HighByte Blog
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Time to read: 8 minutes  Posted by Torey Penrod-Cambra All eyes are on manufacturing these days. Global leaders see manufacturing as the engine powering a wide range of initiatives—from infrastructure development to energy efficiency. Their focus on industrial growth and sustainability shouldn’t be surprising when you consider that manufacturing accounts for roughly 17% of the global GDP and 23% of direct carbon emissions. The reprioritization of industrial investments around the world is good news for manufacturers.
Are you ready for the bad news? Manufacturers lag other sectors by a significant margin when it comes to data management. Enterprise Strategy Group (ESG), a division of TechTarget, surveyed 403 technical and business data professionals at organizations in North America to assess the state of DataOps in 2022. They defined DataOps as “improving the quality, delivery, and management of data and analytics at scale.” The study looked at market maturity, challenges, factors influencing buying and planning decisions, and business benefits among those surveyed. The findings were telling. Time to read: 6 minutes  Posted by Torey Penrod-Cambra When it comes to data collection, who are you really serving?
That objective often gets lost amid the OT/IT alignment discussions. Anyone who has embarked on a digital transformation project is likely familiar with the data silos that exist between their OT and IT departments. But we don’t spend enough time talking about how to make that data usable for the line of business. Our line of business colleagues (and their systems of record) are the ultimate customer. The use of IoT-enabled devices is increasing the availability of operational data. IDC has projected there will be 41.6 billion IoT devices in the field generating 79.4 zettabytes of data by 2025. These devices include machines, sensors, and cameras as well as industrial tools. To truly make that data usable, we need to merge this data with information from other systems and provide context for line of business users. In an industrial environment, these users include quality, maintenance, engineering, R&D, regulatory, and product management. Time to read: 7 minutes  Posted by John Harrington The efforts of standards organizations like OPC Foundation, Eclipse Foundation (Sparkplug), ISA, CESMII, and MTConnect represent a significant step forward for the advancement of Industry 4.0 in manufacturing.
But industry standards only go so far. Businesses need data to tell the story of what is happening, why it is happening, and how to fix it. Multiple pieces of information must be assembled with other pieces of information from other sources to tell the use case story—just like words must be combined into sentences and sentences combined to form stories. Data standards can’t tell the use case story—they can only provide a dictionary. Standardizing the device-level data into structures is key, but only the beginning. Data standards alone will not solve your interoperability problems because they don’t provide the use case related context you need to make strategic decisions. Here are four key reasons why you still need an Industrial DataOps solution like the Intelligence Hub—even with the introduction or evolution of new standards. 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.” |
