HighByte Blog
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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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