Data Quality is embedded into the governance framework — not bolted on as a separate tool. Quality rules travel with your data so agents consume only attested, trustworthy information.
Without governed data quality, these problems compound silently with every new data source and AI deployment.
Schema drift, stale records, and inconsistent formats accumulate across hundreds of sources without detection — discovered only when a report is challenged.
Quality rules don't reflect regulatory requirements. Governance policies can't verify whether data meets the standards they define.
Agents consume data that passes basic validation but carries hidden issues — producing confident but unreliable decisions with no visible failure signal.
Automated profiling of every connected dataset — completeness, accuracy, freshness, consistency
Governance-authored quality rules — aligned to regulatory and business requirements
Rules evaluated at data access time — quality gates block bad data before agents consume it
Quality score and evidence attached to every dataset — audit trail for every validation run
Quality failures surfaced with context for stewards to investigate and resolve
Value distributions, completeness rates, uniqueness, pattern detection, and outliers — at a glance, before any data reaches an agent or report.
Quality rules defined by compliance and risk teams — not engineering. Rules that reflect regulatory requirements, not just technical constraints.
Data that fails quality thresholds is blocked before reaching agents or reports — not discovered downstream after decisions have been made.
Every dataset carries a quality attestation — what was checked, what passed, what failed, and when — regulator-ready without additional documentation.
See how Data Quality works within the Tantor governed intelligence platform.