Technology is advancing at an incredible pace, with profound implications for people and society. Whether you read about artificial intelligence and algorithms in your spare time, or just hear about the latest technology trends from your friends, it can be challenging to keep up.
This is especially true in the humanitarian sector, where the expertise sought by aid agencies usually centers on disaster management and sectoral strengths rather than on technology. When a new data initiative or data tool is launched, humanitarians usually do not have the time to engage with it too deeply. This week will be no different, as headlines emerge from the World Economic Forum Annual Meeting in Davos, where leaders will grapple with the implications of the Fourth Industrial Revolution on our lives.
In managing OCHA’s Humanitarian Data Exchange platform, and now leading its new Centre for Humanitarian Data in The Hague, I spend most of my time talking and thinking about data. (This was not the intention; I was an English literature major in college.) As the center’s work gears up, and as we collectively get our heads around what data will mean for humanitarian action in the months and years to come, I want to share some of the top myths I repeatedly come across, in case they are useful for data newcomers.
Myth 1: We need to invest more in tools.
Digital devices and applications create efficiency, so when a new challenge arises, there is a tendency to focus resources on building a new data tool. However, it is equally important — and arguably much harder — to figure out how to use data responsibly to improve our humanitarian response. The policy and doctrine gaps relating to data privacy, data sharing, and other areas will take years to address, but a failure to do so will create major risks for the people we serve.
Myth 2: It’s all about big data.
The term big data refers to large datasets that must be processed by computers, rather than humans, to reveal insights. An example is using anonymized call-detail records from mobile phones to track people’s mobility patterns following a security event. Big data is promising, but the challenge in the humanitarian sector is bringing together small amounts of non-standardized data, mostly stored in spreadsheets, from dozens of organizations to create a common picture of needs and response. By way of example, look at the 50 datasets that have been shared on Humanitarian Data Exchange for the Rohingya crisis.
Myth 3: Data visualization is the same as analysis.
The Humanitarian Data Exchange team has created dozens of visualizations from data shared by partner organizations. This work involves cleaning and combing data and making choices about how data will be presented (e.g. a bar chart vs. a choropleth map). This is different than drawing conclusions from the data to understand why a crisis is deteriorating, or to decide whether to prioritize a cash response rather than food distribution. Data experts create the visuals; subject matter experts do the analysis.
Myth 4: Technology enables data sharing.
Hang around data experts for any period and the conversation will turn to infrastructure, application programming interfaces, formats, and layers. But the truth is that tech talk can only come after two people representing their organizations decide to exchange data. Trust, not technology, is at the heart of data sharing.
Myth 5: OCHA opened a data center in The Hague.
A data center is a facility where data is stored on networked computers that are organized in aisles of metal racks. The Centre for Humanitarian Data is not such a facility. It’s a place where people come together to work on shared data challenges while drinking coffee or tea in The Hague Humanity Hub. We don’t store any data in the building!
Myth 6: “I am not technical.”
This is the biggest myth of all. I have been in many meetings where a woman (it’s always a woman) prefaces what she is saying with: “I am not technical, but…” This basically translates into: “I don’t know what I am talking about, but…” In fact, we are all technical now. Just because you can’t write a line of code doesn’t mean you don’t understand technology. We all have something important to contribute to the discussion.