobi Abayomi from Warner Music Group, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:22)
Welcome to Me, Myself, and AI, the podcast on artificial intelligence in business.
In each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Information Systems at Boston College, and I'm also the guest editor for the AI and Business Strategy Big Ideas Program at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:40)
And I'm Shervin Khodabandeh, Senior Partner and Managing Director at Boston Consulting Group.

**Kobi Abayomi** (0:46)
Nice to meet you.

**Shervin Khodabandeh** (0:47)
Welcome to the show.

**Kobi Abayomi** (0:48)
Thank you.

**Sam Ransbotham** (0:49)
Shervin and I are excited to be talking today with Kobi Abayomi, Senior Vice President for Data Science at Warner Music Group.
Kobi, thanks for taking the time to join us. Welcome.

**Kobi Abayomi** (0:58)
Yeah, thank you. Thank you for having me.

**Sam Ransbotham** (1:00)
Let's get started. Kobi, you got a new position at a new company for you, Warner Music. Can you tell us about your role?

**Kobi Abayomi** (1:07)
Sure.
I started here about a year ago. I lead the data science effort of the company. It's a music company. And what is a music company? A music company is a support network for artists. It's a repository for licensing rights.
It's a creator of music content. So, a modern music company, one of the three major companies, the others are Sony and Universal, and each of them probably undertake a suite of activity that's relatively similar, which is find new talent, maintain and monetize music that's already been produced, both from the recorded and publishing side, and then find other ways to monetize current and past artists, through merchandise, through licensing, through sync. That's when music is played in other media, commercials, movies, et cetera, and the stuff that we all like and enjoy going on tours, live music, things like that. And so, the company has its fingers in all parts of the music landscape.

**Sam Ransbotham** (2:10)
Your role is Senior Vice President for Data Science, and I don't think you mentioned the word data in there at all.

**Kobi Abayomi** (2:14)
Sure, sure, sure.

**Sam Ransbotham** (2:15)
What role does data play?

**Kobi Abayomi** (2:16)
Well, let me give you the other side of the page, then. Data plays a large role. This is a legacy media company, which there are many. From a data science perspective, much of the work that's infrastructural, you're in an organization which did things one way and now is faced with a more competitive landscape with tech companies that have now their feet on the media side of the consumer space.
So, a lot of the work is around infrastructure, codification, and technology, really. Being able to ingest data, turn it into features, and turn it into insights that are meaningful so that the business can compete and operate now that it's beset with a volume of data that it wasn't before. Much of the way money comes to the door for a music company nowadays is through digital streaming. The distribution channel is digital, right?
The Spotify's and Apple Music of the World are the ones who deliver our product directly to my listening ears as well as your listening ears and my listening ears as well. We get paid for the use of that product and inventory, right? Then what happens is the data about the use of that product, which originates and is at the highest resolution on the distribution side of the channel, we get a version of it. So a lot of the things that have become, I say pro forma in D to C businesses, which are recommendation systems, audience segmentation, things like that, are things now media companies, not just this company, any legacy media company that has a distribution channel that's changed for analog to digital are now having to refactor and re-understand how do we understand market, how do we understand audience groups. I'm old enough to remember and have participated in, back when I was a college student, focus groups.
And I remember I was at the college I went to, I guess I was a good focus group participant, and they kept calling me back in for different campaigns, Budweiser or Hertz, Renekar. And I remember being asked a bunch of different questions in the group. Well now with digital consumption of things, we can augment those older methodologies of what people actually like, who they actually are, what their affinities are, what their behaviors are. And so for this company, in particular for legacy media companies in general, a lot of the data science work is around building the capacity for them to be able to, I say, enjoy what's now available data-wise and understanding their consumers.