The roadmapping process is almost always broken. Roadmaps are met with frustration and boredom in equal measure… Different voices in the company want different things. How do we balance competing demands while uniting the organization behind a plan?
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 all-day workshop for product managers thinking about moving up the product org chart, and for newly promoted product leaders. We will cover what product leaders do, how to signal interest in that role, and discuss its upsides/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.
Sometimes we’re asked for conflicting or less-than-sensible things, both from customers and internal groups. This webinar is about understanding teams and adopting agile processes/tools to our specific situations.
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?
Industrial hardware and enterprise software are both great business, but have very economics, scorekeeping, and development models. To run a strong software business, we may need to retool some operating processes as well as executive assumptions.