As product leaders, most of us have spent the first few weeks of the COVID-19 crisis focused on (worried about) our people and teams. But we’re now shifting attention to how this changes priorities and product plans…
Sending an expensive B2B sales team out to discover what we should build isn’t a great strategy. We should do less expensive, unemotional, non-commissioned validation and learning before scaling up our selling effort.
There are no generic or universal KPIs, since every business has unique aspects. So if we want KPIs for a B2B/enterprise company, where would we start? And how do we avoid committing to improvements in metrics/KPIs before understanding our current scores (or situation)?
As product folks, we should be responsible for reasonably anticipating misuses of our products, as well as harm that flows from fundamental product/economic goals. It’s not clear how we step up to this, though.
This podcast on Creating a Thriving Product Organization covered a lot of ground: becoming a product leader; what to do in your first month on the job; conditions that enable product teams to be their best; and Impostor syndrome.
An episode in ChadMcAllister’s Everyday Innovator series featuring Felicia Anderson and me on “How Product Managers Can Work Effectively With Data Scientists”
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?
An extended interview with Leading The Product transcribed into three linked posts on product leadership and challenges.
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.