bigdata2_featIn the first part of this series, we looked at the definition of Big Data and the business benefits that companies are beginning to see from this new technology. Today, we drill down on what types of security data might make up a Big Data strategy, with an eye toward what types of analytics might help extract value from that data.

But first, let’s go back for a moment to two key phrases: “Big Data strategy” and “extract value.” They are inextricably linked. In other words: no strategy, no value. Whatever your company hopes to achieve with Big Data, you need to have a strategy, or you’ll find that you’re not capturing and storing the data you need to deliver results. More on this later.

The Usual Data Sources, Revisited

We all think we’re pretty familiar with the types of data generated by physical security systems…or are we? The fact is we ignore most of it because there is so much minutia that it would be either too tedious or outright impossible to analyze with traditional tools.

The endless activity logs generated by access control systems are a great example. Or the millions of motion detection events that come from even a modest-sized deployment of video surveillance cameras. What about the sensors deployed to monitor doors, windows, cabinets and anything else that can be opened or traversed? In an enterprise of any size, there is very quickly such a mountain of routine events that we don’t analyze them for larger patterns.

This is where the familiar structured query approach of most report generation tools lets us down. They fail to reveal the bigger patterns that emerge from large data sets and better analytics. That’s because the whole thrust of structured queries is to reduce a large set of records to a smaller set of records, possibly with a few calculations thrown in. They are just not designed to detect global patterns and long term trends. That’s where Big Data tools come into play.

Let’s say that my enterprise has 500 locations, and I am trying to understand how all of these facilities compare to one another over the past five years. The first problem will probably be that the local security systems are an archipelago of pre-cloud information islands. Assuming it’s possible to aggregate that data, or work with a cloud provider who can, there are many new types of questions that can now be answered:

  • How do daily security patterns compare across my facilities?
  • Is traffic flow the same this year as last year?
  • What signals precede an actionable security event?
  • What’s the year-over-year change in visitor-to-employee ratios?
  • Which facilities exhibit the most off-hours video events?
  • Where are administrative privileges changed most often?
  • How do the preventive maintenance signals compare?
  • Which locations stand out this week? This month? Every month?
  • What is the seasonal variation in security events? Is it the same everywhere?
  • How do my large facilities differ from my small facilities?
  • What are the differences in data distributions across the enterprise?
  • How do my physical security data correlate with other data sources?
  • Are there differences in compliance?

And the list could go on, all using discrete data types, without even beginning to have the discussion of how video analytics at this scale can alter the value extracted from an enterprise security data repository. At this level, we can simply regard video analytics as another input to other Big Data tools.

 

The Not So Usual Data Sources

Mixing in other data sources can enhance what we can learn from security data. This should be part of an overall Big Data strategy in order to gain the maximum business benefit from the investment. To name a just few possible sources: HR, compliance, network, certifications, sales, maintenance, shrinkage, safety, weather, and a laundry list of others that will depend entirely on what your business does, and what you want Big Data to do for you.

 

Getting Started

The first thing to do is find out whether your security systems are collecting the types of data you will need to support your Big Data strategy (which, as we said last time, involves sharpening our pencils before anything else). If not, you will need to work with your vendors, augment existing systems, or possibly switch vendors in order to establish a suitable source of data for later analytics. The key question is: Can you marshal all of the relevant data into one (virtual) place so you can extract the value?

 

This is where cloud storage and cloud providers of Big Data services come in handy. They allow you to move data into the right environment and store as much of it as you want, without having to make a huge initial investment in servers or software.

 

It’s as if the cloud and Big Data were made for each other.