(A) Building good data processing systems from an end-to-end perspective is as important as it is complex. While explainable AI (XAI) approaches help users to understand the behavior of trained models, choices in preprocessing and their effects are not yet part of explanations. We have recently proposed PIPE-X (Preprocessing Impact and Pipeline Explanations) to explain data preprocessing pipelines by calculating the impact of each step on the model’s behavior. In this paper, we demonstrate how data scientists can leverage PIPE-X through an interactive graphical interface to gain insights into their pipeline and improve the endto-end system accordingly. Users can provide their data, pipeline, and model to obtain the impacts for individual model outputs or an overview of the effect throughout the model. They can leverage various interactive outputs (graphical and numerical) to gain the best result for their specific use case. Encode Discretize 0 0 0 Impute 0.01 0.7 -0.2 Scale bias unused (B) Encode Scale Discretize 0.9 Sample 0 0.1 Figure 1: (A) Preprocessing pipeline with impacts ■ for each step and selected interaction indicators ■. Discretizer impact is high, imputer has no impact, scaler has little impact and sampler has negative impact. Scaler and Sampler have high interaction. (B) Redesigned pipeline after leveraging preprocessing explanations. Imputer is removed (no missing values, therefore no impact). Sampler is removed (class balancing introduces unintended shift). Scaler is retained despite low impact to benefit training time.