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.

Or consider this scenario: a customer in a metro/downtown area in a large commercial high-rise has an access control system that integrates with the turnstiles in the ground floor lobby. Somehow a couple of turnstiles continuously break down and the customer starts to suspect the issue is quality related due to the recurrence of issues in these specific turnstiles. Using data from the access control system it was determined that the turnstiles in question saw far more traffic than other turnstiles. As a result of the information contained in this data, the customer was able to make changes to spread the load out more evenly between all turnstiles.

The most prolific supplier of raw data in this industry is video surveillance, supplying not only the viewable image itself, but a nearly limitless amount of data culled from each element of the scene. This is called metadata, and all analytics rely upon the generation of metadata to apply algorithms in order to draw conclusions.

The conclusion may be detecting a face, recognizing a face, determining the direction or speed of an object, characterizing the difference between objects, determining color, and so on. But all of the analytic algorithms rely on the generation of metadata. It is this generation of metadata that not only enables the video analytics, but will serve as a foundation for monetization of intelligence and insight for business and operational insight.

As users seek to refine and analyze the data, the tools they use have to be intelligent enough to do that and in a meaningful way. For example, this can mean going beyond telling a retailer that peak hours are between noon and 1 p.m., but also draw conclusions around weather and traffic. As the goals of the user are more fully defined, these intelligent systems that process and analyze data from video streams can also make the decision as to whether reduce staff levels or bring on additional resources.

HVAC sensors combined with video sensors can create complementary data to measure occupancy patterns and usage of areas to determine when to turn lighting on or off or when to operate heating and cooling. That way a building that will soon be unoccupied is not continuously being heated at a higher level, which in turn creates savings.

Big Picture Issues With Big Data

The cloud environment that supplies the massive processing power and storage capacities required by big data presents both opportunities and concerns for end-users and systems integrators navigating this new environment. As the scale of stored information grows, so too does the stakes of a cybersecurity breach. End users involved with big data need the assurance that the third-party repository storing their data is secure and that privacy of their information is a primary focus.

For example this includes making sure the data is partitioned in way so that it stays in a certain region or country, as privacy laws vary by region and certain countries in the world have very strict privacy laws on data.

As the reliance on and use of big data continues, integrators will find themselves in the position of dealing with it in some way. Although some may take on the role of helping to manage big data, most will likely find the biggest value comes in understanding it and helping customers select a system that can both generate and, through its network, manage the data.

Dealing with a cloud-based enterprise also brings security together with IT, so integrators should either have the personnel in place to deal with this, such as hiring someone with a data-focused background, or be able and willing to interface with someone beyond the security director within the business they are servicing. And there are also additional costs to consider with implementing and maintaining these systems.

Big data management presents opportunities for integrators to become part of the value chain, even if they are not the one handling the primary function of collecting, transmitting, and analyzing data. But it also requires the understanding that this is a paradigm shift from selling hardware to selling a service with various features and capabilities.

Who among an integrator’s clientele is most likely to be a big data user? Currently, enterprises with high-risk profiles such as casinos, airports, energy-generating facilities, and pharmaceutical companies can be counted among those businesses that would benefit from in-depth analysis of their data.

The day will come, however, when big data will have an impact beyond key verticals and for those who have gotten in ahead of curve, the rewards will be greatest.

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Big data exploration usually starts at a high level of data abstraction, and then gradually plumbs into the depths of the data as companies learn more from it.

The approach has worked well, and is operative in many different types of applications.

For instance, GIS and mapping systems use data to visualize a big picture map and then to focus in on a specific point or location. As the data analyst drills down to this location, they can then look at other related data that might be appended to the location such as the demographics of individuals who live at that location, or the number of traffic accidents at that location.

However, there is also another ground up approach that has the ability to unlock hidden values of big data. This approach actually starts at the lowest level of the data and then works its way up to more sophisticated data structures to deliver data insights that are helpful to management and staff.

Here is an example:

“A single pixel display can reveal the visible color of a point, but also the infrared value, which can be used to measure vegetative health,” said Layton Hobbs, research and development director and vice president at Woolpert, an architecture, engineering and geospatial solutions firm.

Hobbs is talking about the potential of agriculture and forestry companies to go beyond basic geospatial data that they collect and unlock hidden treasures that are buried in geospatial data such as data on topography, soil, ground cover, plant health, and tree canopies.

“Most geospatial data is created for one specific reason or need, but there is so much more information in geospatial data that is underutilized or not recognized,” added Woolpert’s associate and geospatial discipline leader, Joe Cantz. “Particularly with the newer technologies, the data-rich information is growing exponentially, but we are using only a small percentage at this point.”

According to Woolpert officials, geospatial data pixels are capable of storing a much wider range of values than the traditional 256 values of an 8-bit image. “These modern systems often store four bands of data (red, green, blue and infrared) at up to 12 bits or around 4,000 values for each band,” said Hobbs. “Combining those four bands for image interpretation creates 256 trillion possible combinations at one spatial location! This is definitely overkill for most applications but shows the potential for big-data applications of imagery.”

Why does this matter for company big data projects?

IoT data, such as data captured and emitted by sensors, immediately comes to mind.

With IoT, you can start with your own top-down big data initiatives and analytics when it comes to utilizing data and imagery that gets sent from sensors on board drones—but what if you looked into each individual pixel of data that the drone was sending back—and discovered that there was additional data value captured that could answer questions that you weren’t interested in today, but could be in the future?

Here’s how you can optimize data for both current and future use:

Analyze what is possible to extract from a given unit of data (e.g., a pixel), even though you may not care about all of this information today.

This can be easily done. Referencing Layton Hobbs’ example, maybe you don’t care about the health of the forest floor today, but if you one day want to restore this forest after a harvest, understanding something about forest health will help. At that point, knowing everything you can obtain from your big data under management becomes significant.

Catalog the information capture that is possible at the lowest unit of big data.

If you are dealing with a pixel and you know that forest health and topography is possible to analyze from this data and you make a record of it, it is much easier to remember the information potential of your data and to activate it if and when you need to.

Don’t lose yourself in the details

While it is important to catalogue the information potential of your big data at the lowest level of data, it is equally important not to lose yourself in the details. If your job today is simply to map a forest and to identify stands of harvestable timber, stick with that. Don’t get off course with other types of data explorations that aren’t relevant to the task at hand.

Anticipating lessons learned

When I was running a marketing department for a bank, we used demographics for one of our checking campaigns by identifying persons in certain locations by age group, and then linking checking products to the various life cycle stages that customers were in. Later, we wanted to improve results, and we added occupation as well as age for targeting our checking products.

This is a common scenario for companies. They want to go back to the data to see if they can add more information so they can improve results.

By assessing and cataloguing the potential information yield of big data at the lowest level of the data, data analysts can be poised to open up the data to more comprehensive analytics that can unlock the answers to questions that the company will want to ask next.

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