An agency needs to review new data after it has implemented policies and procedures to see if the new data fits the outcome it had hoped for. When an agency adds policies and analytics to improve operations, resource management, and other organizational areas, it needs to continually reassess the outcome data to see if improvements have been made. Reduced danger to public and officers from unnecessary high priority response.More efficient use of resources (e.g., false alarms can wait).Predictive analytics allow for prioritization of such events and are a more efficient use of resources.Īgencies can align response priorities with likelihood, allowing for benefits, including: Since, we know that agencies always respond to events, we encourage our customers to use predictive analytic models like machine learning to potentially create a reliable probability indicator. Now that there’s a better understanding as to why and how agencies use business intelligence and predictive analytics like stream analytics and machine learning, I would like to discuss the benefits of using predictive analytics in police, fire, and EMS agencies.Īgencies are required to respond to events, including, as I discussed in my previous blog post about machine learning, burglar alarms, which are mostly false. Specifically, I’ve looked at how many public safety agencies are becoming data-driven decision makers and reviewed some examples of how agencies use real time analytics to help them predict the future. Over the last few weeks, I’ve done a deep dive into business intelligence within public safety agencies as well as how agencies are trying to use business intelligence to make communities and employees safer.
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