Use Results to Guide Business Decisions
From evidence to a decision
The final domain asks how findings change what the organization does. Know the elements of a defensible recommendation: the decision to be made, the options considered (including doing nothing), the evidence for each, cost-benefit and feasibility, risks and mitigations, and the success measure with a review date.
Analysis informs a decision; it does not make it. Decision-makers weigh constraints the analyst may not see - budget cycles, regulatory limits, customer commitments, capacity. Your job is to make the trade-offs explicit so the choice is informed, then support implementation: define the operational change, the metric that will show it worked, and the monitoring cadence.
Traps. Presenting findings with no recommendation and calling it neutrality. Recommending the analytically optimal option while ignoring feasibility. Failing to define how success will be measured, so nobody can tell later whether the decision worked. Confirmation bias - seeking data that supports a preferred option. Forgetting ethics and fairness, especially where a model allocates service or credit.
Rule of thumb - every recommendation needs a metric and a review date. State what you expect to change, by how much, by when, and what would cause you to reverse the decision.
Worked example. Analysis suggests a 15% reduction in support tickets if self-service is expanded, at an implementation cost of $200k. You present three options - full rollout, a two-region pilot, or no change - with expected benefit, risk, and a 90-day review against ticket volume and customer satisfaction. Recommending the pilot is a business decision grounded in evidence and reversible, usually the strongest position when uncertainty remains.