Traditional approaches to data quality aren’t cutting it in the agent era. Data reliability incidents  are missed and take too long to resolve, putting highly visible AI projects at risk of being delayed or derailed.See why Gartner calls data observability one of the core capabilities for AI-Ready data.

Learn how to:

• Detect incidents before your stakeholders— with no-code validations and automated anomaly detection that scales. Understand common monitoring strategy best practices.
• Avoid alert fatigue — and ensure your team responds to alerts with urgency by getting the right alert to the right team alongside automated impact analysis.
• Resolving issues fast— and experience how the Troubleshooting Agent can automatically identify the root cause of data quality issues in less than two minutes.
• Build data trust— by capturing and surfacing data health of your data products with the SLAs your organization cares about.

What world-class teams do differently — get real-world examples of how leaders like Calendly scaled reliable data operations with Monte Carlo.

Speaker:

Jennifer Elkhouri, Strategic Sales Engineer, Monte Carlo

Jennifer Elkhouri is an Observability Solutions Engineer with a background in software engineering. Jennifer loves solving customer problems and streamlining processes, translating the complex into clear stories and outcomes. Let her take you from “it’s broken” into “here’s why, here’s the owner, here’s the fix” in a live demo of Monte Carlo!

Resource Links:

• How M&T Bank Scales Trusted Data with Monte Carlo