This is the Electe Review. Today, why the sequence of data pre-processing matters more than the individual cleaning techniques themselves. Most guides treat data preparation as a checklist of interchangeable chores you can run in any order. That assumption quietly corrupts business forecasts. When an average order value suddenly jumps on a finance lead's desk without a single catalog change, the culprit is rarely customer behavior. It is almost always a pre-processing step executed out of turn. Consider a retail export of 12,000 order lines containing 900 duplicate invoice rows. If an analyst imputes missing prices before removing those exact duplicates, every duplicated invoice gets counted twice and pulls the calculated average away from reality. The sequence itself inflates the result. The critical boundary in the seven-step workflow is the data split. Profiling and cleaning come first, verifying data types, logging null rates, stripping customer personal data, normalizing date formats, standardizing labels like United Kingdom, and removing exact duplicates matched on invoice number, date, and amount. Immediately after cleaning, the dataset must be split, typically 80% for training and 20% for validation. Independent records should be shuffled first, while forecasting data must remain in strict chronological order so the model never peaks at the future. Every step that learns a statistic from the data must happen after that fence. Missing value imputation, category encoding, feature engineering, outlier capping, and numeric scaling all belong strictly on the training set. Whether you drop rows when blanks sit under 5%, estimate gaps using regression, or replace skewed order value means with medians, those calculations cannot touch validation rows. The same rule applies when grouping rare categories, dropping features that correlate above 0.95, verifying outliers with the 1.5 inner quartile range rule, or choosing robust scalar over standard scalar to handle heavy bulk order extremes. Finally, locking those fitted transformations into an automated pipeline prevents dashboards from drifting between uploads. Even when platforms like Elect T save teams over 10 hours a week by automating financial analysis, human review remains essential to distinguish a data entry typo from a real 40 unit wholesale order. That's the review.