digna monitors data quality without moving the data: it calculates metrics in-database, learns normal baselines, tracks arrival schedules, flags schema changes and validates records against rules you define, all from one interface. Named modules cover anomaly detection, historical analytics, timeliness monitoring, record-level validation and schema tracking. It is built by a Vienna-based team, deployable on private cloud or on-premise, with a stated installation-to-insights time under two hours. Release 2026.06 shipped in August 2026.
In-database execution is the design decision that matters most for anyone handling regulated or sensitive data: the monitoring runs where the data already lives rather than requiring a copy to be exported to a third-party service, which sidesteps an entire category of risk that most SaaS data-quality tools accept by default. Automated baseline learning without manual rule-writing for anomaly detection specifically is also the right default – hand-written rules for what counts as anomalous data drift out of date as fast as the data itself changes.
A genuinely new release as of August 2026 means a shorter track record than established data-observability competitors, and private cloud or on-premise deployment, while good for data control, is real infrastructure work to stand up compared to a hosted SaaS tool. No pricing figures are published; the two-hour installation claim is worth testing against your own schema complexity rather than taking as a universal figure.
