Prescription and data service offerings continue to grow, with most services requiring farmers to provide precision ag data in order to receive recommendations and other valuable information. Data quality is essential since poor quality data can lead to incorrect decisions. Plus, it’s well known that data quality has lacked over the years in the U.S. Ohio State ag engineer John Fulton discusses quality issues with data layers and how to address them, including how to identify data errors. He also provides an example of how the merger of agronomic and machine data can improve on-farm evaluations.