With nearly 10,000 people killed and more than 95 million affected, 2017 sadly marked a record-breaking year for natural disasters worldwide, ranging from Hurricane Harvey and the massive earthquake in Mexico to Hurricane Irma and the mudslides in Colombia. Unfortunately, 2018 is turning out to be equally calamitous.

Exacerbated by climate change, the years to come may have even more frequent and higher-impact catastrophes in store. But there is some good news in this disastrous scenario: the proliferation of data analytics and sophisticated technology promises to save lives in the face of disaster. As technology gets smarter, officials and scientists are able to analyze once-untapped information. The field of big-data analytics not only allows predication of disaster paths, but it also enables officials to optimize preparation—mapping evacuation routes, pinpointing flooded areas and formulating rescue strategies, for example. By embracing and analyzing big data, agencies can respond more quickly and effectively to the inevitable.


For better or worse, each disaster provides an enormous amount of data. Mining information from previous disasters, officials and responders can collect insights that help forecast future incidents. Combined with sensor data collection, surveillance and satellite imagery, big data analytics allows mission-critical areas to be surveyed and assessed. Knowing, for example, that a particular area has been flooded, and by how much, provides highly useful benchmarks for mapping out flood-prone zones and planning for where to store key rescue resources nearby—beyond the reach of typical inundation.

Through AI, for instance, Google is predicting flood patterns in India and working to bring greater precision and accuracy to response efforts. Drones are increasingly being used to collect data for contextual mapping when it comes to tackling wildfires. And satellite imagery proved its value when a couple’s plea for help spelled out of logs on their lawn was captured during Hurricane Michael, and the family was rescued.


Similarly, responders can better handle emergencies through the data generated by wearables and personal technology. Information transferred from mobile phone apps, smart watches or connected medical devices can be analyzed to help prioritize response and rescues, for example. When there is an abundance of 911 calls during a disaster, dispatchers can identify callers and make decisions about the urgency of each case based on relevant data, such as age and illnesses.

Survivors also turn to social media by geotagging locations or leaving time-stamps that help create a real-time picture of what is happening. Social media provides direct, valuable insights from the user and can alert officials to affected areas, road closures, power outages and more. Social media also collects data and allows survivors to mark themselves safe in times of crisis, helpful for both emergency response teams and worried friends and family alike. Government agencies can also utilize such social media to reach targeted users and provide accessible, large-scale early warning of disasters.


Location technology has become so fine-tuned that the exact position of a limited disaster can easily be identified by emergency staff—using the same technologies that our navigation apps use to such valuable effect. During the recent Carr Fires in Redding, Calif., for instance, officials used real-time maps to warn citizens of danger by showing the exact locations of the spreading fires.

Moreover, in the case of an unfolding emergency, location technology is so advanced in smart phones and IoT devices that responders can determine exactly where a caller is situated. This is especially important, considering that, according to estimates, some 10,000 people could be saved every year in the U.S. alone if emergency services adopted such precise location technologies. Data and mapping technology, for example, can show responders if the location of the caller is accessible by car, foot, boat or helicopter, and enable them to plan the best way to carry out their rescue.

This was put into practice during Hurricane Harvey when a truck driver was stuck inside his vehicle in rising waters. He was not accessible by car and had to be rescued by boat from the middle of a freeway, with no fixed address. Data, measurements and mapping technology showed responders the depth of the surrounding waters, because to the blind eye, it would have been impossible to know where the street lay beneath the flooding.

In an era of data-producing connected devices, what matters is not only the quantity of data collected but also how these data are managed and analyzed. Technology and big data analytics aided by artificial intelligence are transforming disaster relief efforts by enhancing prediction and preparation abilities, and by accelerating response time and enhancing responders’ ability to operate efficiently even when resources are scarce. Equipped with the right data management strategy, governments can be smarter about preparing for disasters and improving aid efforts—when every second counts.