From IoT sensors to “small data,” these technologies are the building blocks on which effective situational awareness strategies are built.
Situational awareness essentially refers to one’s capacity to perceive, analyze, and act upon events within a particular operational context. Successful decisions are never made in a vacuum — one must always respond to the relevant information at hand. That holds true whether you’re deciding to cross a busy street, making the call to send a team of firefighters into a burning building, or determining how to optimize organizational dynamics in a Fortune 500 enterprise.
The contemporary definition of “situational awareness” first arose within the context of tactical military decision-making, where understanding the threat landscape is a top priority. Today, across an increasing number of industries, organizations have begun to implement operational processes and technologies aimed at developing organizational situational awareness.
For an array of non-military organizations, focusing on addressing and improving situational awareness has been revolutionary. It has enabled public safety and event security stakeholders to orchestrate purposeful protocols designed to improve emergency response times. In the transportation and logistics sector, a focus on improving visibility has enabled leaders to create efficiencies and boost profits.
While the people part of the equation still represents probably the most important aspect of situational awareness, technology has become an increasingly crucial component as well. With the right tools, it’s possible for organizations to get real-time data and vastly improve their situational awareness. These are the five most important technologies that organizations can employ to optimize their situational awareness and start making smarter decisions, sooner.
The Internet of Things (IoT) refers to networked physical assets like sensors, drones, vehicles, and cameras. An asset can be considered an “IoT device” if it is designed and built for connectivity — like a “smart” gate, for example. Various IoT devices can gather and transmit relevant data on situational metrics like location, temperature, usage, and status.
This data can feed into and enable other systems, as with autonomous industrial machines. Or it can be leveraged for human oversight and decision-making. IoT devices like connected security cameras, for instance, can transmit information to law enforcement officers and first responders, enabling better traffic management and predictive policing. As another example, a “smart border wall” composed of IoT devices like drones and ground sensors could enable border patrol agents to effectively control illegal immigration.
Edge computing has emerged as a faster, more cost-effective way to manage, analyze, and activate the data generated by IoT devices. Typically, raw data collected by a sensor is sent back to a centralized cloud network to be analyzed or stored. However, for time-sensitive applications — and certain smart technologies, like self-driving cars — this gap can introduce an unacceptable level of latency that may even preclude the viability of the use case at hand. When it comes to public safety or emergency response, for example, waiting for centralized processing can compromise the mission.
With edge computing, data processing occurs on devices at the tactical edge of the network — in other words, out in the field — and can function with better efficacy in locations with limited or intermittent connectivity. This decentralized approach decreases the amount of time — not to mention the expense — required to process data in the cloud, and contributes to faster decision-making (i.e. a shorter OODA Loop). Because edge computing allows raw data to be processed as it’s generated, operatives get the information they need in real-time. That means teams in the field and in the C-suite alike can make (better) decisions against the latest, most relevant information.
GIS mapping capabilities provide spatial awareness and in many cases offer the most effective way to package and visualize data, as decision-makers can instantly orient themselves spatially without the need to cross-reference other sources.
Essentially, GIS provide a dynamically-updating geographical context for crucial data. Digital maps enable an aggregate and spatially contextualized viewpoint of data on potential threats, specific hazards, target objectives, or critical resources — in other words, spatial awareness. Teams can track the movement of individuals, units, or vehicles, along with critical information on infrastructural assets like power lines or fire hydrants.
Packaging data within GIS is an effective means of allowing the right information to be surfaced at the right time — and to the right people. A firefighter battling a four-alarm fire in downtown Chicago doesn’t need to know that a smoke alarm was set off by a microwave in Oak Park. GIS naturally allow this information to be sorted, prioritized, hidden, and surfaced in a manner conducive to dealing with the threat(s) at hand.
The visual overview that GIS provide can be applied to industries as diverse as transportation, event security, or car rentals. When dealing with hazardous situations, GIS insights are necessary to plan safe, efficient movement. And mapping can help first responders understand the context of an emergency before they arrive on the scene.
Small data means, quite simply, the right data. More data isn’t necessarily better — when, for example, unnecessary alerts distract decision-makers from higher-priority objectives. Moreover, higher data volumes and velocities require more complex analytics and more expensive hardware to generate actionable information. Indeed, after a decade of businesses focusing on “big data”, many are realizing that massive data sets can often be overkill. For an operation that needs immediate, actionable information, the answer has to be small data — the right data — or in other words, just the data that you need.
Allen Bonde provides an excellent definition of small data. A leader in the tech industry, Bonde explains that small data allows for “timely, meaningful insights,” which are “organized and packaged — often visually — to be accessible, understandable, and actionable for everyday tasks.” While in some cases big data can be scaled for human decision-making, it’s often possible to reach similar conclusions faster, with less data. This is key for operations focused on real-time situational awareness — you likely don’t have time to process large data sets or sort through extraneous information.
The concept of small data is about designing organizational processes so that the technology is delivering only the information that allows decision-makers to choose and implement the best course of action. You want to deliver the right information, not all the information. When it comes to an organization’s operational procedures, it should prove helpful to ask: “What kind of data should we actually take into account?” and “Are we surfacing information that has no real value?”
Technology can deliver groundbreaking improvements toward better, faster decision-making — but not without a situational awareness platform. Only with a lynchpin that integrates and activates real-time data are these kinds of mission-critical insights possible. If a gun-shot detector throws an alert inside a data silo, it won’t help save lives. Drone video footage of an escaping suspect won’t lead to an arrest unless it’s delivered to the officers in position to act.
Situational awareness software is the foundation on which the above technologies must function. The right software can integrate diverse data sources from heterogeneous assets into a common operational picture, and deliver real-time intelligence designed for immediate human comprehension — and action.
Situational awareness solutions can help organizations turn big data into small data; all the data into the right data; the grand scale into the human scale. With purpose-built situational awareness software, everything from IoT sensors and edge devices to GIS and complex data sets can be leveraged into the information that organizations need to take meaningful action.