Responding to demand: What information actors told us about humanitarian data

Last year, the HDX team created a survey to understand the data needs and perceptions of the humanitarian community. The findings confirmed our expectations, but also surprised us in many ways.

The team had recently completed 45 hours of interviews with humanitarian decision makers to understand the questions they needed data to answer. We wanted to compliment this in-depth, time-intensive research with a broad survey that we hoped would reach hundreds of people. We were also seeking funding at the time and wanted to make the case that HDX was responding to demand and was a worthwhile investment (spoiler alert: we got the funds).

What surprised us?

• Around 3,500 people responded from 151 countries over a one-week period.
  
• Most respondents said they worked for an NGO and were based in Africa and Asia.
  
• Interest in data type varies slightly by organization type (e.g. the media aren’t interested in geospatial data and the private sector is least interested in pre-crisis data).
  

What we expected

• People spend time doing analysis on data that is hard to find and compare.
  
• People are interested in a variety of humanitarian data types (e.g. response data, pre-crisis data, social media data).
  
• People want to do a number of things with the data (e.g. download it, compare it, visualize it).

HDX aims to help an incredibly distributed group of humanitarians use data and analysis to inform their work. The survey findings have provided us with a baseline for understanding user needs. We will build on this as we make progress. We recently finished user interviews to inform the initial features of the HDX Dataset Repository. Shawna Hein, our UX expert, will write about this next week.

We are just starting to form an early-user group of beta testers. If you are interested in taking part, please e-mail us at hdx@un.org.

And a special thank you to Jakob Rogstadius, a bright PhD candidate at the University of Madeira, Portugal, for helping me to analyze the data. It’s very much appreciated, sir!