Enterprises are experiencing rising data complexities as they scale cloud adoption, modernize data estates, and operationalize AI. In this landscape, undetected data issues, ranging from quality degradation and schema drift to pipeline failures and governance lapses, can create costly downstream impacts. As a result, data observability has become essential to ensure resilient, trustworthy, and AI-ready data ecosystems.
This shift marks a move from reactive data quality checks to proactive, automated monitoring across the data life cycle. Modern data observability platforms use ML-driven anomaly detection, automated lineage mapping, and root-cause analysis to deliver real-time visibility into data freshness, volume, schema, performance, and reliability. By identifying issues early and reducing operational firefighting, these platforms help enterprises improve decision-making, optimize costs, and maintain consistent data for AI and analytics workloads.
Enterprises are increasingly seeking providers that can embed observability into broader data and AI operations. In response, technology providers are expanding integrations with cloud data platforms, lakehouses, and MLOps tools, while investing in extensible architectures and accelerators for rapid deployment.
In this report, Everest Group evaluates 19 leading data observability technology providers featured in the Data Observability Technology Providers PEAK Matrix® 2025. The report presents concise provider profiles detailing capabilities, case studies, platform features, key developments, and integrations.
