Data Quality as a Key Success Factor for Migration Projects
Résumé
Organizations are facing the challenges of data migration, which results either from mergers and acquisitions due to the consolidation of the economy, or from upgrade of systems or finally from willingness to simplify the data storage architecture. A data migration is not a trivial task; either, it is a significant project undertaking. Because the data stored in the legacy systems is a strategic company asset, it needs to be analysed, measured, preserved and improved before being brought over to the target system. In this paper, we present business cases and best practices on how companies structure their data quality management in the particular context of data migration. We propose a comparison grid with classified items such as the project management teams, the data quality audit and the key quality indicators (KQIs), the data quality improvement activities, the optimum quality level calculation, the problem of the data references and the need for a data governance post migration.