Modern AI and data platforms now underpin critical business decisions and customer experiences for billions of users. However, many organizations face a growing but largely invisible risk: data debt. Unlike system outages or broken pipelines, data debt accumulates quietly data flows appear healthy, dashboards continue to update, yet data quality steadily erodes. The result is degraded model performance, declining customer outcomes, missed KPIs, and AI and analytics investments that fail to deliver expected returns. Drawing on my experience building and operating large-scale data and machine learning platforms at LinkedIn, Meta, and Uber, this talk explains how data debt emerges, why it is so difficult to detect, and how it silently propagates across teams and systems. Through concrete, real-world examples, I will show how small, undetected data quality issues can cascade into significant business impact—and why traditional monitoring and governance approaches often fall short. Designed for VPs, Directors, and CTOs at SMBs, this session provides a practical lens on the true cost of data debt. Attendees will gain a clearer understanding of how data debt undermines customer trust, slows innovation, and stalls AI and analytics strategies and learn actionable approaches to building more resilient, trustworthy data ecosystems & a data aware culture that scale with the business.