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Time to read: 7 minutes  Posted by John Harrington An executive for an industrial products company once told me even though his factories are full of similar equipment, he still struggled to access meaningful data from the machines. Each one of the plastic injection molding machines had a different way of presenting the data. That meant the company needed to customize coding for every piece of equipment to obtain meaningful insights.
It’s a common scenario in many industrial environments, where plants may have hundreds of PLCs and machine controllers on disparate machines generating operational data that is unintelligible to the data scientists who must make sense of it. This is where Industrial DataOps comes in. It provides a way to standardize data using common models, or object-oriented approaches, to integrate and manage information coming from multiple sources. Here’s a closer look at the top six signs it’s time to consider an Industrial DataOps architecture for your company. Time to read: 14 minutes  Posted by Torey Penrod-Cambra If you know me well, then you’ve probably heard me say words matter. A shared vocabulary—and a shared understanding of a word’s meaning—is a simple but powerful tool when two bodies approach a problem from different perspectives.
Two bodies that often approach problems, projects, and process from different perspectives are IT and Operations Technology (OT). While the industrial automation community has been writing and discussing the necessity of IT-OT convergence for nearly a decade, this functional collaboration still remains a stumbling block for many industrial companies on their Industry 4.0 journeys. The good news is that the emerging concept of Industrial DataOps can provide some common ground. DataOps is a new approach to data integration and security that aims to improve data quality and reduce time spent preparing data for use throughout the enterprise. Industrial DataOps provides a toolset—and a mindset—for OT to establish “data contracts” with IT. By using an Industrial DataOps solution, OT is empowered to model, transform, and share plant floor data with IT systems without the integration and security concerns that have long vexed the collaboration. If we see the value in IT-OT collaboration, the first step is getting these functions to speak the same language. This post aims to document key terms surrounding Industrial DataOps and provide IT and OT with a common dictionary. Some of these definitions are more technical in nature and others are more business oriented. Let’s dive in. Time to read: 4 minutes  Posted by John Harrington In my last post, “Seven steps to making your industrial data fit for purpose”, I briefly covered seven steps that are critical for manufacturers looking to scale their IIoT projects and wrangle data governance. I’d like to use this post to dive deeper on step 4, selecting your integration architecture, which requires diligence during IIoT planning.
Integration architectures fall in two camps: direct application programming interface (API) connections (application-to-application) or integration hubs (DataOps solutions).
Time to read: 6 minutes
 Posted by John Harrington
A modern industrial facility can easily produce a terabyte of data each day. With the proliferation of sensors and the recent wave of real-time dash-boarding, artificial intelligence, and machine learning technologies, we should be seeing huge productivity gains. Unplanned maintenance of assets and production lines should be obsolete.
But this is not the case. Access to data does not mean it is useful. Industrial data is very raw and must be made “fit for purpose” in order to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial facility. With these realities in mind, I’ve written a practical, seven-step guide for manufacturers and other industrial companies to make their data fit for purpose. |
