The Internet of Things offers nearly limitless opportunities for agencies, but the scope of this technology may create as many challenges as it does opportunities.
IoT is the concept that nearly anything — for instance, postal vehicles, office lighting fixtures and roads, to name just a few — can be equipped with sensors that collect information and then relay it over a network connection. Where that data ends up, and how it’s acted upon, is tackled through a process known as digitization.
Before IoT, IT professionals used the term digitization to describe the conversion of printed content, such as text and images, into digital form for easier sharing and archiving. With IoT, the term has taken on new meaning.
“Now, digitization is being used more broadly to encompass the use of digital information to transform business processes,” says Gary Hall, Cisco Systems’ federal strategy, planning and operations leader. “IoT is about generating the data, and digitization is about applying it — for instance, for automating or for making better decisions.”
For federal agencies, digitization is critical to maximize the return on their IoT investments. It provides the tools necessary to efficiently analyze and then act on the data that IoT generates.
As an agency’s IoT initiatives scale up, so does the volume of data the agency has to handle. Digitization helps agencies avoid the kind of information overload that stymies decision-making.
“IoT can create a data tsunami — and probably will,” says Mark Goodge, chief technology officer and IoT lead at the Defense Health Agency (DHA).
One way to manage mounting IoT data volumes is to define what data patterns are relevant and program a corresponding automated action. Automation rules can be set to alert a human only when data is outside of predetermined parameters or when the data starts trending in a specific direction. Alternatively, rules can trigger a system — such as the lighting and air conditioning in a smart building — to take action on its own (for example, turning out the lights in an empty room), with no human intervention.
Both approaches help reduce IoT-related costs such as the number of users required to act on the information. These approaches also reduce the fatigue that comes with fielding too many alerts, so users are better able to act on those that warrant their attention.
Some vendors, such as Cisco, IBM and Hewlett-Packard Enterprise, offer analytics solutions that sit at the network’s edge, fielding data from IoT devices. These nodes use techniques such as machine learning to decide whether to pass data on to other centralized analytics platforms or to human operators.
A benefit of this approach is to reduce data traffic, which, if left unchecked, can create a burden for agencies whose IoT projects use expensive or bandwidth-constrained connections such as satellite links or cellular networks.
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