Dashboards are abundant; decisions informed by them are less common. Designing analytics around the decision rather than the data.
Many analytics initiatives begin with the data available and end with a dashboard that few people open. A more reliable starting point is the decision: who needs to decide what, how often, and with what information?
Working backwards from the decision clarifies which metrics matter, what level of accuracy is needed, and where automation will save the most time. It also makes it easier to judge where AI adds value, for example in summarising large volumes of text or classifying records, and where simpler methods are sufficient.
Trust is the final requirement. Leaders act on numbers they believe, which means definitions, sources and controls must be clear and consistently applied.