A simple method to prioritise data initiatives by impact, effort and data availability, and focus investment where it returns most.
Ideas for what to do with data usually abound; what is missing is the criteria to decide where to start. Without prioritisation, resources scatter and the highest-return projects get stuck among dubious ones.
Prioritising data use cases means ordering initiatives by the value they bring and the cost of achieving them, to concentrate investment where the return is greatest and fastest.
A simple, effective tool is placing each case on an impact-versus-effort matrix. High impact, low effort are the obvious priorities; high impact and high effort, projects to plan; low impact, discardable. Data availability acts as a filter.
Start with a high-impact case of reasonable effort and available data. That first success validates the approach and funds the next. Review the prioritisation regularly: as the data layer matures, previously unviable cases become accessible.
Start where impact is high, effort is reasonable and the data already exists — then let success fund the rest.
Prioritise data use cases by crossing business impact, effort and data availability. The impact-effort matrix surfaces obvious priorities; start with a high-impact, available-data case to validate and fund the next, and review the order as the data layer matures.
By crossing three axes: business impact, implementation effort and data availability. Start with high impact, low effort and available data.
A tool that places each case by its value and cost, helping identify obvious priorities and discard low-return work.
No. It should be reviewed: as the data layer matures, cases previously unviable for lack of data become accessible.
An ideal case with no reliable data is not viable short-term. Availability acts as a filter on the impact-effort matrix.
With a high-impact case of reasonable effort and available data — a quick win that validates the approach and funds the next.
Resources scatter across dubious initiatives and the highest-return projects get stuck, delaying value.
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