FogHorn, a Silicon Valley-based startup, is one of the early movers in the IIoT and edge computing market. The company has raised a total of $47.5M in funding over four rounds. The latest funding came from a Series B round in October 2017 by Intel Capital and Saudi Aramco Energy Ventures. Founded in 2014, FogHorn has been squarely focused on edge analytics and edge intelligence. According to the company, its solution enables high-performance edge processing, optimized analytics, and heterogeneous applications to be hosted as close as possible to the control systems and physical sensor infrastructure that pervade the industrial world.

Edge Computing enjoys a very respectable place in the current IT Transformation journeys. Together with AI, Machine Learning, IoT and Robotic Process Automation, Edge has become the most-discussed topic among global CIOs and IT leaders. According to Forrester's Predictions 2020: Edge Computing, the'edgification' of IT and Automation will become a predominant factor of differentiation between the leaders and laggards in the Cloud Infrastructure and Cloud Computing landscape. IT systems that helped you sail through the challenges last decade are in no shape to assist your business goals. ITOps have evolved significantly, and today CIOs place a much larger emphasis on ease of deployment, speed, security and scale of automation using emerging technologies.

Technological shifts in the industry are needed to continue to meet demand and deliver profits. Traditionally information technology (IT) and operational technology (OT) staff have worked on opposite sides, siloed from each other – not overlapping on projects or deployments. However, in the world of industrial IoT (IIoT) that approach has been flipped, demanding that these departments be entirely in sync and aligned. With Gartner's prediction of 60% of IIoT analytics coming from IIoT platforms coupled with edge computing, OT/IT convergence is necessary to prepare for this influx of IIoT analytics for continued, if not improved, performance and output from refineries and drilling operations, along with individual machines involved in those operations. One concern for aligning teams on IIoT investments and deployments is the differences in languages used.

The IoT is transforming the industrial sector, enabling dramatic gains in efficiency and productivity. But to capture these benefits, you need a way to analyze the high volume of diverse streaming data coming through your machines in real time, and interpret it for actionable insight. Increasingly, this means deploying machine learning, but the question is how to do so. While the cloud has merit as a data modeling and machine learning portal, it cannot always provide the real-time responsiveness needed in applications for the manufacturing, oil and gas, construction, transportation, and smart buildings industries. Thus, there has been a move to augment the cloud with machine learning at the edge.

A couple of weeks ago, I spent a few hours at GE Digital's headquarters in San Ramon, CA. It was a great overview by several executives of how GE is using their Predix platform to create software to design, build, operate, and manage the entire asset lifecycle for the Industrial IoT. A big part of this transformation for GE involves hiring tons of software developers, acquisitions, and partnerships. One of those partnerships is with Silicon Valley based FogHorn Systems (GE Ventures, Dell Ventures, March Capital and a few others are investors). FogHorn is a developer of "edge intelligence" software for industrial and commercial IoT applications.