Case Study: Coyote achieves 9% higher speed-limit reliability and automated corrections with Dataiku DSS

How Coyote Uses Dataiku to Improve Core Product Accuracy and Efficiency with IoT

Coyote, the European leader in real-time road information, faced a critical product challenge: keeping embedded map speed limits accurate using massive IoT feeds (billions of anonymized speed/position rows). To move from manual checks to an automated, algorithm-driven process and instill a data-driven culture, Coyote partnered with Dataiku and leveraged Dataiku Data Science Studio (DSS).

Using Dataiku DSS, Coyote built a machine‑learning pipeline (random forests) that segments roads, predicts section speed limits, and flags anomalies — with daily automated retraining and collaborative workflows that brought analysts and product owners together. The Dataiku-powered solution automated speed-limit correction, increased speed limit reliability by 9% on analyzed datasets, and drove broader data-driven adoption and improved customer loyalty.

Coyote

Florian Servaux

Smart Data Team Leader