Managing the trade-off between speed and accuracy often comes down to setting the right expectations and building a layered approach. For quick decision-making, lightweight models or summary dashboards can provide directional insights, while more rigorous analysis runs in the background to validate and refine those results. Frameworks like agile data sprints help balance the urgency of delivery with quality checks at each step. Tools that enable reproducibility and automation whether through version-controlled notebooks, data validation pipelines, or monitoring dashboards also reduce the risk of errors slipping through. In practice, it’s less about choosing speed or accuracy, and more about designing workflows where both can coexist without compromising business impact.