HighByte Blog
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Time to read: 10 minutes  Posted by John Harrington The promise of Industry 4.0 has many manufacturing leaders thinking big. They envision a future in which real-time access to data opens the door to unprecedented levels of operational flexibility, predictability, and business improvement. For many, early-stage wins often lead to larger projects that stall or fail to scale because their data infrastructure couldn’t support the increasing project complexity.
Enter Industrial DataOps. DataOps (data operations) is the orchestration of people, processes, and technology to securely deliver trusted, ready-to-use data to all the systems and people who require it. The first known mention of the term “DataOps” came from technology consultant and InformationWeek contributing editor Lenny Liebmann in a 2014 blog post titled, “DataOps: Why Big Data Infrastructure Matters.” According to Leibmann: “You can’t simply throw data science over the wall and expect operations to deliver the performance you need in the production environment—any more than you can do the same with application code. That’s why DataOps—the discipline that ensures alignment between data science and infrastructure—is as important to Big Data success as DevOps is to application success.” Time to read: 7 minutes Posted by John Harrington The unified namespace (sometimes referred to as the UNS or universal namespace) can be an allusive concept for many of us as we move to an Industry 4.0. world. At HighByte, we define the UNS as a consolidated, abstracted structure by which all business applications are able to consume real-time industrial data in a consistent manner. The benefits of a UNS include reduced time to implement new integrations, reduced efforts to maintain data integrations, improved agility of integrations, access to new data, and improved data quality and security.
We are often asked if HighByte Intelligence Hub is a UNS. The answer depends on your priorities and project scope. We typically see three architectural patterns for implementing the UNS. HighByte Intelligence Hub can play a key role in each approach by both providing access and structure to the UNS or acting as the UNS. 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: 7 minutes  Posted by Omar Ahmed Based on my conversations with more than 500 manufacturing companies and integrators over the past five years, I believe the Industrial Internet of Things (IIoT) will continue to be a paramount part of the manufacturing landscape in 2021. The new year will bring a continued increase in digitalization across enterprises. While we have seen an increase in “digital transformation” initiatives among manufacturing companies for several years, the COVID-19 pandemic and the challenges it created for production, safety, remote access, and supply chain have accelerated the urgency to make digitalization a reality. I also believe IIoT projects will continue to scale because of changes we are seeing in people, processes, and technology.
Here are five predictions for 2021. |
