Companies often celebrate hierarchy but rarely longevity – the willingness and commitment to stay with a company long enough to have an impact. I recently uncovered some artifacts that reinforced an important cultural emphasis on company-wide success…
I don’t think that product managers should lie to customers or prospects. In enterprise selling cycles, though, there is a lot of gray space around what’s true enough. Can we draw some hazy lines?
The jump from being a Product Manager to overseeing Product Managers/Owners is a big one. What’s in a Product Leader role, how do we design good product organizations, and how do we work across the organization to get good product work done?
Leading a product management team includes delegating most product-level decision-making. How do we match that to team maturity and differing difficulties of various product work?
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.
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…
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.