Data Analytics with a Human[itarian] Face

There has been no shortage of natural disasters in the past decade, all of which have devastated communities and torn lives and hearts apart. In January 2010, a 7.3 magnitude earthquake shattered the small Caribbean country of Haiti. The tremor killed more than 220,000 and left hundreds of thousands more displaced. Six months following this disaster, I embarked on a relief trip to Haiti determined to help rebuild a broken nation.

During that trip, I was shocked at how much rubble still towered in the streets. Depravity prevailed within the tent cities and chaos plagued the recovery efforts. I experienced first-hand how complicated it is to coordinate the shipment of supplies, distribution of food and rebuilding of infrastructure under such unstable conditions.

Just like a spider web, relief efforts are often intricate and delicate to handle. Numerous elements weave together to produce a near-impossible labyrinth for humanitarian coordinators to navigate. With so many factors at play, I can’t help but wonder: how do the many NGOs, organizations and government agencies even make sense of it all?

To explore this question, GovLoop recently spoke with Andrew Schroeder, director of research and analysis of Direct Relief.

Direct Relief is a nonprofit NGO that provides medical supplies and services to people around the world affected by natural disasters and poverty. The humanitarian organization particularly targets those most in need, such as individuals living in rural areas, low-income families, or those that lack access to healthcare.

Schroeder explained that what makes relief efforts so difficult is the soaring number of factors that must be taken into consideration – such as political, socioeconomic, topographic and geological forces. This is where data analytics comes into play.

Direct Relief utilizes powerful data analysis and visualization tools from Esri and Palintir to identify vulnerable populations and mobilize medical assistance. These data technologies have saved thousands of lives and restored communities during some of the most destructive disasters. Here are two stellar use cases of Direct Relief and data analytics at work across the globe:

Hurricane Preparedness Along America’s Coast

When it comes to disaster recovery, a strong defense is often the best offense. That is the philosophy behind Direct Relief’s Hurricane Preparedness program, which aims to provide at-risk communities with medical supplies before the first waves touch down.

Palintir Gotham data integration technology allows Direct Relief to leverage large amounts of information from sources such as the U.S Census Bureau and National Oceanic and Atmospheric Administration (NOAA) on a single platform. They can then visualize historical patterns, assess risk, and identify communities along the coast that are the most vulnerable to storm destruction.

After pinpointing at-risk locales, Direct Relief sends Hurricane Preparedness Packs to local health facilities in these areas. Each pack contains enough medical supplies to treat 100 patients for various maladies for 72-hours. The packs are pre-positioned in “safety-net” clinics along the Gulf and Atlantic coasts, ready to be deployed at any moment in the case of a hurricane.

Typhoon Response in the Philippines

Direct relief also utilized Palintir data analytics when responding to the devastation caused by Supertyphoon Haiyan in the Philippines last fall. Though there was no shortage in relief response efforts, Schroeder explained that the flows of international aid often miss out on populations that are most in need.

To fill in the gaps, Direct Relief deployed devices with a Palintir mobile application that allows users to send SMS text messages without cellular coverage. This empowers individuals and relief teams to send real-time disaster updates, assessment data and other pertinent information from any location.

The data from the SMS messages then stream into a Palintir platform called Raven. With Raven, Direct Relief can fully analyze the incoming SMS data, and integrate it with other datasets and satellite imagery. This produces a more accurate and complete situational analysis, allowing Direct Relief to shape their response strategy to reach those most in need of medical assistance.

As these use cases illustrate, Direct Relief is a pioneer and role model for organizations across sectors looking to integrate data analytics into their current systems. Schroeder recommended some helpful tips for making the most of data analytics:

  • Integrate your organization’s internal data. Direct Relief consolidated and combined its own data in 2006 with SAP platform solutions. “This helped us address our own organizational needs internally and realize the benefits of having integrated, easy to read datasets,” Schroeder said.
  • Realize the benefits of open data. Schroeder noted that the recent open data initiatives within the federal government have unlocked new doors and resources for the organization. For example, Direct Relief can now utilize hundreds of datasets from FEMA to expand their analysis of hurricane patterns and risks.
  • Implement new technologies as quickly as possible. Since disaster relief is an ever-changing landscape, the deployment of new technologies should be as rapid as the response efforts. Schroeder suggests integrating the latest solutions to garner the most benefits.
  • Explore new horizons. New developments in data and technologies are opening doors for organizations to revolutionize their response efforts. For example, Direct Relief is on the forefront of exploring the potential humanitarian applications of unmanned air vehicles (UAVs, or drones). Remaining innovative will take your organization’s operations to the next level.

Data analytics is an invaluable resource in the wake of natural disasters. Like a lighthouse, data illuminates otherwise invisible patterns and information pathways that humanitarian organizations can use to better mitigate risk, respond to disasters, and reach persons in need. As Direct Relief’s example illustrates, data combined with a passionate organizational mission is a powerful tool for saving lives.

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