{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T15:28:32Z","timestamp":1778945312637,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today\u2019s dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible.<\/jats:p>","DOI":"10.3390\/bdcc5020024","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T06:01:33Z","timestamp":1621317693000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Without Data Quality, There Is No Data Migration"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-389X","authenticated-orcid":false,"given":"Otmane","family":"Azeroual","sequence":"first","affiliation":[{"name":"German Center for Higher Education Research and Science Studies (DZHW), 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meena","family":"Jha","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Systems, School of Engineering and Technology, College of Information and Communication Technology (ICT), Central Queensland University, Sydney, NSW 2000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jha, S., Jha, M., O\u2019Brien, L., and Wells, M. 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