Our digital maps are not always up to date with the real world. New road constructions and road blockages could reduce the accuracy of the map data. In a logistics company like Gojek that serves millions of users per day in South East Asia, the core undertaking revolves around routing and ETAs. Any inaccurate local map data can lead to a direct negative impact on business metrics. So how do we ensure that map inconsistencies are detected and fixed promptly to minimise interference of our services? When manual detection is labor intensive and not scalable to millions of road networks in vast regions, how can we effectively automate this at scale? This talk is a story of how we, at Gojek, built a pipeline that uses bad customer experience as the trigger to identify potentially faulty data in OpenStreetMap. Our solution makes use of noisy GPS traces and Overpass, an open source tool, to automate this detection. This solution enabled us to identify 100s of potential issues per day, categorise them, associate business impact to each map issue and allow our map analysts to fix them seamlessly.