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Time to read: 6 minutes  Posted by John Harrington Data modeling is all about standardization. It enables interoperability, shows intent, determines trust, and ensures proper data governance. Given the criticality of usable data at scale for Industry 4.0, many manufacturers have turned to ISA-95—probably the most commonly recognized data-modeling standard around the world—for guidance.
Created by a standards committee at the International Society of Automation, the ISA-95 specification defines in detail the electronic information exchange between manufacturing control functions and other enterprise functions, including data models and exchange definitions. The purpose of ISA-95 is “to create a standard that will define the interface between control functions and other enterprise functions based upon the Purdue Reference Model”. Per the committee, the goal is to reduce the risk, cost, and errors associated with system integration. Historically, ISA-95 has been the guide for many off-the-shelf and bespoke manufacturing execution systems (MES). Today, ISA-95 also helps industrial organizations implement data integrations that link MES, enterprise resource planning (ERP) systems, IIoT platforms, data lakes, and analytics solutions. It also eases the implementation of a unified namespace (UNS) for enterprise data integration. The specification defines a hierarchal model for systems, detailed information models, and a data flow model for manufacturing operations management (MOM). Let’s take a look at these 3 key attributes in more detail and uncover how the ISA-95 specification can be applied within HighByte Intelligence Hub. 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. |
