Original data often requires preprocessing before analytics can be performed. Common preprocessing steps include data cleaning to handle missing values or outliers, data transformation to reformat values for modeling, and data reduction to reduce dimensionality for easier computation. Preprocessing helps ensure data quality and prepares the data for downstream analytics tasks. (Smith, 2020)

1. Why are the original/raw data not readily usable by analytics
tasks? What are the main data preprocessing steps? List and
explain their importance in analytics.
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