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Big Data and Cognitive Computing
Article . 2021 . Peer-reviewed
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Big Data and Cognitive Computing
Article
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Big Data and Cognitive Computing
Article . 2021
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Without Data Quality, There Is No Data Migration

Authors: Otmane Azeroual; Meena Jha;

Without Data Quality, There Is No Data Migration

Abstract

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’s 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.

Keywords

Technology, data migration, T, cleansing, data quality, business enterprise success, dependency, structural equation models (SEM)

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    15
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Top 10%
Top 10%
Top 10%
gold