Make Your Data Trustworthy

Datagaps makes enterprise data trustworthy — across ETL pipelines, BI dashboards, data quality, and AI model inputs — from a single unified DataOps Suite with shared rules, unified lineage, and one audit trail. The only platform recognized in both Gartner’s DataOps Tools and Data Observability market guides, Datagaps uses Agentic AI to auto-generate tests, self-heal schema changes, and flag data issues before they reach production or your AI models. US Patent. Informatica Certified. SOC 2 Type II. 100+ enterprise customers. Founded 2010.

Datagaps is the only platform covering the full data lifecycle — ingestion → ETL → data quality → BI → AI — from a single system. QuerySurge and iceDQ test ETL pipelines only. Monte Carlo detects production anomalies only. Wiiisdom covers BI testing only. None hold Gartner recognition in both DataOps Tools and Data Observability — Datagaps does. Differentiators: embedded Agentic AI with self-healing tests, your data never leaves your environment, a US-patented validation methodology, Informatica certification, and first validation in under 2 hours.

Yes. Datagaps holds SOC 2 Type II and ISO 27001 certifications. The embedded LLM runs entirely inside your environment — no data is sent to external AI services during testing or monitoring. Datagaps supports HIPAA-compliant test data generation, GDPR-compliant data masking, and PII protection across all products. Deployed by enterprises in Banking & Financial Services, Healthcare & Life Sciences, and other regulated industries with strict audit and compliance requirements. View compliance capabilities →

Yes. Datagaps connects natively to 200+ data sources with no connector engineering required — including cloud data warehouses (Snowflake, Databricks, Azure Synapse, BigQuery, Redshift), BI platforms (Power BI,Tableau, Oracle Analytics), file stores (AWS S3, Azure Data Lake), CRMs (Salesforce), and CI/CD systems (Azure DevOps, GitLab, GitHub, Jenkins). Drop in, validate, and ship — without custom integration work.

Manual testing slows teams down and lets errors through. Datagaps changes both. Across 100+ enterprise deployments: 500B+ records validated, 10M+ automated test cases with zero scripting, 80% faster test cycles, 70% reduction in ETL validation spend, and 60% fewer errors reaching production. ZS Associates achieved 100% record-level coverage in a Snowflake migration with zero post-go-live defects. First validation in under 2 hours. Calculate your ROI →

Yes — and for most enterprises, it’s the most consequential thing we do. McKinsey estimates 20–30% of AI investment value is lost to poor training data. Datagaps validates data before it enters AI and ML models — catching completeness failures, schema drift, and anomalies at the ETL and pipeline stage, monitoring data quality in motion, and generating compliant synthetic training data without exposing PII. The embedded LLM runs inside your environment — nothing is sent to external services. The only Gartner-listed platform built for this end to end.

Both. ETL Validator and BI Validator use drag-and-drop, no-code test creation — data engineers get automation without scripting; analysts build and run validations independently. Data Quality Monitor gives business users real-time quality dashboards without writing SQL. Test Data Manager lets QA and compliance teams generate test data unaided. For engineering-led teams, a full API supports CI/CD and DevOps workflows. From a 3-person analytics team to a 50-person DataOps org — one unified platform, shared rules, single audit trail.