Our methodology combines source-level analysis, schema normalisation, record linkage, duplicate resolution, attribute- and identifier-based conformity checks, and both automated and manual verification.
We have successfully improved tens of millions of records in various registries, including in environments where shared identifiers either do not exist or are of poor quality. The result is clean, consistent, machine-readable data that enables registries to interoperate, services to function correctly, and organisations to make more accurate and informed decisions.
We begin with systematic source-level analysis to understand the structure, content, and failure modes of the data. This includes profiling schemas, value distributions, null patterns, inconsistencies, and systemic errors. The outcome is a factual baseline that defines what can be fixed, how, and with what level of confidence.
We normalise and align data structures to create a consistent, machine-readable foundation. This includes resolving schema drift, harmonising field definitions, standardising formats, and aligning data types across sources. Where necessary, we redesign logical models to support interoperability without forcing unrealistic upstream changes.
We apply deterministic and probabilistic matching techniques to identify related records across datasets — even in environments without reliable shared identifiers. This includes attribute-based matching, contextual correlation, and rule-based resolution strategies. Duplicates are resolved in a controlled and auditable manner, preserving traceability and decision logic.
We perform deep validation of attributes and identifiers against defined rules, reference datasets, and external constraints. This includes format checks, logical consistency validation, checksum and range controls, and cross-field dependency checks. Where identifiers are missing or unreliable, we support the creation or reconstruction of stable internal keys.
We combine automated correction with targeted manual verification where risk or ambiguity requires human judgement. Corrections are applied using controlled workflows that preserve evidence, rollback capability, and auditability. The result is measurably improved data quality without introducing uncontrolled or opaque changes.
