The Foundation Of AI Is All In The Data

Marc Kermisch is Chief Technology & AI Officer at Protolabs, a digital manufacturing service for accelerated production of quality parts.

Few topics are getting as much focus in manufacturing as artificial intelligence. Which tooling should you adopt? Which models work best? How do you maximize ROI? How quickly can it all happen?

It’s exciting to be part of an industry going through such significant change, and it’s easy to see why manufacturers are eager to invest in this new technology. However, it’s also easy to assume that AI can simply be unleashed and expected to work.

Before they jump in, companies need to build a strong foundation first, prioritizing connectivity, process and, most importantly, data management.

Clean Data: What Is It, And How Do You Get It?

I’ve found it’s best to start with process and data governance. A data catalog can define data elements, explain how they are categorized and describe how data flows into the organization. A data catalog can provide the context for AI to give accurate answers consistently. Without that context, the same question may produce different answers on different days. If AI receives poorly structured data, the outcome is unlikely to be clear.

On the factory floor, one machine might measure productivity by hours running, another might measure by parts produced and another might measure by a different metric altogether. If AI is asked about plant productivity, it may not know how to reconcile those definitions and could return inconsistent or inaccurate answers. In this example, if the data is clearly defined, well-managed and “clean,” AI can use it consistently and produce repeatable answers. With work, it can do this across disparate systems.

Manufacturing Data Is More Challenging

Enter The Digital Thread

Putting Improved Data Systems Into Practice

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