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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: 10 minutes
 Posted by John Harrington
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.
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.
Time to read: 6 minutes
 Posted by John Harrington
A modern industrial facility can easily produce a terabyte of data each day. With the proliferation of sensors and the recent wave of real-time dash-boarding, artificial intelligence, and machine learning technologies, we should be seeing huge productivity gains. Unplanned maintenance of assets and production lines should be obsolete.
But this is not the case. Access to data does not mean it is useful. Industrial data is very raw and must be made “fit for purpose” in order to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial facility. With these realities in mind, I’ve written a practical, seven-step guide for manufacturers and other industrial companies to make their data fit for purpose. |
