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Time to read: 4 minutes  Posted by John Harrington In my last post, “Seven steps to making your industrial data fit for purpose”, I briefly covered seven steps that are critical for manufacturers looking to scale their IIoT projects and wrangle data governance. I’d like to use this post to dive deeper on step 4, selecting your integration architecture, which requires diligence during IIoT planning.
Integration architectures fall in two camps: direct application programming interface (API) connections (application-to-application) or integration hubs (DataOps solutions).
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. Time to read: 8 minutes  Posted by John Harrington The manufacturing industry is experiencing a change so significant it has earned the title of Fourth Industrial Revolution. This transformation was kick-started by the need to become more data driven and then fueled by a number of recent technological advances. Early adopters in factories around the world recognize that industrial data—operations data coming from machines, processes, products, and systems on the plant floor—is gold. More users and systems want access to this data in real time to convert it into valuable information they can act on to predict machine failure, prevent downtime, and improve product quality. In fact, IDC recently projected that there will be 41.6 million 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. It’s an immense, even overwhelming, volume of data. How can companies leverage it effectively?
Updated: 03/25/2020 Time to read: 9 minutes  Posted by Torey Penrod-Cambra Earlier this year, my colleague John Harrington wrote an article for Control Engineering that I think is worth sharing here as well. The article introduces a concept and process that gained popularity as early as the 1970s: Extract, Transform, Load—or more commonly known as ETL. An ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions (Kimball and Caserta, 2004). So why are we still talking about this acronym 50 years later? Because the unique challenges of working with industrial operations data demand a new look at an old concept.
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