Data Quality Imperatives for Data Migration Initiatives: A Guide for Data Practicioners

Gerhard Längst (), Jürgen Elsner () and Anastasia Berzhanin ()
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Gerhard Längst: Capco, Postal: Opernplatz 14, 60313 Frankfurt am Main, Germany, https://www.capco.com/
Jürgen Elsner: Capco, Postal: Opernplatz 14, 60313 Frankfurt am Main, Germany, https://www.capco.com/
Anastasia Berzhanin: Capco, Postal: Opernplatz 14, 60313 Frankfurt am Main, Germany, https://www.capco.com/

Journal of Financial Transformation, 2019, vol. 50, 118-125

Abstract: This article is based on the experiences gained through a large data migration and business process outsourcing project in 2019. Examining static data linked to approximately 12 million customer records spread across over 10 source systems led to the early identifi cation of unclean data in approximately 10% of the golden source data and resulted in large-scale data remediation efforts that were necessary prior to data migration. Key takeaways and lessons learned about data quality on a fi nancial institution’s customer data are summarized here for the data practitioner, with an emphasis on applicable methods and techniques to gain transparency about an institution’s overall current state of data.

Keywords: Data Migration; Data Analytics; Data Quality; Data Profiling; Data Non-Quality Costs; Large Data Sets: Modeling and Analysis (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:ris:jofitr:1631

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