Overcoming Data Chaos: How Machine Learning Compensates for Inaccurate Utility Records

This case study demonstrates how the Rivixi Decision Fusion pipeline processes chaotic, real-world utility maintenance logs and GIS records to achieve highly accurate pipeline failure predictions. We present five advanced data engineering techniques—Spatial Aggregation, Asset Scale Normalization, Missing Value Encoding, Target Encoding, and Dynamic Age Calculation—that extract predictive signals from unstructured and incomplete data rather than requiring manual data sanitization. Applied to a pilot municipal water network covering 1,200 miles, the pipeline reduced orphaned dispatcher records from 68% to 7% and served as the critical foundation for a downstream boosting-blending ensemble model achieving a Recall rate of 93.88% on balanced evaluation subsets when detecting pre-failure zones. The proposed approach eliminates the need for utilities to meet strict AWWA/ASME data standards prior to AI deployment, enabling rapid model deployment directly on raw database dumps.