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: 11 minutes Since the third industrial revolution, manufacturers have been slowly but steadily automating their operations. From the PLC to ERP to Computer-Integrated Manufacturing (CIM), industry has adopted myriad advancements in technology to gain an edge and, sometimes, even to survive. Rapid technological advancement led to a slew of devices, applications, and services that were rarely introduced or managed under a holistic technology strategy. Time to read: 8 minutes Hannover Messe 2024 is just around the corner with the exhibit floor opening Monday, April 22 in Hannover, Germany. If you’ve never attended, Hannover Messe may truly be the world's leading trade fair for industrial technology, hosting more than 4,000 exhibitors and 130,000 on-site attendees each year. In this post, I’ll share a preview of what you can expect to see and hear from HighByte at the fair, including software demonstrations, product news, theatre presentations, and more. Elevate your industrial interoperability: A primer on data Pipelines in the Intelligence Hub3/8/2024
Time to read: 15 minutes In HighByte Intelligence Hub, the Pipelines feature was created to make modeled data consumable by a diverse range of applications and services. With the last few releases of the Intelligence Hub, Pipelines has undergone big changes to further that goal and more. From adding new functionality to refining the UX, Pipelines has swiftly evolved beyond its initial focus on “post-processing” payloads for advanced use cases. It has become a core data engineering capability to solve industrial interoperability problems within the Intelligence Hub. Time to read: 10 minutes One of the most common concerns I hear regarding the Unified Namespace (UNS) is the architecture lacks the versatility needed to address diverse downstream data consumers. Suppose quality, maintenance, and process engineers are all doing their part to support a production line. Quality teams need inspection results, maintenance teams need asset performance data, and process engineers need lot and process parameters. The teams need data sets that both overlap and differ by use case. These engineers are using different applications and services—anything from ERP modules for quality and maintenance to specialized ML platforms in the cloud—that each require very different data structures. Many of these applications and services do not easily interoperate with the UNS and its architectural conventions. They may not natively interface with MQTT brokers, nor should one expect them to. They may not consume payloads that were oriented around rigid asset hierarchy and publishing telemetry data from process control nodes. They may have completely different needs than what was envisioned when factory automation was installed and integrated. Their data needs can transcend how the UNS was initially architected and organized. HighByte Intelligence Hub can overcome these challenges. Through data modeling and pipelines, the Intelligence Hub enables the full potential of the UNS, delivering contextualized manufacturing data to the cloud. Let’s look at a sample architecture to see how. 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: 8 minutes For quite some time, people have been asking us how the Intelligence Hub relates to a Unified Namespace (UNS). Is it a specific part of a UNS architecture, a platform by which one might build a UNS, or a UNS architecture itself? Over time, our answers have developed alongside the capabilities of the Intelligence Hub. From the beginning, the Intelligence Hub could connect to third-party MQTT brokers as well as model the data going in and out of them. And recently, we added an embedded MQTT broker to the Intelligence Hub to address brokering and provide the ability for MQTT clients to connect to the Intelligence Hub directly. These provided the functionality needed for much of what might facilitate or be considered a UNS, but it was missing a critical part: a way to visualize the contents. 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: 6 minutes Ever since the first release of HighByte Intelligence Hub in 2020, HighByte has developed the solution to meet the industrial data integration needs of today’s industrial customers and tomorrow’s market requirements. The first version of the Intelligence Hub was a client-based application that collected and published contextualized data to any consuming system. As Industry 4.0 leaders began to integrate more systems and assets into their ecosystems, data consumers needed better data visibility and access. The consumers wanted to be able to see all available information, so they could access exactly what they needed. To deliver this visibility and access, we embedded an MQTT broker into the Intelligence Hub, giving administrators the necessary tools to build a Unified Namespace (UNS) that would allow consumers to easily subscribe to the information they desired. With changing needs in mind, in May 2023, we took the next step in the evolution of the Intelligence Hub, adding the ability to request data on-demand through the Intelligence Hub with a built-in REST Data Server. This addition allows users to request time series, transactional, or master data from systems through a single, simple API. Time to read: 8 minutes As industrial organizations across the globe work toward digital transformation, many find the lack of readily accessible and usable data to be major barriers to success. Manufacturers want to use operational data to drive automated decisions from machinery to the Cloud and put better information in the hands of business leaders. But depending on their data maturity, manufacturers often find that:
The result of these challenges is the inability to scale. Even projects that manage to get rolled out are difficult and expensive to maintain, and therefore digital transformation goals are not met. Time to read: 9 minutes From our very first release in early 2020, HighByte Intelligence Hub has developed alongside the needs of Industry 4.0 pioneers. We have focused our efforts on addressing the most pressing challenges faced by industrial companies as they integrate systems and modernize their architectures. Today, industrial information networks encompass more sensors, devices, and systems than ever before, indicating an exciting level of connectivity being adopted globally. To facilitate and accelerate this adoption, we are excited to announce Intelligence Hub version 3.1. Intelligence Hub version 3.1 includes powerful data accessibility, governance, and scaling capabilities from a broad swath of new features, each of which extends the reach of the Intelligence Hub to even more producers and consumers of industrial data. Time to read: 7 minutes You don’t have to agree with environmental policies to know that sustainability is a part of business and life today. Supply chain partners, regulators, customers, and investors are demanding more environmental accountability from manufacturers—and with good cause. According to the International Energy Agency, the manufacturing and power sectors account for 63% of energy-related CO2 emissions worldwide. Progress depends largely upon their success. Thankfully, manufacturing has come a long way since the third industrial revolution that saw a rise in automation and productivity without much consideration for environmental impact. The fourth industrial revolution, or Industry 4.0, has given manufacturers more insight into their operational efficiencies. Network-connected assets provide a real-time lens into performance metrics that go hand-in-hand with more sustainable production. Still, this level of connectedness presents a new challenge: How to manage data more efficiently. Time to read: 7 minutes For the past several months, 55 beta testers in 13 countries have been kicking the tires on HighByte Intelligence Hub version 3.0 and generously providing their feedback. Today, I’m excited to announce this major release is now available. Version 3.0 is a step change for the Intelligence Hub and for the Industrial DataOps market. It raises the bar for what a DataOps solution can be at Enterprise scale. It introduces a powerful new Pipelines builder to curate complex data pipelines. It makes the often-vague concept of the Unified Namespace (UNS) tangible and achievable with an embedded MQTT broker—reducing additional software, cost, and administration overhead for our customers. I sat down with HighByte Chief Product Officer John Harrington to talk about some of these advancements available in Version 3.0, including Pipelines. His thoughts are below. I also provide insights from our partner Goodtech, a deep dive on the embedded broker, a review of new project management capabilities, and more. Time to read: 6 minutes Have you ever watched a press conference when a room full of reporters bark questions at the same time? Typically, the media event host will call on a particular reporter to repeat the question and then move on to the next person in the room. Without some ground rules, an actual conversation couldn’t take place. No one could hear the questions being asked, and few would get any answers. Unfortunately, this same scenario often occurs with industrial data. With so much operational technology (OT) data generated on any given day, manufacturers risk losing critical information in the sea of “data noise” coming from their systems or having to expend vast resources to clean that data in the cloud. Time to read: 8 minutes 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 I’m excited to announce that HighByte Intelligence Hub version 3.0 is now available in beta. The release is packed with powerful new capabilities, including a fully integrated MQTT broker, enhanced Central Configuration, more intuitive user experience, and many more. These capabilities will enable you to rapidly deploy the data infrastructure you need to build a Unified Namespace (UNS), scale these deployments, manage the environment, and meet your advanced Industrial DataOps use case requirements. Time to read: 8 minutes "We're not in Kansas anymore." In fact, we’re in Chicago at IMTS, the largest North American Industrial Technology show of the year, where we’re announcing the release of HighByte Intelligence Hub version 2.5! The latest version has come a long way since its original release in early 2020. What began as a data hub for OPC to MQTT has evolved into an Intelligence Hub that truly lives up to its name. The Intelligence Hub satisfies real, complex business use cases and enables enterprise IT management of industrial data. Manufacturers have realized that publishing 750,000 data tags (or even 75,000 data tags) straight to the cloud at a 1 second rate does not solve real business problems for the enterprise and often creates a data swamp. Instead, industrial data must be collected, transformed, aggregated, and delivered to the cloud as curated data payloads. In HighByte Intelligence Hub version 2.5, we’ve provided unparalleled support for Microsoft Azure with the addition of Azure IoT Edge connectivity, support for Azure IoT Central, and the ability to import DTDL models from Azure Digital Twins. We’ve also also added support for third-party JavaScript functions, input cache management, instance referencing, and many more features to improve the coverage of more complex use cases that customers need to solve. As industrial data has become the major source of data for the broader business to drive improvement and new lines of revenue, the systems feeding the cloud for these initiatives must fall within the enterprise IT infrastructure. With the latest release of the Intelligence Hub, we’ve added support for Active Directory and improved ability to monitor and alert when data is bad or stale and when flow performance is poor. This post provides an overview of the release, highlighting new connectivity, monitoring, management, and advanced use case capabilities. Let’s take a look. Time to read: 6 minutes 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: 9 minutes It’s been inspiring to see the wide variety of ways customers are using HighByte Intelligence Hub to conquer Industry 4.0 use cases that previously seemed impossible. From creating contextualized electronic batch reports to improving first run yield, predicting asset maintenance, performing real-time analytics on UNS data, and gaining enterprise-wide performance visibility across multiple sites with different systems—customers are using the Intelligence Hub in increasingly more sophisticated ways. With more sophisticated use cases comes the need for more sophisticated tools for scalability and connectivity in the Intelligence Hub. That’s why I am so excited to introduce HighByte Intelligence Hub version 2.4. With new instance and input templates and parameters, global functions, custom conditions, OPC collection, and more, the latest release takes a giant leap forward in terms of scalability and data pipeline automation. I sat down with my friend and colleague John Harrington, Chief Product Officer at HighByte, to learn more about version 2.4 and what these new capabilities will mean for our customers. 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. Introducing the Intelligence Hub version 2.3: Data conditioning, data pipeline monitoring & more4/5/2022
Time to read: 6 minutes Real-time performance monitoring is a crucial element of the Industry 4.0 revolution. Advanced analytics allow you to view or even predict anomalies in operational processes, such as asset performance, defects, or production bottlenecks in real time. But what about the integrity of data flows? As you become more reliant on analytics, disruptions to data collection, modeling, preparation, and delivery will reduce your operational agility, and could cause production delays, scrap, and other business challenges. You need confidence that you have reliable data when you need it. This is where HighByte Intelligence Hub version 2.3 comes in. The latest release of the Intelligence Hub enables you to condition raw data, view flow and connection status in the user interface, and easily monitor data pipelines at scale using the Intelligence Hub or your preferred third-party system-monitoring applications, like Splunk and Datadog. The release is packed with enhancements that make developing and troubleshooting data pipelines easier than ever. Time to read: 5 minutes If you’re moving industrial data to the cloud, you’re probably aware that AWS offers a breadth of services to store, catalog, analyze, and share operational information. Cloud services have become a key enabling tool in digital transformation because of their ability to store, process, and analyze large amounts of data in a centralized location. HighByte recently joined the AWS Partner Network to help manufacturers contextualize and deliver data from their connected systems to the cloud with seamless efficiency. As stated by Sandrine Périno, Manufacturing and Industrial Global Partner Lead at AWS, “The Intelligence Hub delivers a modern approach to integrating factory floor systems with AWS cloud services. HighByte complements our edge strategy and aligns with our mission to optimize operations for Industrials by providing highly contextualized data ready for analysis.” Manufacturers use cloud services differently depending on their specific needs, so we wanted to highlight the four primary ways our customers are connecting the Intelligence Hub to AWS. They include: Time to read: 5 minutes The real value of Industry 4.0 is realized when manufacturers unlock the power of analytics. With the addition of artificial intelligence (AI) and machine learning (ML), organizations can transform raw data into predictive, meaningful insights. But manufacturers must first overcome a structural barrier to connect their process historians with their analytical applications. This is where HighByte Intelligence Hub comes into play. The latest release of the Intelligence Hub extends connectivity from enterprise systems to historian and time-series database applications, including PI System and InfluxDB. It removes a major disconnect between operational technologies (OT) and the business systems where organizational leaders access the information to make strategic decisions. Time to read: 3 minutes We live in a time where attacks on critical infrastructure and the underlying software and hardware that comprise these systems is all too common and will only increase year over year. Rest assured that HighByte is committed to putting security first in its design and implementation of its software solutions. Our industry recognizes that a defense-in-depth strategy must be employed when building out a technology stack from various components. This not only applies to an end-user’s use of applications and equipment from various vendors, but even more so by vendors who develop solutions that pull in third-party technology or tap into interfaces and standards that allow for seamless integration with foreign sources of data and information. |
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