data-observability-audit

You can identify data observability gaps by scanning dbt manifest artefacts and data-stack-context files. The audit maps missing monitoring, freshness checks, lineage tracking, and tests, then generates a prioritized remediation plan to resolve pipeline vulnerabilities.

A data observability maturity assessment evaluates your pipeline coverage across monitoring, alerting, freshness, lineage, and tests. It categorizes observability maturity from reactive to proactive, generating actionable playbooks and checklists to systematically raise data quality standards.

Yes, the audit leverages dbt manifest artefacts and .claude/data-stack-context files to tailor recommendations for your modern data stack. These inputs allow the workflow to accurately map lineage, tests, and monitoring gaps within your specific analytics warehouse environment.

To create a prioritized remediation plan, execute the observability audit workflow on your data platform. It evaluates test, freshness, and alerting coverage, then outputs a prioritized action plan and playbooks to accelerate incident recovery and reduce data pipeline issues.

The audit is optimized for analytics warehouses and modern data stacks leveraging dbt projects. While it specifically parses dbt manifest artefacts and data-stack-context to assess lineage and tests, its remediation principles can inform broader observability strategies across similar data platforms.