Access The Guide Today
The Data Quality Paradox
Every day, more than 1,000 data quality incidents are detected and resolved in the Monte Carlo Data + AI Observability platform.
Across hundreds of organizations and millions of tables, thousands of data engineers, analysts, and AI practitioners rely on Monte Carlo to monitor, detect, and resolve reliability issues before they impact the business.
This scale gives us a unique vantage point into how modern teams approach reliability, the most common causes of bad data, and what separates top-performing teams from the rest. Adopting data quality best practices isn’t just about cleaner pipelines, it’s about trust. Reliable data drives accurate dashboards, dependable AI models, and confident decisions. Unreliable data erodes that trust just as quickly.
This report aims to uncover:
- How data quality varies across modern tech stacks
- The most common issues degrading reliability
- How top-performing data teams are closing the trust gap
