HighByte Blog
Read company updates and our technology viewpoints here.
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Read company updates and our technology viewpoints here.
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Time to read: 9 minutes In an earlier blog, “The power of payloads in your unified namespace,” I discussed the use of complex payloads combining multiple unified namespace (UNS) data streams to make the architecture more responsive to the diverse needs of consuming personas and systems. In this post, I want to show what these complex payloads might look like, how data models can enable a UNS architecture, and how easily HighByte Intelligence Hub can provide consuming systems with the necessary data—when and how it’s needed. Time to read: 9 minutes I consistently hear that many manufacturers are drowning in data and struggling to make it useful. Why is that? A modern industrial facility can easily produce more than a terabyte of data each day. With a wave of new technologies for artificial intelligence and machine learning coupled with real-time dashboards and prescriptive insights, industrial companies should be seeing huge gains in productivity. Unplanned asset and production line maintenance should be a thing of the past. But we know that is not the case. Access to data does not make it useful. Industrial data is raw and must be made fit for purpose to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial enterprise. For many industrial companies, this is a daunting task requiring alignment of people, process, and technology across a global footprint and supply chain. At HighByte, we’re putting our best foot forward to solve this data architecture and contextualization problem from a technology perspective. But what about people and process? To pull it all together, we recently published a new guide, “Think Big, Start Small, Scale Fast: The Data Engineering Workbook.” The guide provides 10 steps to achieving a scalable data architecture based on the best practices we’ve learned from our customers over the last several years. Time to read: 7 minutes The Unified Namespace (UNS) architecture pattern has proven to be an effective means to opening industrial data access up to the entire business, but the road to implementation is not without a few speed bumps. First, as industrial companies start to establish their hierarchy and build their UNS, they may find it difficult to get their data to follow their own rules. By its nature, UNS architecture draws from a multitude of different data sources, most of which present data in unique formats. Even superficially similar assets can format the data they generate in completely unique ways, and differences in data generated by wholly different machines, systems, and PLCs are even more stark. To limit problems in creating and operating a UNS, some industrial companies simply publish data from each system and device directly to an MQTT broker in their own topic namespace. This practice is not truly a UNS, and it offers little of the data accessibility and usability promised by this architectural pattern. Second, the UNS topic space typically follows the hierarchy: Site, Area, Line, Zone, Cell, and Asset. At each level, the information may include data from multiple systems including PLCs, SCADA, MES, CMMS, QMS, ERP, etc. On the consuming side, many users have unique needs that the UNS alone may not be able to meet. These challenges are what make consistent, easily scalable abstraction a critical part of your UNS. Time to read: 7 minutes The Unified Namespace (UNS) is among the fastest-growing data architecture patterns for Industry 4.0, promising easy publish-subscribe access to hierarchically structured industrial data. At HighByte, we define a UNS as a consolidated, abstracted structure by which all business applications can consume real-time industrial data in a consistent manner. A UNS allows you to combine multiple values into a single, structured logical model that can be understood by business users across the enterprise to make real-time decisions. But many industrials are finding that though they’ve loaded their device telemetry data in their UNS, they are struggling to use it. The UNS’ uniform data standards, hierarchical structure, and publish-subscribe pattern do an excellent job of providing easy, logical access to data, but business and analytics users often discover that they must subscribe to multiple data streams from separate levels of the hierarchy to get what they need for their applications. There are two problems with this approach: Time to read: 7 minutes 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. |
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