What is the result of not addressing changes in data characteristics over time?

Not addressing changes in data characteristics over time can lead to degraded model performance. This phenomenon occurs because many machine learning models are trained on historical data that may not represent future conditions accurately. If the underlying data trends, distributions, or patterns change, the model may struggle to make accurate predictions or decisions, leading to a decline in performance.

For instance, if a model trained on customer purchase data from several years ago is not updated to incorporate recent trends, it may fail to account for shifts in consumer behavior, such as a sudden preference for online shopping or changes in seasonal buying patterns. This disconnect can result in outdated insights and predictions that do not align with current realities.

In contrast, other outcomes such as data enrichment, data inconsistency, and data redundancy focus on different aspects of data management. Data enrichment pertains to enhancing the dataset to improve insights, while data inconsistency involves conflicts within the data, and data redundancy is about duplicating data unnecessarily. However, these factors do not directly address the challenge of adapting to evolving data characteristics, which is why the primary concern in this situation is degraded model performance.