The phenomenon in predictive analytics, data science, and machine learning where the statistical properties of the target variable change over time in unforeseen ways, invalidating the data model and causing predictions to become less accurate as time passes.

• Healthcare
• Finance
• Marketing
• Manufacturing
• Transportation

For instance, a company that uses a machine learning model to predict customer churn based on historical data may find that the model becomes less accurate when a new feature is introduced, such as a change in billing practices or the introduction of a new product line.

• Semantic Data Model – Introduction