7 trends impacting commercial and industrial IoT data

This is turning out to be an active year for advances with Internet of Things technologies, especially in the area of commercial and industrial IoT. To put all the advances in perspective, let’s look at seven top trends that are driving this space, from compute size, to the value of true edge computing, to closed-loop edge to cloud machine learning and more.

1. Survival of the smallest. IIoT analytics and ML companies will be heavily measured on how much they can deliver in how little compute.

As IIoT projects pivot away from cloud-centric approaches, the next step in the evolution of artificial intelligence and IIoT will address the need to convert algorithms to work at the edge in a dramatically smaller footprint.

According to , within the next four years, 75% of enterprise-generated data will be processed at the edge (versus the cloud), up from <10% today. The move to the edge will be driven not only by the vast increase in data, but also the need for higher fidelity analysis, lower latency requirements, security issues and huge cost advantages. While the cloud is a good place to store data and train machine learning models, it cannot deliver high fidelity real-time streaming data analysis. In contrast, edge technology can analyze all raw data and deliver the highest-fidelity analytics, and increase the likelihood of detecting anomalies, enabling immediate reaction. A test of success will be the amount of “power” or compute capability that can be achieved in the smallest footprint possible. As with all hot new technologies, the market has run away with the term “edge computing” without clear boundaries around what it constitutes in IIoT deployments. “Fake” edge solutions claim they can process data at the edge, but really rely on sending data back to the cloud for batch or micro batch processing. When reading about edge computing, the fakes are recognized as those without a complex event processor (CEP), which means latency is higher and the data remains “dirty,” making analytics much less accurate and machine learning (ML) models are significantly compromised.

“Real” edge intelligence starts with a hyper-efficient CEP that cleanses, normalizes, filters, contextualizes and aligns “dirty” or raw streaming industrial data as it’s produced. In addition, a “real” edge solution includes integrated ML and AI capabilities, all embedded into the smallest (and largest) compute footprints.

The CEP function should enable real-time, actionable analytics onsite at the industrial edge, with a user experience optimized for fast remediation by operational technology (OT) personnel. It also prepares the data for optimal ML/AI performance, generating the highest quality predictive insights to drive asset performance and process improvements.

Real edge intelligence can yield enormous cost savings, as well as improved efficiencies and data insights for industrial organizations looking to embark on a true path toward digital transformation.

Moving machine learning (ML) to the edge is not simply a matter of changing where the processing happens. The majority of ML models in use today were designed with the assumption of cloud computing capacity, run time and compute. Since these assumptions do not hold true at the edge, ML models must be adapted for the new environment.

In other words, they need to be “edge-ified”. In 2019, “real edge” solutions will enable relocating the data pre- and post-processing from the ML models to a complex event processor, shrinking them by up to 80% and enabling the models to be pushed much closer to the data source. This process is called edgification, which will drive adoption of more powerful edge computing and IIoT applications overall.