How can data preprocessing techniques handle missing data effectively in data analytics?

Data preprocessing is a crucial step in the data analytics pipeline, particularly for handling issues like missing or incomplete data. Incomplete data can distort analytical models and lead to misleading or inaccurate conclusions. This topic falls under the subject of "Data Processing" and is crucial for anyone involved in data analytics or machine learning.