Today I’m wrapping up my observations from the CDOIQ Symposium and sharing what’s new in the world of data. I was only able to attend a handful of sessions, but they were primarily ones tied to the topic of data products, which, of course, brings us to “What’s a data product?” During this episode, I cover some of what I’ve been hearing about the definition of this word, and I also share my revised v2 definition. I also walk through some of the questions that CDOs and fellow attendees were asking at the sessions I went to and a few reactions to those questions. Finally, I announce an exciting development on the launch of the Data Product Leadership Community.

Highlights/ Skip to:

Brian introduces the topic for this episode, including his wrap-up of the CDOIQ Symposium (00:29)
The general impressions Brian heard at the Symposium, including a focus on people & culture and an emphasis on data products (01:51)
The three main areas the definition of a data product covers according to Brian’s observations (04:43)
Brian describes how companies are looking for successful data product development models to follow and explores where new Data Product Managers are coming from (07:17)
A methodology that Brian feels leads to a successful data product team (10:14)
How Brian feels digital-native folks see the world of data products differently (11:29)
The topic of Data Mesh and Human-Centered Design and how it came up in two presentations at the CDOIQ Symposium (13:24)
The rarity of design and UX being talked about at data conferences, and why Brian feels that is the case (15:24)
Brian’s current definition of a data product and how it’s evolved from his V1 definition (18:43)
Brian lists the main questions that were being asked at CDOIQ sessions he attended around data products (22:19)
Where to find answers to many of the questions being asked about data products and an update on the Data Product Leader Community that he will launch in August 2023 (24:28)

Quotes from Today’s Episode

“I think generally what’s happening is the technology continues to evolve, I think it generally continues to get easier, and all of the people and cultural parts and the change management and all of that, that problem just persists no matter what. And so, I guess the question is, what are we going to do about it?” — Brian T. O’Neill (03:11)
“The feeling I got from the questions [at the CDOIQ Symposium], … and particularly the ones that were talking about the role of data product management and the value of these things was, it’s like they’re looking for a recipe to follow.” — Brian T. O’Neill (07:17)
“My guess is people are just kind of reading up about it, self-training a bit, and trying to learn how to do product on their own. I think that’s how you learn how to do stuff is largely through trial and error. You can read books, you can do all that stuff, but beginning to do it is part of it.” — Brian T. O’Neill (08:57)
“I think the most important thing is that data is a raw ingredient here; it’s a foundation piece for the solution that we’re going to make that’s so good, someone might pay to use it or trade something of value to use it. And as long as that’s intact, I think you’re kind of checking the box as to whether it’s a data product.” — Brian T. O’Neill (12:13)

“I also would say on the data mesh topic, the feeling I got from people who had been to this conference before was that was quite a hyped thing the last couple years. Now, it was not talked about as much, but I think now they’re actually seeing some examples of this working.” — Brian T. O’Neill (16:25)

“My current v2 definition right now is, ‘A data product is a managed, end-to-end software solution that organizes, refines, or transforms data to solve a problem that’s so important customers would pay for it or exchange something of value to use it.’” — Brian T. O’Neill (19:47)

“We know [the product is] of value because someone was willing to pay for it or exchange their time or switch from their old way of doing things to the new way because it has that inherent benefit baked in. That’s really the most important part here that I think any data product manager should fully be aligned with.” — Brian T. O’Neill (21:35)

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