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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: 8 minutes  Posted by John Harrington How much time do you spend cleaning data?
If your factory is like most connected operations, you probably have tons of raw data streaming from connected devices to existing enterprise systems, bespoke databases, and a cloud data lake. This architecture often leads to inconsistent or even unusable data for several reasons. We know the Cloud is a key tool for digital transformation. It provides the scalability and storage capacity you need to collect and interpret vast amounts of data coming from the operations level. However, by nature, cloud platforms are IT-focused tools. They structure data differently than operational systems, which means IT must spend a lot of time cleaning the data before it can be used. And if the data moves directly to different enterprise systems, multiple teams across the organization will clean the data independently, leading to different versions of the truth.
Time to read: 7 minutes
 Posted by John Harrington
An executive for an industrial products company once told me even though his factories are full of similar equipment, he still struggled to access meaningful data from the machines. Each one of the plastic injection molding machines had a different way of presenting the data. That meant the company needed to customize coding for every piece of equipment to obtain meaningful insights.
It’s a common scenario in many industrial environments, where plants may have hundreds of PLCs and machine controllers on disparate machines generating operational data that is unintelligible to the data scientists who must make sense of it. This is where Industrial DataOps comes in. It provides a way to standardize data using common models, or object-oriented approaches, to integrate and manage information coming from multiple sources. Here’s a closer look at the top six signs it’s time to consider an Industrial DataOps architecture for your company. |
