Data-Informed Product Management
In doing Agile Product Development, there can be a tension between a "top-down" (I don't mean exec-driven, I mean mental-stack) Product Vision/Product Strategy/Conceptual Integrity and an incremental/iterative process of Thinking In Bets.
- waterfall: spend years building the ultimate product before launching it
- incremental waterfall: deliver piecemeal, but filter every lack of adoption as "we just need to get further along the roadmap"
A new anti-pattern: being mindlessly data-driven
- "since FeatureAPage1 gets more views than FeatureBPage1, FeatureA is more important/valued"
- "we used a survey to have users rank the importance/value of 6 features - we must put the menu items in that order"
- "people's biggest request is FeatureX so we must do that"
Solution: A smarter way to use data
- see Triple-Loop Product Management
- iterate on your mission, business model (lean canvas), product vision, product strategy: these are all models (see Strategic Context)
- identify/rank problems/opportunities
- identify/rank initiatives/solutions -> Opportunity Solution Tree
- recognize that each initiative-candidate is a Bet - generate ideas on how to "test" that Bet with as little work/delay as possible
- released work generates adoption Data which with thinking supports/challenges your models
- if it seems to challenge/kill your plan, consider where to change direction (micro-pivot)
- feature design
- solution ranking
- problem validation
- strategy validation
- vision validation