When a crisis hits, the need to respond quickly and efficiently is urgent. Unfortunately, crises often produce chaos and disorganization, which can place even the best laid plans into a state of ruin. Whether its a natural disaster or something man-made, disaster management always needs some help in creating safer situations and reaching out to those who have been affected.
Thats where big data has so much potential. While businesses have already been using big data to improve their operations, crisis response teams have been catching on to the potential big data analytics can play in minimizing the damage caused by a disaster. These efforts have already been put to use in recent disaster scenarios, and the results have been promising.
As in business, big data is at its best when it takes unorganized sets of information and makes them into something understandable. There are few situations more chaotic than a disaster, so it makes big data a perfect fit for crisis response. In the case of crises, making the right decision is crucial to preventing further damages and saving lives. Unfortunately, too often response teams have to base their decisions on incomplete or inaccurate information.
They do the best they can, but even the best, most experienced teams can reach the wrong conclusion if the available data doesnt tell the whole story. Thats one reason big data has become so vital in crisis response. It allows disaster management teams to gather large amounts of data, analyze them, and determine the best course of action to take.
One example of this is the response to the massive mudslide in Oso, Washington that killed dozens of people and destroyed 30 homes. While disaster response teams obviously want to go into an area as soon as possible, the situation might still be dangerous. Thats the problem emergency responders had once the mudslide happened. The weather conditions at the time prevented accurate surveying of the area, and the risk of other slides was great. Teams decided to send in a drone to collect data and create a 3D model of the area. From that information, responders were able to get an accurate picture of what needed to be done and how they should act. This not only lead to a quick response time, but it kept responders safe as well.
The same idea applies to the use of big data for disaster relief. Once a disaster hits, response teams want to be able to reach those who need help quickly. In the case of the 2011 earthquake and tsunami in Japan, for example, time was of the essence to get food, water, and other essentials to victims.
The same problem occurred in the wake of Hurricane Sandy in 2012. Such an undertaking is enormous in scope, especially for disasters of that size. Emergency responders turned to big data to help solve the problem, which pointed out which areas needed power, what the health needs to individual communities would be, where people were likely to go in cases of disaster, and much more. With this information at their fingertips, disaster teams were able to use big data analytics tools to coordinate their efforts, along with crowdsourced data initiatives, to get to those areas that were hardest hit.
Big data also plays a crucial role in finding missing people after disaster strikes. When an earthquake shook Nepal earlier this year, systems developed by Facebook and Google were used to help track missing loved ones. This live information that was constantly updated not only keeps friends and families in the loop, it helps emergency responders track down those people, possibly saving them from worse situations. These systems are themselves a response to earlier disasters (notably the 2010 Haiti earthquake), and theyre already paying dividends in saving lives and making recovery a faster process.
Disaster management is all about having the right preparation, quick response, and efficient recovery, all goals that are extremely hard to achieve without help. Big data is just the right ingredient to make those goals a reality. As emergency responders get more used to how to properly utilize big data, theyll become even more effective in their work. As can be seen in these examples, big data isnt just about increasing revenues or developing new products; it could very well save whole communities.
