Dataset preparation

Dataset preparation is the critical process of cleaning, transforming, and structuring raw, disparate data into a high-quality, analysis-ready format for BI and machine learning models.

This foundational technology ensures data integrity: garbage in, garbage out is not an option. Data engineers and scientists consistently report that preparation consumes 70-80% of total project time (Source: Industry reports). The process involves distinct, non-negotiable steps: data cleansing (handling missing values, removing outliers), data transformation (normalization, aggregation), and feature engineering. For example, a raw customer dataset with 20% missing email fields must be imputed or dropped; date formats must be standardized (e.g., ISO 8601) across all sources. Successful execution directly correlates to model performance, boosting predictive accuracy and delivering reliable, actionable business insights.

https://www.techtarget.com/searchenterpriseai/definition/data-preparation