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: 7 minutes 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 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: 6 minutes In my last post, “An intro to industrial data modeling”, I shared my definition of a data model and why data modeling is important for Industry 4.0. I’d like to take that a step further in this post by explaining why you need a dedicated abstraction layer for data modeling to achieve a data infrastructure that can really scale. Time to read: 7 minutes 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.
Time to read: 10 minutes
Most manufacturing companies realize the benefits of leveraging industrial data to improve production and save costs, but they remain challenged as to how to scale-up their pilots and small-scale tests to the plant-wide, multi-plant, or enterprise level. There are many reasons for this including the time and cost of integration projects, the fear of exposing operational systems to cyber-threats, and a lack of skilled human resources.
At the root of all of these problems is the difficulty of integrating data streams across applications in a multi-system and multi-vendor environment, which has required some degree of custom coding and scripting. Standardizing data models, flows, and networks is hard work. Unlike an office environment with its handful of systems and databases, a typical factory can have hundreds of data sources distributed across machine controls, PLCs, sensors, servers, databases, SCADA systems, and historians—just to name a few. Industrial DataOps provides a new approach to data integration and management. It provides a software environment for data documentation, governance, and security from the most granular level of a machine in a factory, up to the line, plant, or enterprise level. Industrial DataOps offers a separate data abstraction layer, or hub, to securely collect data in standard data models for distribution across on-premises and cloud-based applications. These four use cases illustrate how Industrial DataOps can integrate your role-based operational systems with your business IT systems as well as those of outside vendors such as machine builders and service providers. |
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