Giving an interface to data centric concepts

UX Design

UX Design

UX Research

UX Research

MVP

MVP

Data viz

Data viz

Client

Eyeclimate

Client

Eyeclimate

Client

Eyeclimate

Role

Product Designer

Role

Product Designer

Role

Product Designer

Location

Remote

Location

Remote

Location

Remote

Tools

Figma, Tl Dv, Miro

Tools

Figma, Tl Dv, Miro

Tools

Figma, Tl Dv, Miro

Overview

Eyeclimate is a company that merges environmental data (satellite, drone images, calculations, maps) into clear overviews for their users so they can inform themselves and make better desitions. In this particular case we worked a system that measures cattle via drones and images and provides a clear overview of the headcount at times when it’s most needed.

The Outcome

Giving life to new data concepts and make them easy to visualize

I was tasked to prepare an interface that would allow us to translate new concepts for cattle control into an easy to use interface. The result drove us to develop a way for users to upload and debug their own images as well as take control over “Bomas” which are the circles seen here that represent cattle concentration points.

The process

Understanding the concepts
Data isn’t easy to get. To make able to prepare an interface for a system like this we need to turn abstract concepts into ready to code objects with its own limitations and attributes. In this case, understanding how data has to be displayed is essential.

Lifting up questions with drafts and iterations
You never get a prototype right the first time. This is why we worked through a brief, a lot of interviews and many drafts that would open up more questions that resulted helpful in the end. We came to understand what the needed states where, What changes when we zoom in and out of a map and what would useful components be to ground our users.

Testing and refinement
We worked through five formal iterations in several weeks to be able to come up with a solid MVP, deprecating what didn’t work and refining the parts that provided easier readability on the data and the features like debugging.

Conclusion

Data tools are heavily visual. And when we design for data and maps, it is very important to be very detailed in the change of states we can display at hand that can communicate to the user; For example, a zoom in of the components on a map looks different than a zoom out, how? We have to design it so we we make it easy to the user to understand all the little things that are happening in the map with minimal intervention and a carefully structured UI.

solbenitezca@gmail.com