AI models are only as good as their training data. A data strategy creates the prerequisites that AI projects need: clean, documented, accessible data in a unified format. Without this foundation, models fail due to contradictory customer definitions, missing values, or data that may not be used at all.

Specifically, an implemented data strategy delivers three things: first, a platform where data is available for training and inference. Second, governance that clarifies which data may be used for which purposes. Third, a data catalog that lets data scientists find data without having to ask each department individually. In this project, 73 percent of data was AI-ready for the first time after implementation. The foundation for the next projects.