How to calculate the ROI of your data (formula & examples)
A practical guide to calculating the return on your data projects: formula, hidden costs, tangible and intangible benefits and real examples for leadership.
Read articleWhy decisions made without reliable data have a real but invisible cost, how to estimate it, and why investing in data is, at heart, risk management.

It is easy to quantify what a data project costs; much harder to quantify what not having data costs. Yet the cost of poorly informed decisions usually far exceeds the investment needed to avoid them.
A poorly informed decision is one made without the right data, with wrong data, or too late. Its cost spreads across missed opportunities, misallocated resources and late reactions.
Though hard to measure precisely, it can be estimated: what would detecting a sales drop earlier have meant? what does a recurring inventory error cost? Putting conservative figures to these questions reveals a cost almost always larger than expected.
Seen this way, investing in data is not only a growth lever but a form of risk management: it reduces the probability and cost of deciding badly. A good data layer’s value is measured as much in errors avoided as in opportunities opened.
The value of data is measured as much in the errors it prevents as in the opportunities it opens.
Poorly informed decisions cost real money — missed opportunities, misallocated resources, late reactions — even if it never appears as a budget line. Estimating it with conservative figures usually reveals a cost larger than expected. Investing in data is, in part, risk management: it reduces the chance and cost of deciding badly.
Approximately: by estimating missed opportunities, recurring errors and margin lost to decisions without data. It is usually larger than expected.
Because investing in data reduces the probability and cost of deciding badly, like any measure that mitigates a risk.
No. It is also in errors avoided and timely reactions, which are rarely accounted for but have real impact.
Ask concrete questions — what would detecting a sales drop earlier be worth? what does a recurring inventory error cost? — and use conservative estimates.
In missed opportunities, misallocated resources, late reactions and margin lost to pricing decisions made without data.
By providing the right information in time, it lowers both the probability and the impact of deciding badly.
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