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.
Highlights
Companies today often use Information Technology (IT) systems that are old and have been specially developed for the company
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
The results of the dependency between data quality and data migration have shown that data quality is relevant in data migration and that counteracting this is an essential prerequisite for success
Summary
Companies today often use IT systems that are old and have been specially developed for the company These systems are called legacy systems [1] and have high operating costs or the employees lack the know-how for maintenance work, since the system is based on old programming languages and mainframes, and the documentation has been lost [2]. Data migration is not just a process of moving data from an old data structure or database to a new one; it is a process of correcting errors and improving overall data quality and functionality. The research in this paper provides new insights into the issue of data quality in relation to data migration.
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