What is a significant risk of not cleansing historical data before forecasting?

Not cleansing historical data before forecasting introduces significant risks, particularly in the form of forecast inaccuracies stemming from data errors and anomalies present in the historical dataset. Forecasting relies heavily on the accuracy and relevance of past data to predict future trends. If this historical data is tainted by inaccuracies, such as incorrect sales figures, outliers, or irrelevant information, the forecasts generated will likely misrepresent future conditions.

When data is not cleansed, patterns identified in the historical data may be flawed, leading to poor decision-making. For example, if there are anomalies due to data entry errors or unusual market events that are not accounted for, the forecast could either overestimate or underestimate demand. This can ripple through supply chain management, inventory control, and ultimately affect service levels and customer satisfaction.

This understanding highlights the importance of proper data management practices, as they form the foundation of reliable forecasting. By cleansing historical data, organizations can ensure that the forecasting model reflects accurate trends and dynamics, leading to better planning and strategic decision-making.