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Today I’m chatting with returning guest Tom Davenport, who is a Distinguished Professor at Babson College, a Visiting Professor at Oxford, a Research Fellow at MIT, and a Senior Advisor to Deloitte’s AI practice. He is also the author of three new books (!) on AI and in this episode, we’re discussing the role of product orientation in enterprise data science teams, the skills required, what he’s seeing in the wild in terms of teams adopting this approach, and the value it can create. Back in episode 26, Tom was a guest on my show and he gave the data science/analytics industry an approximate “2 out of 10” rating in terms of its ability to generate value with data. So, naturally, I asked him for an update on that rating, and he kindly obliged. How are you all doing? Listen in to find out!

Highlights / Skip to:

Tom provides an updated rating (between 1-10) as to how well he thinks data science and analytics teams are doing these days at creating economic value (00:44) Why Tom believes that “motivation is not enough for data science work” (03:06) Tom provides his definition of what data products are and some opinions on other industry definitions (04:22) How Tom views the rise of taking a product approach to data roles and why data products must be tied to value (07:55) Tom explains why he feels top down executive support is needed to drive a product orientation (11:51) Brian and Tom discuss how they feel companies should prioritize true data products versus more informal AI efforts (16:26) The trends Tom sees in the companies and teams that are implementing a data product orientation (19:18) Brian and Tom discuss the models they typically see for data teams and their key components (23:18) Tom explains the value and necessity of data product management (34:49) Tom describes his three new books (39:00)

Quotes from Today’s Episode “Data science in general, I think has been focused heavily on motivation to fit lines and curves to data points, and that particular motivation certainly isn’t enough in that even if you create a good model that fits the data, it doesn’t mean at all that is going to produce any economic value.” – Tom Davenport  (03:05)

“If data scientists don’t worry about deployment, then they’re not going to be in their jobs for terribly long because they’re not providing any value to their organizations.” – Tom Davenport (13:25)

“Product also means you got to market this thing if it’s going to be successful. You just can’t assume because it’s a brilliant algorithm with capturing a lot of area under the curve that it’s somehow going to be great for your company.” – Tom Davenport (19:04)

“[PM is] a hard thing, even for people in non-technical roles, because product management has always been a sort of ‘minister without portfolio’ sort of job, and you know, influence without formal authority, where you are responsible for a lot of things happening, but the people don’t report to you, generally.” – Tom Davenport (22:03)

“This collaboration between a human being making a decision and an AI system that might in some cases come up with a different decision but can’t explain itself, that’s a really tough thing to do [well].” – Tom Davenport (28:04)

“This idea that we’re going to use externally-sourced systems for ML is not likely to succeed in many cases because, you know, those vendors didn’t work closely with everybody in your organization” – Tom Davenport (30:21)

“I think it’s unlikely that [organizational gaps] are going to be successfully addressed by merging everybody together in one organization. I think that’s what product managers do is they try to address those gaps in the organization and develop a process that makes coordination at least possible, if not true, all the time.” – Tom Davenport (36:49)

Links Tom’s LinkedIn: https://www.linkedin.com/in/davenporttom/ Tom’s Twitter: https://twitter.com/tdav All-in On AI by Thomas Davenport & Nitin Mittal, 2023 Working With AI by Thomas Davenport & Stephen Miller, 2022 Advanced Introduction to AI in Healthcare by Thomas Davenport, John Glaser, & Elizabeth Gardner, 2022 Competing On Analytics by Thomas Davenport & Jeanne G. Harris, 2007

HighlightsBeatport’s Top 100 chart keeps highlighting the latest music in the club world, and today, we’re doing aSpecial artist deep-dive into a Norwegian producer who found streaming success in a far-off land called...SeattleMissionGood morning, it’s Jason here at Chartmetric with your 3-minute Data Dump where we upload charts, artists and playlists into your brain so you can stay up on the latest in the music data world.DateThis is your Data Dump for Friday March 29th 2019.ChartsBeatport.com is the go-to electronic music marketplace for professional DJs around the world so they can make people dance. With over 60K suppliers and labels, 450K customers and 35M unique annual visitors, Beatport is a B2B business at its core: providing high quality downloads so DJs can fill their sonic arsenal.It also provides a weekly Top 100 chart that essentially becomes an up-to-date soundtrack to what clubbers are getting down to globally.For the week ending March 22nd, “Inside My Head” by UK-based duo Audiojack took the #1 most purchased download for the second week in a row spending 10 days on the chart. Spinnin’ Records had the most tracks with 4, including a David Guetta and Tom Staar track, while legendary London-based Ministry of Sound Recordings had 2.While you may see some familiar Top 40 names such as Childish Gambino in the #3 spot or Calvin Harris & Rag’n’Bone Man at #5, the Beatport Top 100 is really an anti-pop serum: nearly half the chart’s tracks have 4-minute-plus run times, zero tracks directly releasing into the chart and nearly ⅔ of the list spending less than a week on the chart themselves...meaning lots of track turnover, and lots of opportunities for emerging artists with great dance music.Artist Highlight in the NewsHere’s an interesting case of streaming’s global nature at its finest: Norway-based house producer Simon Field found unexpected attention in Seattle upon the release of his track “Shake the Tree” on January 25th.Field, who sports a 53 Spotify Popularity Index score and 385K monthly listeners despite having only 10K followers, is an example of an emerging artist that organically over-indexes their stream count in a particular city for an unknown reason.Upon “Shake the Tree”’s release, it found a snug spot in the #62 position of the 90-track New Music Friday playlist for that week, which then seemed to feed playlist adds within 24 hours on no less than 20 mid-tier playlists ranging from 10 to 80K followers.While Sony-owned playlist curator Filtr UK’s “Dance All Night” was among these lists, Field had no major label support in the release, and yet from Feb 21st to March 20th saw a 455% increase in monthly listeners in Seattle, peaking at 7.4K.Virtually mirroring Field’s rise in Seattle however, a certain mid-tier playlist called “CloudKid” by the curator of the same name added “Shake the Tree” with 98K followers…..adding the track on Feb 16th, and removing it on March 21st, after which day Field saw an immediate decrease in Spotify listener growth in Seattle after a month long increase.Coincidence? Possibly, except for the fact that CloudKid is an influential electronic music label & curator who came up on YouTube, with a channel influencing 2.9M subscribers with over 906M total views.While the connection between Seattle and CloudKid’s audience is still unclear, the data suggests at the minimum an appreciation of mid-tier, five-digit follower count playlists helping propagate new music.And at its best, Field may have a unique case of cross-platform success where a veteran YouTube curator’s side hustle (or here, a Spotify playlist) gave an unknowing artist streaming success thousands of miles away.Playlist Round-Up (none)OutroThat’s it for your Daily Data Dump for Friday March 29th 2019. This is Jason from Chartmetric, if you’re enjoying the podcast, hit that subscribe button so you get the latest episodes at the earliest time.And if you feel like you missed something, you can get full show notes at: chartmetric.transistor.fm/episodes.Have a great weekend, see you Monday!