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The Smartification of Humanitarian Response

By Moses Sitati | April 8, 2015

Emergency support to the Victorian flood relief effort (Australia). CREDIT: Australian Department of Defense (CC).

Have you ever found yourself standing in the middle of a parking lot wondering where you parked your vehicle? How about trying to figure out a specific detail of a street you regularly walk along? Very often we find our memory put to the test when trying to accomplish daily routines. Under relaxed circumstances, the consequences of not being able to recall this information ranges from being mildly comical to downright frustrating. However if you drastically escalated the urgency of that moment, take an accident situation for example, then a lot more than our personal comfort would hinge on our ability to recall this information. Now zoom out to a much larger emergency—a disaster such as an earthquake or flood—then the importance of having the right intelligence at hand is vital for the preparation of an adequate emergency response. The point is, if having ready information is useful in guiding our day-to-day activities, then it is acutely important during critical moments.

It all begins with the data collection. Data to help deal with a lengthy list of questions such as what do I know? What do I have? What does it mean? Where do I go? When should I start? How was it before? This list can be long. In a major disaster or conflict the data collection requirements could cover things such as: recording of damage to housing, infrastructure, and services; tracking displaced populations; distributing the massive influx of humanitarian supplies; and coordinating the work in and between clusters (sectoral working groups), as well as dozens of other agencies outside these clusters.1 Proper collection, storage, and analysis of this information help managers not to get overrun by data.

A number of digital services cater to these needs for humanitarian managers. ReliefWeb's dedicated staff team collects and delivers official disaster reports, maps, and info-graphics via internally-created information products. This makes it possible to know, for example, that following the recent tropical cyclone Pam that struck the islands of Vanuatu, 166,000 people remained in need of assistance and another 75,000 in need of emergency shelter. The related websites, on the other hand, contain community-sourced information to aid operations around the crisis, such as a 3W (who, what, where) response map for Vanuatu, situation reports, and assessments. These portals are facilitated by the United Nations Office for Coordination of Humanitarian Affairs (OCHA) which brings together humanitarian actors for a coherent response to crises and emergencies. Numerous humanitarian workers globally rely on these services for their day-to-day work.

Even with these management tools, there is still a need to make humanitarian data easier to find and to make it useful for analysis purposes. How can a decision-maker quickly track the trend of a crisis in a certain area? How can she compare the incidence between two regions? Information that resides in reports and documents is not always standardized, comparable, or reliable. Enter the need for smartification of humanitarian information management. Just as technology solutions have come up to inject efficiency and excitement into our mundane daily routines—there's a smart app for just about everything we care about—advances and new practices in data and information management can similarly improve further the access to and value of data to the humanitarian community.

The ubiquity of technology in general and phones in particular creates a fantastic opportunity for improvements in data collection. Today we carry around living databases—phones—constantly collecting data about our location, tastes, gender, relationship status, etc. This same technology allows humanitarian workers to keep information up to date and fresh for decision makers. Field assessments can be conducted via mobile phone and the data relayed to servers for quicker analysis and faster response. Data becomes much more easily retrievable, and not dependent on manual labor. This ensures the data is as current as possible but also less prone to errors from human intervention. Mobile tools like those provided by Ushahidi, Ona, and KoBo are examples of such data management systems. The KIRA rapid assessment mechanism, used in Kenya to conduct short notice assessments of humanitarian needs, equips field workers with a mobile survey application called mFieldwork that helps to reduce on time, costs, and errors from paper-based data collection. Data from KIRA assessments, such as from one done in Northern Kenya, can be packaged into reports, but most importantly, it can also be stored in a database: a clean, organized tabular data that can be queried in analyses.

A UN OCHA initiative, the Humanitarian Data Exchange (HDX), is championing the case for connection of data sources that contain structured, tabular data. HDX aims at supporting quick analysis for decision-makers in the humanitarian community through its information exchange platform. Using teams on the ground, HDX is working through outreach and direct engagements to surface valuable data that would otherwise be sitting on the personal computers of, for example, a UN agency staff member, in the PDF and text documents of an NGO's reports, or a corporation's internal database; and it's also working with them to share these datasets via HDX. Users of the HDX platform can then use the available data to build their own analyses according to contextual needs. The interactive dashboard built by a community member showing the organizations that were involved in the response to the 2014 Ebola outbreak in West Africa is a good example. While the Reliefweb and websites mentioned above do share data, it is not immediately available in formats which are ready for analysis or comparison.

Campaigning for data sharing as in the case of HDX is a long and uphill journey. Data is still a valuable and delicate enough resource within an organization that many may believe it is not to be shared. Data costs money to collect, is subject to varied collection methodologies, can be highly specific to an organization, generates insights and knowledge for its owner, can contain private information, or can be dangerous to share. For instance, NGOs operating in Somalia may not wish to disclose their project location information, to avoid exposure to insurgent groups. Private companies extract commercial value from the data within their walls as it can be viewed as a form of barrier-to-entry against competitors.

Going beyond data collection lies the issue of use and impact. It is, after all, not about having and storing information but about using it to better deliver much needed support and assistance to communities in crisis. Within each country, there is a coordination structure that provides oversight for emergencies requiring United Nations humanitarian assistance. Using a data-driven approach, this leadership team evaluates the severity of the crisis and decides on the needed solutions and the respective lead agencies. To be able to make informed decisions quickly, these teams need insightful analyses from fresh data. The growth of data visualization as a field of analysis represents a valuable tool that helps to communicate messages in a new way giving fresh meaning to information held by an organization.

This visual type of analysis is highly advantageous in a time-constrained and fast-paced world where text and numbers labor to deliver. The HDX platform aims to provide some visual analysis tools for managers and decision-makers working in humanitarian response. Through shared2 data around the West African Ebola outbreak, for example, the team was able to put together an informative and interactive crisis page for Ebola. Data from this Ebola page informed a New York Times article at a time when information around the emergency was sparse and hard to find.

Eventually, as the abundance of shared data grows, we can learn use it more smartly. By incorporating computing intelligence for analysis we could understand more about what we know and what we don't know during times of crisis. We can also learn how to ask better questions as managers and decision-makers to aid in response planning. I wonder if analyses on the shared data could help us find patterns in humanitarian crises and detect indicators of risk and vulnerability in communities. And I also wonder what the data could tell us about ourselves.

Though there are still significant hurdles to be overcome, not to mention data gaps and unavailability of data in developing countries, the ball is rolling, the disruption has happened, and the "data revolution" is underway, ushering a smart era of humanitarian and development work.


1 See the IASC Guidelines. Common Operational Datasets (CODs) in Disaster Preparedness and Response

2 Including data from the World Health Organization, the World Food Programme, the MIT Governance Lab, the UN Mission for Ebola Emergency Response among others

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