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Big Data Applications For Physical Security Posted on Sep 13 - 2017

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In the world of physical security, now driven so heavily by cutting-edge technology, there is information coming in from a multitude of data points. From the control room, security personnel and facility professionals are on the receiving end of massive amounts of information from various sensors and systems, from reports from access control systems on current badge swipes, video clips from surveillance cameras, and reports on vehicles — sorted by color, make or model — that entered parking lots in the last 24 hours.

So large and complex a data pool that neither individuals nor everyday computing systems can handle it, big data, as it is often referred to, is an aggregation and analysis of these data streams to find anomalies that people and many machines wouldn’t be able to find on their own. It can even identify problems an enterprise may not even know it had. The key is finding a way to better harness and understand it.

Many companies such as Google, Amazon, and Facebook are already mining big data based on people’s actions online and with social media, using sophisticated algorithms that automate the collection, analysis, and related actions based on the results of those efforts. These companies see the tremendous value in this data differently than most businesses currently do.

With its increasingly sophisticated management platforms that continually gather data, the security industry has the opportunity to leverage this information as well. Being able to harness data from physical security systems and break it down into more usable packets can not only enhance its use for forensic security purposes, but also to offer some predictive tools so users can make proactive decisions.

Every sensor within a system has data to share. Whether it’s fire, intrusion, or access control, the information can be used for its own security-related purposes, but it also can be used to indicate patterns or key learnings. This means going beyond just gathering and recording data from a system to find out, for example, the peak hours of operation for an access control system and receiving instead key learning that can give predictions about future usage and needs. View More

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