Data Obsolescence occurs when information loses its intrinsic relevance, accuracy, or utility for its intended analytic purpose over time.
In industrial contexts, real-time sensor data vital for immediate operational control (e.g., predictive maintenance) can quickly become obsolete for long-term strategic analysis or machine learning training within days.
The core challenge is not mere storage capacity, but developing robust Data Governance strategies. These frameworks must define clear retention policies, manage necessary data archiving, and implement cleansing processes.
This comprehensive framework ensures that only high-value, actionable data persists, preventing flawed analytics and unnecessary infrastructure scaling.