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How to make industrial data fit for purpose
Seven steps as a practical guide to data management
Author: John Harrington, HighByte Co-Founder & Chief Business Officer
Introduction
It seems to me that most of the manufacturers I talk to are drowning in data, and yet they are struggling to make it useful.
A modern industrial facility can easily produce one terabyte of data each day. With a wave of new technologies for artificial intelligence and machine learning—on top of real-time dashboards and augmented reality—we should see huge gains in productivity. Unplanned maintenance of assets and production lines should be a thing of the past.
However, even in 2020, this is not the case. Access to all of this data does not mean it is useful. Industrial data is very raw. The data must be made “fit for purpose” to extract its true value. Also, the tools used to make the data fit for purpose must operate at the scale of an industrial facility.
With these realities in mind, here is a practical, step-by-step guide for manufacturers and other industrial companies to make their industrial data fit for purpose.
Seven steps as a practical guide to data management
Author: John Harrington, HighByte Co-Founder & Chief Business Officer
Introduction
It seems to me that most of the manufacturers I talk to are drowning in data, and yet they are struggling to make it useful.
A modern industrial facility can easily produce one terabyte of data each day. With a wave of new technologies for artificial intelligence and machine learning—on top of real-time dashboards and augmented reality—we should see huge gains in productivity. Unplanned maintenance of assets and production lines should be a thing of the past.
However, even in 2020, this is not the case. Access to all of this data does not mean it is useful. Industrial data is very raw. The data must be made “fit for purpose” to extract its true value. Also, the tools used to make the data fit for purpose must operate at the scale of an industrial facility.
With these realities in mind, here is a practical, step-by-step guide for manufacturers and other industrial companies to make their industrial data fit for purpose.
Improve industrial data integration with ETL software
Extract, Transform, Load (ETL) software can help improve data gathering for Operations Technology (OT) applications, but there are major challenges with data integration that companies need to overcome.
Author: John Harrington, HighByte Co-Founder & Chief Business Officer
Introduction
Most people are familiar with Industrie 4.0, Smart Manufacturing, and the Industrial Internet of Things (IIoT). These terms are used to describe the tremendous changes in operations technology brought on by a surge in underlying technologies including cloud, big data, smart sensors, single board solid state computers, wireless networks, analytics, application development platforms, and mobile devices.
Some of these technologies are not new, but recent price drops and improved ease of use have increased their usage. These technologies are being combined with traditional Operations Technology (OT) like control systems and Manufacturing Execution Systems (MES) to improve operations and business functions of industrial companies by providing more data—and tools to leverage that data.
Many of these technologies were first developed for Information Technology (IT) departments to interact with other business disciplines like marketing, sales, logistics, and finance. Given the vast amount of data in manufacturing and the need to improve operations, these tools are being evaluated and adopted by IT. However, operations teams looking to leverage industrial data face unique challenges around data integration, which have increased the effort required to deploy such systems.
Extract, Transform, Load (ETL) software can help improve data gathering for Operations Technology (OT) applications, but there are major challenges with data integration that companies need to overcome.
Author: John Harrington, HighByte Co-Founder & Chief Business Officer
Introduction
Most people are familiar with Industrie 4.0, Smart Manufacturing, and the Industrial Internet of Things (IIoT). These terms are used to describe the tremendous changes in operations technology brought on by a surge in underlying technologies including cloud, big data, smart sensors, single board solid state computers, wireless networks, analytics, application development platforms, and mobile devices.
Some of these technologies are not new, but recent price drops and improved ease of use have increased their usage. These technologies are being combined with traditional Operations Technology (OT) like control systems and Manufacturing Execution Systems (MES) to improve operations and business functions of industrial companies by providing more data—and tools to leverage that data.
Many of these technologies were first developed for Information Technology (IT) departments to interact with other business disciplines like marketing, sales, logistics, and finance. Given the vast amount of data in manufacturing and the need to improve operations, these tools are being evaluated and adopted by IT. However, operations teams looking to leverage industrial data face unique challenges around data integration, which have increased the effort required to deploy such systems.
Photo credit: Elle Darcy
