Not long ago, I visited with Ilir Causchaj, Head of the Zone Logistics Unit, Global Logistics Service-Americas, for the International Federation of Red Cross and Red Crescent societies (IFRC). Caushaj explained some of the challenges of getting aid to individuals when disaster strikes.
“When a sudden and unpredictable disaster places great demands on humanitarian aid supply chains, there can be finite supply chain capacity into the disaster zone (i.e., only so many trucks, planes, warehouses, etc., are available),” said Caushaj “Often, the wrong types of goods can be shipped-by organizations that don’t have the ability to traverse the critical “last mile” to the disaster site. The “coordination” attempt on the ground is nothing more than marshalling of incoming goods and trying to get the most needed ones through a constrained pipeline. Examples of where this has happened include Haiti, the tsunami in Japan and the earthquake in Pakistan.”
Orchestrating disaster response depends not only on coordination, but on the ability of aid organizations to collaborate with one another, access information coming in from all directions, and derive actionable intelligence from information. Humanitarian aid organizations like the IFRC, UNICEF, the Red Cross and others are beginning to meet these challenges with the use of Big Data that gives them (and government leaders of the countries that they operate in) more complete views of how well aid is working, and how they can optimize aid efforts. Just as importantly, these organizations are beginning to use Big Data in new ways that can help them in non-disaster aid efforts that have the power to preempt tragedy– if they act swiftly enough, and apply the right solutions.
Improving crop yields and agricultural practices in countries with high starvation and malnutrition rates is one example.
For years, non-profit aid organizations have been sending in field workers to advise local farmers on best agricultural practices. These workers file progress reports and keep tabs on agricultural projects to see if crop yields improve.
The difficulty has been in collecting all of these reports, which come in many different forms–and then trying to glean insights into them after they become a monolithic body of unstructured and semi-structured data. By using Big Data collection, grooming and analytics techniques, humanitarian aid organizations are now able to compile all of these unstructured reports of field farming activity into databases-and then to mine these databases for information about which farming projects are succeeding, which are not, and why.
These Big Data practices allow them to refine their metrics and practices for improved outcomes. They are also tying in weather reports with incidences of malaria and then breaking down malaria outbreaks by age group—again, an example of how Big Data emanating from a variety of collection points can be pulled together into a database and then queried for meaningful aid interventions.
Then there is Benetech, which processes over 1.3 million downloads of accessibility-friendly books from its online library for persons with disabilities like blindness and severe dyslexia. The organization collects information on over 200,000 program participants, as well as data on which books are most widely read. This information can provide insights into the handicapped demographic, how best to serve it-and potentially even intelligence on cognitive and motor skills.
The common denominator of all of these Big Data projects for non-profits is that they are providing answers to problems that were previously considered to be almost insurmountable. This has resulted in interventions in both disaster and non-disaster scenarios that have yielded more success and relief from suffering-and just as importantly, less waste in situations that are always resource-critical.