Most agile conversations are about morale, velocity (aka throughput), quality, predictability, and team dynamics. But we rarely address actual customer/user vale or business outcomes — instead hiding behind story points or vanity internal value metrics. This discussion will be about how strong product management bridges the outward customer/market view and inward development view.
In this podcast with Jay Stansell, we note that company executives often have very different perceptions of customer needs than the product management team. How do we understand this, sympathize, yet still bring balance evidence into C-level conversations?
Product leadership workshops in Toronto and Kitchener workshops co-led with Saeed Khan. We’ll look at organizational structures, leadership roles, portfolio-level prioritization, mentoring, and other product leadership challenges. A very full day of learning, sharing, collaborative problem-solving.
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)?
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.
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.