Data Analytics Help Floods Spped Up Relief
The International Organization for Migration (IOM), a non-governmental group that is helping find shelter for the millions left homeless by the months long devastating floods in Pakistan, is getting assistance from a rather unlikely source.
SAS Institute, a Cary, N.C. based software vendor better known for helping businesses analyze and manage large volumes of data, is now working with IOM to find ways to use its data analytics and advanced data management tools to get aid to flood victims more quickly and efficiently.
The effort is still somewhat exploratory but the early results have been promising, said Brian Kelly, who heads IOM's emergency and stabilization programming team from Islamabad.
For instance, by meshing and analyzing data provided by IOM and from multiple other sources, SAS has been able to compile useful data on the availability of bamboo stock in various regions in Pakistan. Bamboo is being used to construct temporary shelters as well as more permanent structures for homeless victims.
SAS has also begun playing a role in IOM's efforts to help flood victims better prepare for winter by providing actionable data on weather, health conditions and other factors. "What we are doing is giving them our data and trying to see what they can grab from data sources to provide us with a more complete picture of how to shape a response," Kelly said.
IOM is one of several local and international disaster relief agencies currently helping flood victims in Pakistan. The organization's primary role has been to help displaced victims find shelter in tents or other accommodations. The organization has also distributed plastic sheets, blankets and household items to tens of thousands of victims.
The sheer scale of the humanitarian disaster in Pakistan has made the task of making aid available to victims extremely complicated, Kelly said. So far, aid workers have made temporary shelters available to about 3.5 million victims which, while an enormous number, still represents just about 20% of the total number of victims.
Mobilizing an effective response to such a large disaster requires a through understanding of available resources, distribution logistics and prioritization skills, he said. You wouldn't necessarily associate humanitarian aid work with market analysis," Kelley said. But in a lot of ways that's precisely what is required, he said.
To help victims, aid workers must know how many tents and blankets are available, what raw materials are in stock and even how the cotton crop might affect the availability of blankets down the road. Decisions also need to be made on issues such as how many blankets can be provided to each family, whether the blankets need to be wool or fleece and what kind of kitchen sets and utensils to distribute.
Knowing how to prioritize is vital, Kelly said. "If you got a dollar to help one person it's a straightforward process. But if you have a dollar and you need to help 10 people you need to know who to target" first, he said.
In most disasters, prioritization decisions have been based on data collected from the ground and little else, Kelly said. "We make decisions based on information we get from talking to the beneficiaries of the aid and the displaced population. What we don't do is throw data on top of that," to determine if the aid is reaching the ones who need it the most.
IOM is hoping that its work with SAS can help it better address such problems. "We have our own data sets and SAS has other data sets in Pakistan and they know how to mine that data," he said. "They can make the decision making environment a bit richer."
Over the longer-term, IOM will work to determine how companies like SAS can play a broader role in future disasters, he said, "In every humanitarian crisis we get overwhelmed with data. In every response, we say we have too much data that we don't know what to do with," he said.
The goal is to see whether data analytics tools can help extract value from such data to help aid agencies work better in the reconstruction stage, he said.
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