Lack of data quality within digital engineering

Dark factory, fully automated manufacturing & processes, soulless #digital #engineering - a realistic "dystopia"?

No. At least not yet. And that's a good thing.

Because: there is a lack of #data quality in digital engineering.

With the current developments in #AI / #KI, people often rave about the incredible opportunities that automation and digitalization offer us.

I'm honestly as excited about the possibilities as many others and have always been a fan of digitization and automation.

BUT: Some clearly paint this picture too colorfully and one-sidedly. 💥

THERE ARE: As with all technological advances, there are risks and challenges that come with it. In this context, it is the data quality with which success in digital engineering stands and falls.

While ensuring this is not an obstacle to use per se, it is a task that should not be underestimated - despite all the enthusiasm for the possibilities that lie ahead.

👉 Let's make it concrete: 100% automation may seem economically tempting in the context of #manufacturing, for example, but it is an illusion as long as we humans are responsible for maintaining the data.

And that's just as well.

Automation systems can only access new information if it is available. They are still provided by humans. In the context of #ChatGPT, too, it is people who provide both the initial data basis and the concrete instructions for handling it (in the form of prompts).

The risk of errors and inconsistencies grows the more we blindly rely on machines and human intuition and experience fade into the background.

Insufficient data quality remains a factor to be considered and dealt with first before taking the next steps with AI and automations.

First learn to walk, then buy shoes 😉