Data Drift: Everything You Need to Know

Data drift refers to the changes in the statistical properties, distribution, and characteristics of data over time, which can significantly affect the performance of data-driven systems and models. Several factors contribute to data drift, including seasonal variations, changes in user behavior or data collection methods, and external changes like shifts in market trends. Understanding data drift is crucial for maintaining accurate predictions and decision-making processes. It can be categorized into three types: covariate shift, prior probability shift, and concept drift. Strategies for mitigating data drift include retraining models with new data, implementing data governance practices, and continuously improving data quality.