Data Testing


Data is the Lifeblood of Your Business, But is It Truly Reliable, Accessible, and Ready for the Future?

Traditional data testing often falls short, focusing solely on basic functionality and neglecting the critical areas that truly impact your organization’s success.

Introducing our revolutionary Data Testing service, designed to go beyond the ordinary and address the unique challenges of modern data environments.

Data Testing Services

Data issues cost companies millions in losses each year. Yet few validation processes specifically focus on uncovering data risks before they impact operations

Forget generic data testing that misses the mark. Our service offers a comprehensive and differentiated approach that covers three key areas:

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Database migration testing

Ensure your data migration is seamless and error-free. We meticulously test data integrity, functionality, and performance to guarantee a smooth transition to your new environment.

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Data quality testing

Uncover hidden flaws and inconsistencies in your data. We employ advanced techniques to identify and address data gaps, duplicates, and inaccuracies, ensuring your data is reliable and trustworthy.

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Data democratization testing

Empower everyone to leverage data effectively. We test the accessibility, security, and governance of your data infrastructure, enabling secure and informed decision-making across the organization.

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Synthetic data testing

Unmasking hidden biases and risks. Test performance, identify biases, and refine models ethically and securely.


Data Quality is intentional. A conscious and continuous effort to ascertain the availability of accurate, complete, and reliable data for business decisions at the right time.

Kairo’s DQGateway is AI driven, ‘No Code’ solution which addresses every aspect of data quality effectively.

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Data accuracy and cleansing

Extensive data profiling—missing values, inaccuracies, duplication, inconsistencies, outliers, security issues, privacy violations…etc, through a pre-configured profiler and/or custom rules.

CI/CD integrated and/or scheduled automated checks.

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Integrate with over 50 major data platforms and tools, ensuring a 70% faster data quality implementation process.

Integrate with all leading DevOps tools to create an effective CI/CD pipeline.

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Real-time data monitoring and reporting

Gain insights from detailed data quality reports, leading to a 60% improvement in data governance and strategy formulation.

Subscribe to events, tasks, and work units to get real-time alerts.

Did we mention it goes beyond reporting and provides insights on your data too?

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Scalable data quality

Scale data quality operations seamlessly, with no performance degradation.

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Enhanced data security & compliance

Ensure 100% compliance with industry data protection standards, reducing the risk of data breaches

DQGateway reflects our unwavering commitment to data quality. However, rest assured, these standards are integral to our Data Quality Testing services, extending beyond DQGateway.

Data Democratization Testing

Making quality data accessible and in a consumable form such that it helps in decision making.

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Objectives set for ‘Data Democratization’ define

Access Control, Ease of Use, User Experience, Insights Effectiveness.

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Role, Access level, Data Quality, Privacy, Security, Regulatory Compliance.

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User Interface Effectiveness, Ease of Use, Customizing interface / Self-serving, Facility to store / save.

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Data summarization / consolidation, dicing-slicing of data, visualization, data staleness.

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Data tool and platforms

Ease of use, performance against Governance, Usability and Insights, Data Virtualization, Data Federation, Self-Serving interface, Data Quality Enforcement.

We understand that organizations are interested in democratizing “insights” and not data per se. That is why, we go beyond fundamentals to include,

Track user adoption, analyze user activity, measure data discovery and sharing.

Conduct surveys and interviews, offer training and workshops, monitor data-driven decision-making.

Track data-driven initiatives, measure the impact of these initiatives, analyze sentiment about data use, etc.

Test data management

Artificial but realistic data sets that mimic the characteristics of actual data, organizations can overcome data limitations, ensure compliance, and accelerate their testing and development cycles. Synthetic Data not only mitigates privacy concerns but also enables comprehensive testing in controlled environments, fostering innovation and efficiency across various industries.

However, ensuring the validity of synthetic data is important, as otherwise, it will result in undesired and erroneous outcomes.

We employ elaborate measures to ensure the validity of synthetic data by checking for,

  1. Preservation of statistical properties
  2. Preservation of correlations among variables
  3. Preservation of data patterns
  4. Preserve categorical/ordinal variable representation
  5. Identification of outliers
  6. Preservation of data dependencies

Database migration testing

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  1. Objectives, Scope
  2. Data source, target
  3. Environments required
  4. Assess resource requirements
  5. Success criteria
  6. Migration approach, technique
  7. Duration
  8. Iterations
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Req. Detailing
  1. Business requirements
  2. Source data detailing
  3. Target data requirements
  4. Mappings
  5. Transformation req (if any)
  6. Co-existence period (if any)
  7. Metrics
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Design specifications
  1. Target preparation – schema, data model
  2. Tool / approach and technique implementation
  3. Develop transformation mappings (if any)
  4. Rollback / Savepoints
  5. Telemetry
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  1. Data backup
  2. Migrate data as per selected approach and technique
  3. Monitor and check the logs
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  1. Data reconciliation
  2. Monitor and check logs
  3. Data quality checks
  4. Accuracy, Completeness, Referential integrity, Transformations, Duplicates
  5. V&V against preparation and specifications
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  1. User acceptance
  2. Co-existence and incremental migration scheduling
  3. Environments maintenance and governance
  4. A&A checks
  5. Cut over plan updates and maintenance

Navigate the Complexities / Demystifying the Details

Recognizing the diversity of data challenges, we acknowledge that a universal approach is not the solution. Hence, our strategy is customized according to Migration Categories, Migration Approaches, Migration Techniques, and Sampling Techniques. Each of these aspects plays a crucial role in shaping our Data Testing methodologies.

Ancillary requirements

Our Data Testing methodology transcends basic checks, incorporating considerations like migration type (on-premise to cloud, native cloud, native SaaS, on-premise SaaS), data access control policies, regulatory compliance, external data source connections, data retention policies, etc. Our holistic approach, established practices, and robust metrics address these dimensions to deliver comprehensive testing, ensuring the completeness of your data evaluation.

Go beyond traditional data testing with our advanced solutions. With expertise in database migrations, data quality, democratization, and synthetic data, we tailor our approach to your unique needs. Trust our established practices and robust metrics to deliver complete and insightful data evaluations. Partner with us for a data-driven future.