As organizations scale AI, analytics, and automation, the risk of bad data grows. Small upstream issues can silently move into dashboards, forecasts, and use cases like AI. Gartner notes that 93% of teams fail to detect data issues before they impact the business, and at least 30% of AI projects are abandoned due to poor data quality and inadequate controls.

Data Observability Demystified: From Basics to Advanced explains how modern observability moves beyond reactive monitoring to proactive issue detection and mitigation. It explains core capabilities such as data monitoring, automated anomaly detection, lineage, and SLA management, then introduces Data Observability Agents and MCP-enabled connectivity to support AI assistants and agentic workflows. The result is trusted, validated data that supports autonomous decisions and AI without sacrificing governance.

Dashboards don’t fail first. Data does. When bad data slips through pipelines, it doesn’t just break reports. It breaks confidence in AI, analytics, and decision-making. This eBook shows how modern data observability solutions and observability agents detect issues early, explain root causes clearly, and ensure trusted data for AI, decision making, and other use cases.

Your AI is only as reliable as your data. Download the eBook to use data observability for proactive issue mitigation and to ensure reliable data.