Evaluating forecasting models requires more than measuring accuracy. Proper evaluation involves selecting appropriate metrics, validating models over time, and understanding the context of predictions.

Many data science initiatives fail not because of algorithms, but because of poor problem definition, weak data quality, and unrealistic expectations about machine learning.

Understanding why a model makes a prediction is often as important as the prediction itself. Interpretable analytics helps organizations trust results, identify key drivers, and make better strategic decisions.

Forecasts are never perfectly certain. Understanding prediction uncertainty helps organizations make better decisions and manage risk in planning and operations.

Machine learning is powerful, but not every problem requires complex models. In many cases, simpler statistical or analytical approaches provide more reliable and interpretable solutions.