Amal leans back and looks at her notebook. Three pages filled. Plus four whiteboards covered in post-its from the requirements workshop. Somewhere in all of that are the business objects she needs for the new data model. "Diego," she asks, "how long did it used to take you to figure out what actually needed to be modeled after a workshop like this?" Diego smiles. "Back in the day? Sometimes a week."
AI systems require perfectly structured data but cannot create the necessary data models themselves. Why does even the most powerful AI fail to understand what a "customer" or "product" means in a specific company? And why is precisely this definition work the key to success for every AI implementation?
Artificial intelligence is currently revolutionizing virtually every business area. Yet amid all the enthusiasm for these technologies, a fundamental paradox is often overlooked: AI requires high-quality, structured data to function at all. At the same time, AI itself is unable to create the data structures it needs to work.
We have celebrated a special moment: In August, Roelant and I signed the first copies of our “Special Edition” of Data Engine Thinking for the people who have supported us from the beginning – especially for the supporters of our originally planned limited edition.
Whether you’ve been following our journey from the beginning or are just curious what all the fuss is about, we’d love for you to check it out.
How can I use natural language in my modeling process to achieve high-quality information models?
And there it was again, the lively discussion about the use of a Data Vault Link Satellite. The argumentation, whether yes or no, went round in circles.
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