Datamonitor Healthcare

Goal

Our goal was to create a UX design that would be 100% intuitive to use and would require no explanation or training. Users should be able to immediately create unique research criteria and produce customized data-rich reports.

Relevance

Data resources large enough to facilitate disease statistics across the world are not easily deployed. Data cleansing and structuring were key preparation steps in order to properly deploy our proposed functionality and data flow.
Our approach to creating effective data visualization relies heavily on understanding the total amount of data available, the specific data types and the relevance for the pharma industry.

Just a birdseye view of the what it takes to design and deploy an application this dense with data and this rich with embedded functionality and usefullness.

The final UI design represents the culmination of research, usability testing, hundreds of user stories developed and an overall adherence to corporate standards and branding.

Header design requires some of the most human-centered intuitive design strategies possible, as this will be the main navigation hub for the entire application across every screen throughout the platform.

High-level map data visualizations were developed to give the user a kind of orienting point to begin to better understand what is possible as they dig deeper into the data and begin to select data filters needed to achieve their user goals.

A Sankey diagram was designed to give users the most comprehensive view of the data possible, while also allowing for individual country deep dives for more detailed data.

The key deliverable of our Datamonitor design resides in the user’s ability to customize data selections and compare data based on their individual user goals.

Sometimes the more simple the data visualization the better. This view allows users to quickly see where particular disease numbers are the highest and begin to dig into the specific country data to discover more details.

Country by country data is just the beginning data level, as users can then proceed to look and compare states, cities, and counties. Once a user has determined the specific region of choice, they can then begin to filter the data by a specific timeframe, which adds another level of information and provides a basis for projected data into the future.