Use RICE to turn a messy data backlog into a shortlist
Apply RICE scoring to rank data-team requests using explicit assumptions about reach, impact, confidence, and effort.
A data team has capacity for two requests: rebuild the revenue KPI, create a territory export, investigate churn drivers, and add five dashboard filters. The manager needs a transparent shortlist. RICE = Reach x Impact x Confidence / Effort, with effort including validation and adoption. The common trap is scoring only the build work. Data requests often carry hidden effort in definition alignment, data quality checks, downstream changes, and stakeholder enablement. Backlog ordered by executive pressure: dashboard filters, churn investigation, territory export, revenue KPI rebuild. Backlog ordered by explicit assumptions: revenue KPI rebuild, churn discovery spike, territory export, dashboard filters…
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