Ataccama, the AI-powered data trust company, is introducing Data Quality Gates, an extension of its data quality suite that validates data in motion across the modern data stack.
According to the company, the new capability applies rules in real time, intercepting unfit inputs before they distort analytics, compromise compliance reporting, or degrade AI models.
Ataccama Data Quality Gates prevent issues upstream, the company said. By running checks in real time as data flows through pipelines, the solution prevents invalid records before they contaminate downstream systems.
Instead of surfacing only after they hit dashboards or reports, flawed inputs such as incomplete transaction codes or restricted country data are intercepted immediately. This “shift-left” approach reduces remediation costs, lowers compliance risk, and ensures AI and analytics are powered by trusted inputs, according to the vendor.
By enforcing business rules in motion, Data Quality Gates bridge the gap between business teams and engineering pipelines, ensuring standards are applied consistently and automatically.
“Many of the biggest failures in AI and compliance can be traced back to bad data flowing unchecked into critical systems,” said Jessica Smith, VP of data quality at Ataccama. “With Data Quality Gates, part of our unified data trust platform, we’re changing that model. Outdated or unfit data never gets through, which means enterprises can protect trust at its most vulnerable point and know that their most important decisions are powered by data they can trust.”
Key differentiators include:
- Validation in motion. Rules run natively in Snowflake, dbt, and Python environments, flagging flawed records without moving data or adding latency. For example, transactions missing currency codes can be intercepted before they hit trading systems, avoiding reconciliation costs.
- Business rules where data flows. Finance, compliance, and business standards are enforced automatically in pipelines. A payroll file with invalid tax IDs or an onboarding record from a restricted country can be blocked before reaching reporting or audit systems.
- One governed rules library. Updates cascade automatically across all pipelines, eliminating duplication and version drift. For instance, when a capital adequacy reporting threshold changes, the new rule applies instantly across every pipeline, cutting compliance risk and audit cost.
- Bridge business and engineering. Governance teams define the rules once, and engineers embed them directly into dbt or orchestration jobs without rewrites. This shortens development cycles for new data products and eliminates bottlenecks between business and technical teams.
- Cross-platform trust. Standards apply consistently across environments, so checks like customer eligibility or risk ratings are enforced the same way everywhere data flows.
With Data Quality Gates, Ataccama makes data quality the engine of trust for modern AI and analytics. By unifying checks on data at rest and in motion, enterprises can scale confidently, knowing their most critical decisions are powered by data that is accurate, reliable, and fit for purpose, the company said.
For more information about this news, visit www.ataccama.com.