Data Reconciliation Frameworks

We can support you in developing and implementing a data reconciliation framework that will ensure data consistency, accuracy, and integrity across all systems, pipelines, and storage environments in scope. We design and implement automated reconciliation processes that detect discrepancies, validate data correctness, and flags and resolves inconsistencies in real time.

Our automation first approach allows us to develop a framework that allows for data reconciliation tests to be applied at every stage of the pipeline, covering drift detection, data mapping and business rules validation, schema validation, row counts checks, duplicate data detection and freshness checks. We will also define error-handling strategies and secure logging mechanisms for any record discrepancies. Data quality dashboards are usually developed using a BI tool of your choice to highlight and pro-actively resolve any data migration exceptions.

How we build the data reconciliation framework is dependent on your need, infrastructure and tooling requirements. Our automation first approach ensures data reconciliation frameworks are engineered to deliver automated validation where possible, which also includes automating the logging, reporting and error handling where possible too.

Throughout the life cycle of the migration, rigorous data testing, validation, and monitoring at occurs every stage of the pipeline. Automated schema checks, data type validation, and constraint enforcement safeguard against corruption and inconsistencies, while unit, integration, and regression testing of data transformations and workflows guarantee completeness and accuracy.

To support enterprise-scale migrations, we conduct load testing, stress testing and performance benchmarking to accurately predict how the migration will run on the go-live day.