A systematic framework for preprocessing validation in business analytics

Purpose Recent studies have identified methodological concerns in data science research, including unvalidated preprocessing assumptions that may contribute to inflated performance claims and irreproducible results. Despite preprocessing consuming 60–80% of analytical effort in business analytics projects, strategy selection typically relies on convention rather than systematic evidence. This study introduces REPROPREP, a methodological framework designed to enable systematic validation of preprocessing effectiveness assumptions.Design/methodology/approach The REPROPREP framework incorporates conservative statistical analysis with Benjamini-Hochberg false discovery rate correction, systematic quality degradation protocols, and cost-effectiveness assessment. The framework was evaluated across 10 UCI repository datasets using gradient boosting classifiers with 5-fold stratified cross-validation under three simulated quality conditions, resulting in 90 statistical comparisons.Findings The analysis found no statistically significant differences between preprocessing strategies after multiple comparisons correction, with negligible effect sizes (mean AUC difference: 0.001). Cost analysis indicated implementation cost differences ranging from $150 to $800 across strategies.Practical implications The framework provides a systematic methodology for preprocessing evaluation, enabling organizations to conduct context-specific validation of preprocessing effectiveness assumptions and support more informed decision-making regarding preprocessing strategies.Originality/value REPROPREP introduces a reproducible and systematic approach to preprocessing validation that integrates statistical rigor with cost-benefit analysis. The framework is designed for adaptive development, with this initial release focusing on numeric preprocessing and providing a foundation for future extensions.