Datagaps‘ AI Copilot revolutionizes quality assurance (QA) testing with simplified SQL query generation and code maintenance. Integrated with OpenAI, the platform enhances productivity, ensures compliance, and automates data quality assurance and ETL testing.
What’s inside
- SQL Query Generation: Quickly create optimized SQL queries.
- Code Maintenance: Standardize and improve code quality across languages.
- Integration and Automation: Seamlessly integrate with existing tools and workflows.
- Compliance and Privacy: Adhere to compliance standards and privacy regulations.
- Future Innovations: Leverage upcoming features to streamline QA processes further.
Customer Profile
A large court system modernizing its data infrastructure and migrating critical judicial data to a new platform while ensuring accuracy, compliance, and uninterrupted operations.
Business Challenges
- Validating large-scale migration of court and case management data.
- Ensuring data accuracy and integrity across complex datasets.
- Reducing manual validation effort and migration risks.
How We Solved It
- Established seamless integration with CRMs and other data sources.
- Executed large-scale data reconciliation and quality checks.
- Enabled continuous validation through reusable automated test assets.
Outcomes Delivered
- 60% reduction in overall deployment time
- 70% faster validation of new datasets
- 95% improvement in overall data quality
Customer Profile
A healthcare data organization managing APCD submissions across multiple U.S. states, focused on ensuring data quality, compliance, and reporting accuracy.
Business Challenges
- Managing complex state-specific data quality rules and reporting requirements.
- Validating large volumes of claims, provider, and member eligibility data.
- Reducing manual review efforts while ensuring data accuracy and compliance.
How We Solved It
- Automated APCD data quality validation using configurable rule-based templates.
- Implemented threshold-based monitoring and exception management workflows.
- Enabled seamless integration with provider systems and automated reporting pipelines.
Outcomes Delivered
- 150+ data quality rules automated per file
- Integrated with 35 healthcare data providers
- Supported APCD submissions for 18 states
Customer Profile
A leading asset management firm managing high-volume trading and securities data across Snowflake, Azure, and Power BI environments. The company sought to establish end-to-end data assurance across its data pipeline.
Business Challenges
- Validating high-volume third-party and asset data.
- Managing frequent ETL and transformation changes.
- Ensuring reporting accuracy while reducing manual validation efforts.
How We Solved It
- Automated data quality, ETL, and file validation processes.
- Integrated validation across Snowflake, Azure Data Factory, and Power BI.
- Enabled continuous monitoring with automated test execution and alerts.
Outcomes Delivered
- 100% Validation coverage across transformations
- 100% quality and reporting assurance
- 0% pipeline disruption
Customer Profile
A marketing analytics organization processing large volumes of advertising and commerce data from multiple sources to support campaign optimization and business reporting.
Business Challenges
- Ensuring data quality across multiple vendors, BI systems, and transformed datasets.
- Managing large-scale data transfers from diverse data sources.
- Reducing manual validation efforts and monitoring complex data pipelines.
How We Solved It
- Automated data validation and anomaly detection using DataOps Suite.
- Implemented continuous monitoring, reporting, and alerting for data quality issues.
- Centralized validation workflows using reusable rules and templates.
Outcomes Delivered
- 99.9% improvement in data model quality
- 75% reduction in data assurance effort
- 50–70% faster issue resolution
A higher education customer integrated Datagaps DataOps Suite with Collibra to automate and enhance data governance and quality management. The institution achieved higher data accuracy and compliance by applying data quality rules to SIS datasets and reporting scores back to Collibra.
What’s inside
- Seamless Integration: Use Collibra’s REST APIs for smooth integration with Datagaps DataOps Suite.
- Automation: Reduce manual effort with automated data quality rule application and scoring.
- Enhanced Reporting: Gain comprehensive insights into data health with detailed reporting.
- Real-Time Monitoring: Ensure data remains accurate and reliable with real-time monitoring.
- Scalability: Handle large volumes of data and multiple sources effectively.
Customer Profile
A leading global research university migrating its data warehouse to Snowflake while ensuring accurate data across SIS and CRM systems.
Business Challenges
- Ensuring consistency between legacy and new ETL systems during migration.
- Addressing recurring data quality issues, transformation errors, and reporting inaccuracies.
- Reducing the time and effort required for manual data validation.
How We Solved It
- Automated ETL validation using 1,500+ generated test cases.
- Enabled report-to-query comparisons and visual validation across releases.
- Implemented continuous data quality monitoring and cross-system reconciliation.
Outcomes Delivered
- 66% reduction in data validation resources
- 100% data validation coverage during Snowflake migration
- $580K cost savings through validation automation
Customer Profile
A leading pharmaceutical company with over 11,000 Tableau users and multiple production environments, seeking to automate report validation and ensure reporting accuracy.
Business Challenges
- Extensive manual testing of Tableau reports.
- Frequent report updates across multiple environments.
- Maintaining report accuracy, performance, and user trust.
How We Solved It
- Automated Tableau functional and regression testing.
- Implemented performance validation across environments.
- Reduced manual QA effort through continuous test automation.
Outcomes Delivered
- 25% reduction in functional testing effort
- 20–30% lower QA-related costs
- 20% reduction in total cost of ownership (TCO)
Customer Profile
A leading U.S. cereal manufacturer undertaking a large-scale Oracle-to-Snowflake migration. The company sought to automate ETL validation while enhancing scalability and data reliability.
Business Challenges
- Validating complex Oracle-to-Snowflake data migrations.
- Scaling ETL testing across large enterprise datasets.
- Improving data quality while reducing manual testing effort.
How We Solved It
- Achieved 100% migration test coverage with million-record validation.
- Accelerated validation through parallel execution of reusable test plans.
- Enabled continuous data quality monitoring and automated test execution.
Outcomes Delivered
- Up to 60% reduction in migration testing time.
- 70% cost savings in QA through automated validation.
- 95% reduction in data quality testing effort.
Customer Profile
A leading French personal care company modernizing its data ecosystem and seeking to automate ETL validation during large-scale data migration initiatives.
Business Challenges
- Managing complex ETL validation and migration processes.
- Addressing data quality issues and performance bottlenecks.
- Reducing manual testing effort while ensuring data accuracy.
How We Solved It
- Managed reusable dataflows and pipelines for ETL and quality validation.
- Leveraged scalable processing to handle large data volumes.
- Implemented continuous data quality and integrity checks.
Outcomes Delivered
- 45–60% reduction in migration testing time
- 30% reduction in total cost of ownership
- 100% automated testing coverage
