When it comes to emergency situations, even seconds can be a matter of life and death. Data analytics can help emergency services operate more efficiently, and quickly. Thus, data analytics can help us save lives. Why Focus On Emergency Call Centers? There are over 6,000 ambulance crashes in the United States each year. This may be a result of the constant pressure to respond promptly to emergency calls. The drivers of emergency vehicles feel pressured to meet rapid response targets, but this is dangerous, itself. This can create an entirely new emergency. Therefore, it would be highly beneficial to discover inefficiencies and time-saving solutions at the emergency call-center level, so drivers are not experiencing as much pressure. Traffic and congestion certainly are not helping this situation. This is especially the case, here, in the Denver area. I recently heard that Denver now has the 3rd worst traffic in the nation. This significantly slows down emergency response times. Unfortunately, this is not a quick fix, and emergencies need to be urgently and promptly addressed. Luckily, analyzing data is a much quicker solution than constructing new roads. Predictive analytics turn data into real-time recommendations, and it is proven that they help call centers reduce the response time. How? Emergency call centers gather data from a variety of sources. These may include geographic information systems, wireless communications, and global positioning systems. They may even be collecting data from other, third party emergency response organizations. For a long time, it was impossible to gather and combine data from all of these disparate sources. Now, with help from data analytics services, they can gather all of this data, despite its origin. Then, they incorporate it all into their single data visualization platform or dashboard. Therefore, the maps that dispatchers are looking at, are easy to read and understand. More importantly, they are constantly being updated, in real-time. Combining this with predictive analytics, the dispatchers can have teams ready to go in areas where it is most likely that there will be an emergency. Proof After implementing data analytics, a city’s average response time drops well below the national average. For example, Jersey City’s response time dropped from 8 minutes and 59 seconds, to under six minutes. When they can respond more quickly, they arrive more quickly. They also arrive safely. An emergency service organization, located just outside of San Fransisco also experienced successful results in using predictive analytics. The results include: reduced risk, improved service, increased profitability and even a decrease in calls for service. What’s the Cost? Although saving lives justifies the cost, we have to keep in mind that these too are organizations that have bills to pay. For many, this is an issue. For example the price tag for the predictive analytics tools and services for Jersey City was $250,000. However, advanced analytics services can also save you much, much more than you spend on the services. The insight doesn’t just save lives, its saves money too. They find inefficiencies that can be addressed, and provide insight that lead to solutions. Emergency call centers could save money in many areas. But, $250,000 is a bit pricey for a base service charge. However, there are other data solutions firms that will cost a fraction of that. And they will still pay for themselves, and help save lives.
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