Large-Scale Data Quality Improvement

RaulWalter developed a methodology that combined automated analytics, record linkage, and manual verification.

One of our most extensive data-cleansing projects focused on improving the quality of datasets held by a national financial institution. In those hundreds of thousands of records, there were errors, inconsistencies, or missing attributes. The datasets originated from multiple sources and could not be reliably linked, as no trustworthy identifiers were available and the data formats had evolved inconsistently over many years.

RaulWalter developed a methodology that combined automated analytics, record linkage, and manual verification. The datasets were enriched using multiple independent registries, address information, utility data, and geographical attributes to establish reliable connections and eliminate duplicates and errors. As a result, data quality improved significantly, creating a foundation for functional data exchange, more accurate taxation processes, and better service delivery.

This project illustrates how a systematic workflow and analytical precision can bring order to even highly complex and historically fragmented datasets.

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