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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. Updated: 03/25/2020 Time to read: 9 minutes  Posted by Torey Penrod-Cambra Earlier this year, my colleague John Harrington wrote an article for Control Engineering that I think is worth sharing here as well. The article introduces a concept and process that gained popularity as early as the 1970s: Extract, Transform, Load—or more commonly known as ETL. An ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions (Kimball and Caserta, 2004). So why are we still talking about this acronym 50 years later? Because the unique challenges of working with industrial operations data demand a new look at an old concept.
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