Stakeholders want every dataset on a municipal open-data portal in Q2, but demand is uneven and many datasets are still messy. How should the Product Owner forecast the release?
Select an answer to reveal the explanation.
Short Explanation
Every dataset in Q2 is a warehouse with no aisles. Ship a thin slice of what residents actually search for and learn from that. Empty shells and all-or-nothing dumps both fake completeness.
Full Explanation
Thin vertical releases maximize early value and produce forecastable learning; broad completeness on messy, uneven-demand data does not. The Product Owner orders the highest-demand datasets into a releasable slice and forecasts that Increment, then inspects use. Waiting for every dataset to be clean delays value; empty shells optimize the calendar at the expense of Current Value. Completeness can remain a later hypothesis without being the Q2 forecast.