Big Data Offers New Solutions for Disaster Mitigation Planning

Organizations are using big data to solve many of their most pressing challenges. Some big data applications have received considerably more attention than others. Marketing and finance are two of the functions that are most dependent on big data. However, there are other benefits of big data that are just as important but receive far less publicity. Disaster planning is one of the areas that can benefit from big data solutions. Big data can help organizations prepare for massive, unexpected disasters that can cost millions of dollars, as well as minor, recurring incidents that collectively have significant costs. Here are some ways that prudent organizations are utilizing big data for their contingency plans.

Preparing for mounting concerns about cybersecurity breaches

Cybersecurity is becoming a greater concern than ever. In 2016, cyber-attacks cost the United States economy between $57 billion and $109 billion. These costs are projected to continue growing in the years to come. Big data has played an important role in restoring cybersecurity for businesses of all sizes. There are several ways that predictive analytics is helping organizations prepare for these challenges:

• Predictive analytics models are helping organizations develop risk scoring algorithms. These algorithms can scan emails, file contents and other possible ports for cyber-attacks. They use machine learning technology to determine the likelihood that content is associated with such an attack.
• Big data helps organizations determine the likelihood that they will be the target of a security breach. They take a number of variables into consideration, such as the company?s industry, profitability, previous threats, the general public sentiment around the brand, news releases about new intellectual property developments and much more. If these algorithms indicate that there is a high chance the company will be targeted by a security breach, the company will understand that it needs to dedicate more resources into prevention.
• Big data has helped cybersecurity planning companies develop better detection solutions. It is able to carefully monitor internal activity to identify the likelihood of a threat that has penetrated the system. These solutions are able to alert network administrators in real time, so corrective action can be taken.

Big data cybersecurity solutions are going to be even more important as new malware and hacking threats surface.

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Developing new safeguards to protect various types of equipment

Organizations need to carefully protect their equipment. They often spend tens of thousands of dollars on new capital. Insurance providers might require them to have adequate safeguards to get compensated for any damages. Small devices also need to be carefully protected. Mobile phones are a prime example. Although a mobile phone might not seem as valuable as many other assets, mobile devices can store data that is incredibly valuable to the company. Therefore, companies are using big data to develop a better cell phone case to protect them.

Preparing for weather challenges

You have probably heard the old adage ?everybody talks about the weather, but nobody does anything about it.? While it is true that you can?t change the weather, you can use big data to help prepare for it and minimize the risk of disasters. Organizations need to be prepared for both one time and long-term weather threats. Predictive analytics technology is helpful for both. The Weather Company has said that predictive analytics is revamping the way they forecast the weather. New predictive analytics models are able to identify the likelihood of inclement weather up to a couple of weeks in advance. This helps organizations plan for events better. It also helps them assess the probability that a structure will be significantly damaged by any major weather event over a long interval of time. This will help companies plan the location of new facilities to avoid these challenges. This benefit is especially important for the construction of data servers, because they are more vulnerable to major weather incidents than other structures.