The Internet of Things (IoT) has been around as a concept for almost 2 decades now, with great promise and hype about tying far reaches of organizations into a single flowing network of interactive data. But there is still a lot of work to be done, especially in assuring that the data moving between the edge and more centralized systems is timely, viable, and accurate.
IoT keeps rapidly growing. The number of connected IoT devices was expected to grow 14% in 2025 and reach 39 billion in 2030 and more than 50 billion by 2035, according to estimates by IoT Analytics. AI is driving this renewed growth and interest, the report’s authors stated.
The epicenter of IoT has been, and continues to be, found within operational technology (OT), which are the systems that control physical devices. These range from production-floor machines to robots. In many cases, they exist in what are currently closed or air-gapped environments that aren’t necessarily directly attached to corporate networks. These systems are generating increasingly large volumes of data, which ultimately will fall within the purview of enterprise data managers and professionals. The challenge is to bring that OT data into the enterprise data management realm.
“OT is where IT becomes real. It’s where infrastructure directly impacts people, even if they don’t see it,” said Mark Christie, senior director of technical services at StorMagic.
IoT’S PROMISE
The most successful use cases for IoT—as well as its cautionary tales so far—are within OT areas, said John Q. Martin, technology partner and alliances manager at Redgate. “In general, many of the broad promises around ROI have been realized. However, on a granular scale, many organizations are hitting barriers. They’re able to collect data, but struggle to integrate it into their platforms, causing operational bottlenecks.”
Indeed, IoT success is based on better operational efficiency, as well as “being able to make smarter decisions from all of the real-time data devices generate,” stated Shankar Somasundaram, founder and CEO of Asimily. “Success has been more concentrated to certain verticals.”
Sectors such as manufacturing, energy, and utilities are further ahead with IoT, said Bakul Banthia, co-founder of Tessell. “Gains are appearing in industrial settings, especially around predictive maintenance and real-time quality control.” For utilities, “large-scale smart metering and grid-edge systems are already enabling faster fault detection, more precise load balancing, and dynamic pricing models that seemed impossible just 10 years ago. IoT is delivering value, but unevenly and often for surprising reasons.”
Add logistics, healthcare, and facilities management to industries in which IoT is delivering value. “Logistics and warehousing have made real progress, using device data to drive throughput and asset utilization,” said Somasundaram. “Healthcare has met its regulatory needs by connecting devices and transferring data more efficiently. Smart buildings are catching up as energy costs and sustainability reporting create stronger business cases.”
At the same time, however, other industries have further to go. “The enterprise-wide insights many predicted a decade ago are still uneven,” Somasundaram stated. It’s a matter of “achieving a contained device network environment and a clear owner for the data those devices are generating.”
Within process manufacturing, “the most useful applications are around real-time monitoring and early detection, knowing something is trending the wrong way before it becomes a shutdown,” said Molly Culwell, application engineer at dataPARC. “That is where edge systems have made a genuine difference, because you can act on data at the source instead of waiting for it to travel somewhere and come back as a report nobody reads until Tuesday.”
APPLICATIONS
In terms of applications, predictive maintenance is seen as the primary IoT use case with its straightforward ROI math, stated Ramon Guiu, VP of product at Tiger Data. “Catching a compressor failure 72 hours early pays for the entire platform. High frequency sensor streams can be correlated against historical baselines, surfacing deviations before they become incidents.”
Employing telemetry to “stop failures before they occur has generated massive ROI for many organizations,” Martin agreed. Fleet telemetry and asset tracking are also seen as compelling use cases. “We’re also seeing serious growth in infrastructure monitoring, structural health of bridges, water system pressure and quality, [and] grid load balancing,” Guiu said.
Christie cited additional IoT use cases now in action that include the following:
- Bridge control systems
- Power station monitoring
- Reservoir water pressure
- Air quality control systems
- Flight information display systems
- Remote asset monitoring
- Supply chain tracking
- Smart cities
- Healthcare monitoring
- Agriculture
- Environmental monitoring
IoT’S LINGERING ISSUES
Data quality and connectivity are the main inhibitors to successful IoT efforts. “Getting clean, consistent data out of older equipment is hard, and a lot of plants are running hardware that was never designed with any of this in mind,” said Culwell. “That gap between legacy infrastructure and modern data expectations is not going away quickly.”
Connectivity issues may have largely been addressed, but data infrastructure issues hold back IoT efforts. “The bottleneck today isn’t connectivity, it’s everything that happens afterward,” said Banthia. “Many organizations have built robust sensing layers, only to hit the wall with their back-end data infrastructure. High-frequency, time-series data gets funneled into systems never built for it, creating latency, inflating costs, and turning real-time use cases into something closer to ‘almost real-time,’ which in operational environments usually means too late.”