Data Quality at Scale Begins with Data Observability
February 28, 2025
Data is valuable—bad data is expensive. As organizations rely more on data to fuel decisions and power large language models, the cost of poor data quality has soared. Missing, inaccurate, or unreliable data can cause critical disruptions, erode trust, and lead to nonstop firefighting for data teams.
In fact, data downtime—when data is unusable—nearly doubled from 2022 to 2023, driven by a 166% increase in time to resolve data quality issues. That’s where Monte Carlo comes in. Integrated directly with your AWS environment, Monte Carlo pairs automated anomaly detection with AI-driven data quality rules and rapid root-cause analysis to help you solve issues fast, prevent future incidents, and build lasting trust in your data.
In this guide you’ll learn how to:
- Maximize Data Reliability: Discover strategies for preventing and resolving quality issues at scale.
- Reduce Downtime by Up to 88%: Learn how AI-powered observability can keep critical operations running smoothly.
- Accelerate Innovation: Free up your team to focus on dashboards, AI initiatives, and business growth.
Ready to future-proof your data landscape? Learn how Monte Carlo on AWS helps you safeguard data reliability, reduce data downtime, and drive your AI initiative with data you can trust.