underlying data is in order. It usually is not.
Data is generated quickly, managed slowly, and nearly never retired on schedule across businesses. Teams in charge of compliance rush to rebuild audit trails. AI teams discover their models were trained on data nobody authorized or data that was already three years out of date. These are not edge cases. They are the norm.

How Data Lifecycle Management Supports Compliance and AI Readiness

Sakshee

Data Lifecycle Management (DLM) governs data from creation to destruction, ensuring it is clean, secure, and traceable. It prevents regulatory risks, lowers storage costs, and provides the reliable foundation required to scale trustworthy AI models.