Data Certainty During Times of Uncertainty

How data can help citizens predict and prepare for natural disasters

Mon Jul 02 10:47:00 EDT 2018

I’ve lived in New Hampshire my entire life. The state is known for its quaint towns, vast green landscapes, skiing, and best of all…its maple syrup. But New Hampshire is also known for its extreme winters and heavy snowfalls. On average, Mount Washington experiences snowfall nearly one-third on the year. For New Hampshirites, snow is just a part of life, and residents have found ways to cope with the complexities that often accompany it.

One of those complexities happened to a friend of mine this year—a complexity that should’ve never happened. Let me tell you about it.

Anyone who has ever bought a home understands the never-ending amount of paperwork involved. In order to meet all the necessary loan requirements, we’re often pulling documents from many agencies and organizations, sometimes all over the state or country. Some of these documents are easily acquired online, but others must be sent through a mail service.

My friend was just days away from closing on the home when the local weatherman began to report an impending snow storm. “Stella,” as the storm would be called, was scheduled to be one of the largest storms to ever hit New Hampshire, with multiple feet accumulating overnight. For my friend, the worst part of this news was that it was set to hit just days before he closed on the house.

Locals began to prepare for the storm by stocking up at the grocery. City officials closed government offices and schools. Transportation officials began salting roads and placing snowplows throughout the city. And the mail carriers halted all services, hunkering down in the warmth of their homes and off the icy roads.

Stella turned out to be just as bad as predicted, but it was especially tough for my friend. Because the mail carriers halted operations, he did not receive the necessary paperwork to close on the house in time – in fact, he didn’t close on it for several weeks after, costing him time and money.

I wish I could have told my friend that what happened to him wasn’t the norm—it was just a fluke!—but unfortunately, it happens all too often. Almost every weather-related event has a direct impact on the shipping industry, and few are doing enough to predict, prepare, and combat the impact.

Take for example Hurricane Harvey. The storm was predicted, as was the reach and devastation that it would cause. But despite this forewarning, distribution centers across Texas were shut down completely for extended periods of time. Because of this, residents experienced extreme delays on receiving packages that contained critical things like insurance-recovery information, financial documents, and even life-sustaining medications.

As we enter hurricane season, businesses must better prepare to keep their customers safe and equipped when disaster strikes. Fortunately, Insurance providers have already adopted many practices that other industry organizations can replicate to prepare for weather-related shipping delays.

Here are four key pieces of data information and activity organizations need to adopt immediately:

  1. Historical Data is the logical starting point for any organization wanting to better predict weather-related risks. If an area is prone to natural disasters, it should be identified as a high-risk area. Similarly, demographic information should be assessed to understand the potential needs of residents within that area.
  2. Pre-storm Alerting ensures that all measures have been taken to prepare for the worst. Organizations can leverage forecast data to notify high impact regions of potential shipping delays. Forecast data is also useful for planning post-storm response as it becomes increasingly clear which areas will need the most assistance as forecasts stabilize. For instance, if an area has a high population of elderly citizens, mailing services can expedite high-priority packages pre-storm, such as mail order prescriptions. 
  3. Post-storm Analysis allows us to continuously improve crisis response plans. By conducting an analysis of the crisis response to a weather-related event, organizations can better understand what challenges residents faced following the storm, and they can better determine if their crisis response provided a solution to these challenges.
  4. Predictive Modeling combines historical data and analytics to help organizations prepare to address the needs of their customers. If we understand an area’s history, the people who live there, and how we’ve previously responded, we can better predict best practices to implement in the future. For example, if an area experienced extreme shipment delays from a previous storm, an organization might need to ship packages further in advance, or hire additional staff to ensure packages are processed and shipped on time.

Weather is unpredictable, but the way in which we respond and prepare doesn’t have to be. In times of crisis and uncertainty, an organization’s ability to get consumers the resources they need in advance of the storm can have an incredible impact on the people they serve. Hurricane season is ahead of us, and it’s still too soon to predict what will happen. But with the right data, analysis and modeling, we can predict how we will respond.