HighByte Blog
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Read company updates and our technology viewpoints here.
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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 Aron Semle I love the chaos of an early market like DataOps for Manufacturing. It’s clear that things are changing, but what technologies and approaches will win out is less obvious. In these types of markets, as a solution provider, it’s equally fun to watch them mature.
One sign of a maturing market is the type of questions early customers ask about a solution. At first, the questions are different variations of “Does it work?” or “How is it different than a, b, or c?” as customers try and understand the solution and how it solves their problem. As the market matures, the questions shift focus to technical requirements like “What’s the performance with 10,000x?” or “Does it support high availability?” Here at HighByte we’re seeing more scale and reliability questions in early engagements, a sign that both the market and the product are maturing. That’s why I’m excited to announce some key features in version 2.1 that make HighByte Intelligence Hub more scalable and reliable to fit the needs of your production environment. Time to read: 7 minutes  Posted by John Harrington Manufacturers and other industrial companies adopting Industry 4.0 want to make industrial data available at scale across the enterprise to drive business decisions. Yet as these companies connect more processes, systems, and machines, their data modeling and integration needs have become more complex.
Industrial DataOps solutions like HighByte Intelligence Hub provide an answer to this complexity. The software provides a dedicated data modeling management and abstraction layer that helps users streamline their data architecture and reduce time to deploy new systems. In fact, as companies have expanded their usage of HighByte Intelligence Hub, they’ve begun to implement deployment architectures beyond a single hub. In a recent poll of HighByte Intelligence Hub users, we asked how many instances they plan to run at a single site. The results validated the demand for a multi-hub architecture: Half of the respondents expect to deploy two to five hubs per site; nearly one-quarter said they plan to use six to 10 hubs per location. Time to read: 5 minutes  Posted by Tony Paine Since releasing HighByte Intelligence Hub version 1.0 in January 2020, our customers have been successfully deploying solutions to simplify the integration of existing operational technology (OT) and new Industry 4.0 solutions that deliver rich information to IT, data scientists, and other stakeholders. Throughout the past year, we have focused on building out a connectivity library that enables users to connect to AWS IoT SiteWise, Azure IoT Hub and Event Hubs, REST, SQL, MQTT / Sparkplug, OPC UA, and CSV files, providing the market with the interoperability needed for Digital Transformation. In addition to connectivity, HighByte Intelligence Hub introduced a no-code approach to modeling assets, systems, processes, or systems of systems that are centrally managed and automatically transformed into a usable format for any one of our connectors. This has enabled customers to scale their plant to cloud initiatives in days and weeks, rather than months and years.
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