DataOps Suite
Data Quality and Agentic AI platform that validates, reconciles, scores, monitors and remediates data from ingestion to BI and AI.
Why one platform
DataOps Data Quality
Continuously profiles, validates and scores enterprise data, with Agentic AI that recommends rules and flags anomalies, so analytics and AI run on trusted data.
Capabilities
ETL Validator
Automates ETL/ELT testing, with Agentic AI that builds tests from mapping docs, confirming every record arrives complete, accurate and correctly transformed.
Capabilities
BI Validator
Automates testing and governance for Power BI, Tableau and Oracle Analytics, with Agentic AI that builds tests from plain English and keeps reports accurate and reliable.
Capabilities
Test Data Manager
Uses Agentic AI to detect and mask sensitive data and generate synthetic data that mirrors production, so teams can test without exposing real records.
Capabilities
Data Quality Use Case
Start from the outcome your team needs, with Agentic AI doing the groundwork.
Cloud Platforms Use Case
Validate and monitor data and its quality on the platforms you already run, backed by Agentic AI.
BI and Analytics Use Case
Keep every report accurate across your BI tools, as Agentic AI builds the tests for you.
Banking, Financial Services, and Insurance (BFSI)
Stop reporting errors before examiners find them, with every number traced from core system to board dashboard.
How Datagaps helps
Healthcare
Keep claims and clinical data accurate through every migration, without exposing patient records in testing.
How Datagaps helps
Life Sciences
Catch issues in clinical and real-world data before submission, and keep trial subject data out of test environments.
How Datagaps helps
Manufacturing
Avoid broken ERP cutovers and supplier overpayments, and catch sensor drift before it hits production.
How Datagaps helps
Retail and Utilities
Catch settlement, inventory and pricing errors before your customers or regulators do.
How Datagaps helps
Higher Education & Research
Get enrollment, financial-aid and transcript data right before SIS cutover, before students feel it.
How Datagaps helps
Trusted Data Summit
A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.
Learn
Customers and tools
Support
Automating Financial and Production Data Reconciliation for a U.S. Property Insurer
A major U.S. property insurer managing 600 to 800 data interfaces and pipelines across Oracle, Oracle PeopleSoft, Guidewire, and AWS
Datagaps
Get in touch
Why teams trust Datagaps
100+ Enterprise Customers
Datagaps partners with Vega IT
Bringing trusted data foundations to digital and AI transformation.
Technology Partners
Datagaps works natively with the cloud, data and BI platforms your teams already run.
Our technology partners
SI Partners
Consulting and integration firms that deliver Datagaps validation for enterprise clients worldwide.
Our SI partners
Platform
Solutions
Use cases
Cloud platforms
BI and analytics
Industries
Trusted Data Summit
A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.
Resources
Learn
Customers and tools
Support
Company
Datagaps
Get in touch
DataOps Suite is a comprehensive data migration testing tool that automates data validation, data quality checks and data reconciliation across the full migration lifecycle.
Automate test execution to reduce manual validation time, cut testing overheads and deliver faster cutovers.
Automate validation workflows to eliminate manual effort, reduce resource dependency and lower the overall cost of migration testing.
Compare source and target datasets across environments to detect discrepancies and ensure data integrity before go-live.
Analyze source datasets to identify data quality issues prior to migration and ensure readiness before transformation or transfer.
Apply automated quality checks to detect duplicates, null values, and inconsistent records that may impact migrated data.
Validate table structures and field mappings to ensure compatibility between legacy and target systems during migration.
Compare migrated data across systems to detect discrepancies introduced during migration workflows.
Validate record counts, required fields and checksums to confirm all business-critical data has transferred successfully with no data loss.
Run automated quality checks at each migration phase to catch duplicates, null values and inconsistent records before they reach the target system.
Validate data consistency between legacy and migrated environments while both systems are live.
Continuously validate migrated data across environments during phased migration rollouts.
Validate reporting accuracy across BI platforms such as Power BI and Tableau after migration to ensure consistent insights.
Automate migration validation runs across environments to continuously test data integrity during phased rollout or post‑cutover data transfers.
Track past migration validation runs to compare outcomes across cutover and incremental migration cycles.
Trigger automated migration validation within deployment workflows to support repeated migration testing as releases progress.
Our clients receive great value from our data validation solutions
Reduce your data testing costs dramatically with ETL Validator –
Get your 14 days free trial now.
Migration projects often require reshaping data to fit the target system’s schema, business rules, or platform constraints. ETL Validator compares transformed outputs across staging and target environments to ensure mapping rules are applied correctly and business logic remains intact during migration cycles.
Migration validation often needs to be executed repeatedly across development, UAT, and production environments. ETL Validator enables teams to run parameterized migration test cases across environments to compare source target outputs consistently and detect mapping or data consistency defects early.
Running repeated mock migration cycles allows teams to validate schema mappings and transformation logic before cutover. ETL Validator supports automated migration validation pipelines so defects can be identified prior to go live instead of during production operations.
Automated validation pipelines allow reconciliation checks to be executed after each migration cycle, supporting structured migration testing strategies and enabling teams to apply go/no‑go thresholds based on validated outcomes.
Migration defects introduced during transformation or schema conversion can impact BI dashboards and business workflows. ETL Validator validates migrated datasets across environments to ensure migrated data supports downstream reporting and analytics consistently.
Schema conversions across cloud and database migrations may introduce data type mismatches or inconsistent mappings. ETL Validator compares structural metadata across systems to detect schema inconsistencies before migrated datasets are loaded.
ETL Validator enables CI/CD integration so migration validation can be triggered automatically alongside deployment pipelines, ensuring transformation logic and source‑target migrations are validated after every incremental transfer.
Migration readiness depends on repeated validation cycles and reconciliation evidence across environments. ETL Validator generates downloadable migration validation reports that can support stakeholder review before go‑live decisions are made.
Automate data warehousing, data migration and big data testing projects.
