Decisions are only as good as the quality of data behind them.
Unstructured formats, inconsistent data transformation and poor validation practices can quietly erode the effectiveness of even the most advanced models. Our Data Quality practice embeds assurance as a strategic layer across modernization, migration, and analytics. By safeguarding the integrity of data feeding models, reports, and workflows, we ensure that what systems learn, predict, and recommend is grounded in reality.
Data Quality Services
Data Migration Testing
ETL & Pipeline Validation
Data Fabric Testing
Data Visualization Testing
Data Migration Testing
Avoiding loss or duplication of data during platform migrations. Conduct end-to-end reconciliation between source and target, validate record counts and hash totals, and ensure mapping rules are enforced.
ETL & Pipeline Validation
Ensure that every transformation is accurate and traceable. Validate field-level transformations and business logic, detect duplicates or null propagation, and benchmark projects against SLAs for freshness and throughput.
Data Fabric Testing
Aligning outputs across distributed platforms and data sources. Monitor consistency windows, enforce access controls, and validate retention and failover for governance.
Data Visualization Testing
Ensure that what business leaders see is based on real, validated data. Reconcile data between dashboards and source systems, validate metrics, filters, and role-based access, and conduct stress tests under concurrent loads.
Data Migration Testing
Avoiding loss or duplication of data during platform migrations. Conduct end-to-end reconciliation between source and target, validate record counts and hash totals, and ensure mapping rules are enforced.
ETL & Pipeline Validation
Ensure that every transformation is accurate and traceable. Validate field-level transformations and business logic, detect duplicates or null propagation, and benchmark projects against SLAs for freshness and throughput.
Data Fabric Testing
Aligning outputs across distributed platforms and data sources. Monitor consistency windows, enforce access controls, and validate retention and failover for governance.
Data Visualization Testing
Ensure that what business leaders see is based on real, validated data. Reconcile data between dashboards and source systems, validate metrics, filters, and role-based access, and conduct stress tests under concurrent loads.
Bring reliability to enterprise data ecosystems
A leading healthcare non-profit that was struggling with poor data clarity, complex data, and slow data consolidation adopted a shift-left, techno-functional QA approach for early validation of complex data mappings and evolving business rules, and was able to consolidate fragmented, multi-regional data environments into unified, validated pipelines.
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90%+
Reduction in UAT and production defects
95%+
Accuracy in DHIS2 metadata validation
Read the Case Study
Our Other Quality Engineering Services
Explore the entire range of our quality engineering solution offerings.
