Is machine learning (ML) or artificial intelligence (AI) the key? Companies have worked on many ways to offer plug-and-play sensor packages to collect information, with multiple options to send it where ever it needs to go. To reap the benefits of a higher-performing data network such as IIoT, ML, or Big Data, an interdisciplinary communication network is essential. "OT professionals are focused on keeping manufacturing, plant, and physical equipment in operation for extended periods of time, while IT professionals focus on keeping data flowing and accessible to all facets of an organization," says Dariol.

With the Industry 4.0 factory automation trend catching on, data-driven artificial intelligence promises to create cyber-physical systems that learn as they grow, predict failures before they impact performance, and connect factories and supply chains more efficiently than we could ever have imagined. To avoid IIoT digital exhaust and preserve the potential latent value of IIoT data, enterprises need to develop long-term IIoT data retention and governance policies that will ensure they can evolve and enrich their IoT value proposition over time and harness IIoT data as a strategic asset. A practical compromise IoT architecture must first employ some centralized (cloud) aggregation and processing of raw IoT sensor data for training useful machine learning models, followed by far-edge execution and refinement of those models. A multi-tiered architecture (involving far-edge, private cloud and public cloud) can provide an excellent balance between local responsiveness and consolidated machine learning, while maintaining privacy for proprietary data sets.