[Workbook] 10 steps to designing an industrial data architecture for scale | SPONSORED
Many manufacturers are drowning in data and struggling to make it useful. A modern industrial facility can easily produce more than a terabyte of data each day. With a wave of new technologies for artificial intelligence and machine learning coupled with real-time dashboards, we should see huge gains in productivity. Unplanned asset and production line maintenance should be a thing of the past, but even today, that is not the case. Access to data does not make it useful. Industrial data is raw and must be made fit for purpose to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial enterprise.
With these realities in mind, here is a practical, 10-step guide with a few short questionnaires to help manufacturing and industrial leaders better understand the process of applying proven data engineering processes and technologies to make their data fit for purpose.
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