Every analytical breakthrough begins before modeling or visualization—with data quality. This month’s Quant edition examines data cleansing as the invisible architecture behind trustworthy analytics, scalable AI, and credible decision-making. Data cleaning—the process of identifying and correcting errors, inconsistencies, and missing values—shapes how information is used in modeling and strategy. Clean data reduces mistakes, improves efficiency, strengthens compliance, and builds confidence in insights. Across industry guides, enterprise frameworks, and technical deep dives, one truth stands out: even the most sophisticated model cannot compensate for flawed inputs. From governance structures to machine-learning-powered automation, this edition explores why data cleansing matters, how it is implemented, where it fails, and how leading organizations institutionalize it as a long-term strategic discipline.