I think this is one of the biggest challenges in data work  bridging the gap between “right” and “useful.”

Accuracy is essential, but if stakeholders can’t connect the dots, the work often falls flat.

What’s helped me is focusing on translation, not just presentation. Instead of dropping a dashboard or model output in front of someone, I try to tell a story around it: What does this number actually mean for them? What action can they take with it?

finding that contextualizing uncertainty is important. Rather than showing a forecast with a confidence interval and leaving it there, explaining what it means for a decision today – for example, “This suggests demand is likely to rise, so it’s safer to increase stock rather than risk shortages.”

Finally, I always try to involve stakeholders early. If they’ve shaped the question with me, they’re more invested in the answer and more likely to act on it.

For me, the real impact comes when the work sparks a decision, not when the model hits 95% accuracy.