Jack Jansonius published an article, “When Data Doesn’t Know What It Means.”
Many enterprise data systems suffer from a hidden problem: the data no longer “knows” what it means. Over decades, business meaning has been fragmented across processes, rules and technical structures, making systems increasingly opaque and difficult to control.
Meaning-driven architecture offers an alternative. By explicitly modelling goals, decisions and domain concepts, systems become transparent, testable and easier to govern. Instead of hiding logic in process flows or rule sets, decision tables make reasoning explicit—providing a stable semantic foundation for both traditional IT and AI. Link
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