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.
Product managers need to talk — often — with actual end users and buyers. We need to listen, interview, understand and empathize with paying customers. Unmediated by marketing, sales or researchers. What organizational barriers block this essential work, and can we remove some of them?
In this “Mastering Business Analysis” podcast, Rich shares thoughts on product manager versus product owner; output versus outcome; getting out of our cubes to learn from lots of real users; and building the right thing (not just building things right) to deliver measurable value.
There are a lot of inputs to product strategy including advisory boards, customer forums, sales teams and ROI prioritization algorithms. None are sufficient on their own: we have to develop and apply strategic judgment, and test that in the marketplace.
The AgileCamp organizers have generously invited me to kick off the Dallas event with a keynote on unpacking business value. We’ll look at things from “the business side” ahead of a full day of Agile and Lean practices.