Telling Stories with Humanitarian Data
[Guest post by Andrej Verity, Disaster Responder and Information Management Officer for UN-OCHA. Originally posted on VerityThink]
Last summer I had the fortunate pleasure to work with Annie Waldman for a few months. With her journalism background and enrollment in a Data Journalism masters, simply watching how she worked, how she searched for data and how quickly she could augment a story with that data was inspiring. It was at that time when I realized that the Data Journalism discipline would soon be impacting the humanitarian sector. Knowing that “facts tell and stories sell”, how can we use the increasing amount of humanitarian-related open data to augment good story telling (and information products). We need people who can find data in a range of locations and then be able to look at the pile and distill it down to interesting, impactful stories. In my day job, that got me thinking about how this would impact the work UN-OCHA’s Information Management Officers do and how that work would change in the future to support improved data-driven storytelling.
When I was leading OCHA’s information management response to Typhoon Haiyan, I regularly helped clean and compile the Who’s doing What Where (3W) data in the early days of the emergency. But, after a few cycles, I realized that there was a lot of very interesting facts being lost as the data was summarized into matrices or maps and only the big numbers (e.g. number of people fed) were pulled out for official reports. We were not using the data to tell compelling stories or even relay facts in an interesting fashion to decision makers. As Nate Silver noted in The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, “The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning”. I needed to find a way to make the 3W data interesting. Late one evening, I started talking to the team about developing comparisons as a way to draw out interesting facts from the 3W. The interest factor “hit home” when we calculated which western city could be covered with all the tarpaulins that had been delivered to date. Although we did not release the details of that comparison, we started pulling interesting facts from the 3W and doing some comparisons. On 24-November we released our first 3W: Interesting Facts as part of the regular 3W package. Once we did that, people wanted more and it turned into its own product. And, as you see below, we were even able to include some interesting comparisons. The 3W Interesting Facts product quickly became the most popular product in our physical kiosk in Manila. Why? Because it shared small facts in a digestible, interesting format with a bit of storytelling effort.
Re-inspired with the Humanitarian Data Journalism concept, I arranged for Daria Kireeva to join me to do some research and work on the topic. We are going well beyond the interesting facts concept. We are focusing our initial efforts on augmenting early narrative UN reports from the Philippines with data, comparisons and trends. We chose the Philippines as I know the context and, in many respects, it is an easy case given that there was little security concern and there was a lot of data available. If the Philippines case proves difficult, we can imagine what it will be like in places like the Central African Republic where data is sparse and security is a major concern. However, our hope is that we will produce examples and materials that will help spark ideas and efforts throughout humanitarian organizations. We want humanitarian data to help produce even more compelling humanitarian stories. Stories that help decision makers to understand the situation and to compel both governments and individuals to respond with even bigger hearts than they already do today.
But, to do this, we know that data preparedness will be key. I do not mean just the regular operational data. I mean understanding the full range of data available that will or could be relevant to a given emergency. Why so much? So that when an emergency hits and you want to show a multi-year trend or a comparison against a similar emergency, you know where to go. You will need to be able to do it in a hurry. You will not have time to search through the Internet. Such work will not be easy. In Guiuan, Philippines I combined official humanitarian community data with data that was found on local Filipino news sites to create a bunkhouse graphic within minutes. So, I know that it can be done. But, how much better could have the product been if I had fast access to even more relevant data?
OCHA has recently started the Humanitarian Data eXchange project which will initially house over 150 baseline humanitarian indicators and eventually expand into a larger data repository. This site will become a great resource for humanitarian data. ACAPS has created data-rich preparedness packages on Bangladesh for Flash Floods and Landslides, River Flooding, Haor Flooding, Waterlogging, Cyclones, Cold Wave [PDF for cyclones without registration requirement]. Even beyond these, we will have to stop and recognize that there will be different data sources out there that must be accepted and used if we want to put together the most complete picture as possible for decision makers. Wikipedia and social media are obvious examples. But what if we stretched our thinking even more? What about collaborating with Data Brokers? Data Brokers are heavily active within the western world in efforts to collect a huge amount of personal data about us that we do not even realize. Is it much of a reach to imagine that such companies will eventually get involved in collecting data shortly after a crisis? Of course that could raise some serious privacy issues, but if we were to work collaboratively with them and ensure that they adhered to humanitarian principles, perhaps they would become a great, new data source to help tell constructive and compelling stories.
What will the result be? My preference in the short-term is to think that we can use data to augment our existing reporting and storytelling. But, I would also love to see a humanitarian data lab that includes capacity to write about humanitarian response from the data perspective just as the FiveThirtyEight Data Lab does about main stream media events. It could tackle hard questions like “Has Humanitarian Aid Been Effective in Sudan?” just as FiveThirtyEight wrote about increased disaster costs not being related to climate change. Now that would be a exciting challenge.
I think that the future holds an interesting time for the humanitarian sector, its data efforts, and its story telling. That is why I have enrolled in the (free) Doing Journalism with Data: First Steps, Skills and Tools course. Will you join and become a future data-powered humanitarian story teller?
I look forward to reading your data-driven humanitarian stories.