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Time to read: 6 minutes  Posted by Tony Paine In my last post, “An intro to industrial data modeling”, I shared my definition of a data model and why data modeling is important for Industry 4.0. I’d like to take that a step further in this post by explaining why you need a dedicated abstraction layer for data modeling to achieve a data infrastructure that can really scale.
Time to read: 7 minutes
 Post by Tony Paine
The data model forms the basis for standardizing data across a wide range of raw input data. An industrial DataOps solution like HighByte Intelligence Hub enables users to develop models that standardize and contextualize industrial data. In short, HighByte Intelligence Hub is a data hub with a modeling and transformation engine at its core.
But what exactly is a data model, and why is data modeling important for Industry 4.0? This post aims to address these questions and provide an introduction to modeling data at scale.
Time to read: 6 minutes
 Posted by Aron Semle
Let’s talk about getting OPC data into Microsoft Azure. When you search this phrase in Google, 90% of results provide this use case: streaming sensor data to the Cloud.
If your Industry 4.0 solution is streaming sensor data to the Cloud, you're doing it wrong. Now let me explain. On the factory floor, we have machines driven by PLCs, and we typically have an OPC server connected to those PLCs that feeds data into an HMI. OPC servers and HMIs work with tags, which are discrete streams of data. For example, one tag might be for pressure and another might represent the on and off state of the machine. When cloud technology like Microsoft Azure first entered the scene, vendors created IoT gateways to connect to the OPC server and send tag streams to the Cloud in a JSON format. It was the easiest thing to do, and once that connection was made, we thought we were done.
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. |
