Restructuring product management teams is challenging: there’s no universal “best practice” or generic org chart, and people issues are the tough ones. We step through two examples of redefining what product folks do…
Synerzip webinar for product managers (and others) with tips for working with data scientists and DS/AI/machine learning projects.
How do we provide additional context? Understand possible failure modes? Define “done” operationally rather than academically?
Rich Mironov was MC for Australia’s largest product conference in Melbourne and Sydney (October 2019). Organized by Brainmates, this year featuring Radhika Dutt, Bruce McCarthy, John Zeratsky, Sally Foote, and Audrey Cheng — plus Rich’s personal reflections on three decades of increasing visibility for product management.
Product managers working with data science teams on production applications have more challenges than with more deterministic (traditional) applications. These include providing more business/user context, not assuming that data will be predictive, and discussing accuracy requirements at the very start of a project.
Career paths for product folks are murky, with most of us falling into product management accidentally. Opportunities for advancement can be just as unclear. How do we think about product leadership roles, and how might we pursue them?
Wide-ranging conversation about product leadership, how product management has evolved, validation ahead of building, teleportation, scaling up product management teams, and working with non-product executives.
I talk with lots of senior individual contributors about the risks and challenges of moving “up the ladder” into product leadership roles. Here’s a survey I fielded to capture their top questions and concerns about getting promoted. What do product leaders do? How do product managers signal their interest in becoming one?