discovery.data_audit

Data readiness assessment catalogs your available datasets, identifies instrumentation gaps, and evaluates quality to ensure reliable data exists before development begins. This Skill automates that inventory and gap analysis, producing a data catalog with sources, owners, freshness, and accessibility alongside a gaps report with recommendations.

A data audit inventories datasets, documents owners and freshness, flags ETL or instrumentation gaps, assesses data quality considerations, and produces a structured decision-support plan with implementation steps, owners, and sequencing for upcoming analytics decisions.

Yes. Gap analysis within a data audit identifies instrumentation or ETL gaps that prevent you from supporting specific decisions. The Skill recommends changes and sequences implementation steps so you can close gaps before analytics work begins.

Data cataloging documents all available datasets with their sources, owners, freshness, and accessibility status. This Skill automates inventory across product domains, supporting analytics discovery for any decision timeline or use case.

A data quality assessment produces a data catalog with trust and reliability metrics, a gaps and recommendations report highlighting instrumentation shortfalls, and a decision-support plan with interim proxies and implementation owners.

Run a data audit early in the planning phase for any new analytics initiative. It prevents downstream issues by confirming data availability, identifying gaps upfront, and establishing baseline quality before development begins.