I think it goes back to the defining the problem space. If a key requirement is precision and the solution is not meeting that, then we've failed to meet the customer need. That's 90% of what platform product managers are spending the time on. I don't know if I see this across the board in the industry, right? But like, PMs largely understand counting through two or three numbers isn't good enough. And maybe it's not going to be perfect when it comes to data analytics.
Data gets accessed and used in an organization through a variety of different tools (be they built, bought, or both). That work can be quick and smooth, or it can be tedious and time-consuming. What can make the difference, in modernspeak, is the specifics of the "data products" and "data platforms" being used for those tasks. Those specifics, in turn, often fall on the shoulders of (data) product managers! In this episode, Austin Byrne, Group Product Lead for Data at Canva, joined us for a discussion about the similarities and differences between typical product management and data product management! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.