Improving Data Quality

Climate FieldView is a digital farming application that allows farmers to make more informed and confident decisions about their operation.

The FieldView Drive is a small device that is plugged into a farmer’s equipment which allows all equipment generated data from that machine to flow directly into the application where they can visualize it. Our science team is able to utilize the data flowing into our platform to feed science models which ultimately are able to generate tailored science-based recommendations that help growers increase the productivity of their farms.

Climate is able to directly stream equipment generated data directly into our app via the Drive. However, that data is not always accurate. Out on the field, a farmer may be in a rush and may type in an incorrect hybrid name. Then, that incorrect hybrid name is attached to the generated data that is streamed into our application. Since our science models rely on that data to improve recommendations, it’s crucial that we always strive to improve data quality.

—

THE Problem

63.4% of seed names (hybrids) within FieldView do not match the catalog names.

How do we encourage the user to correct their hybrid data and seamlessly incorporate bulk corrections?

—

Team

I was the Product Designer working on this project along with a Product Manager, an Engineer, and a Data Scientist.

—

Role

User Research, Competitive Analysis, Flow Generation, Remote User Testing, Wireframing, and Prototyping.

—

Access the full project here!