Data Science and Analytics for Decision Support (SIG DSA)
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Paper Type
ERF
Paper Number
1661
Description
With the new technological developments, we are in the era of "big data", where organizations have more and complex data than before. However, "big data" doesn't automatically mean that it is useful. Not having high-quality data not only disables organizations from creating knowledge but also harms the organizations as it leads to wrong strategic decisions and wrongdoings in operations. The cost of dirty data is not only money and time but also customer dissatisfaction, loss of trust, and employee dissatisfaction. In this study, by utilizing the Technology–Organization– Environment Framework (TOE), we have identified the factors affecting data quality and proposed a model for achieving high-quality data. One of the contributions of this study is the identification of factors affecting data quality from different perspectives. Such understanding can bring new insights to the discussion and provide contributions to both practitioners and researchers alike.
Recommended Citation
AKGUL, Mehmet, "Data Quality: Success Factors" (2021). AMCIS 2021 Proceedings. 16.
https://aisel.aisnet.org/amcis2021/data_science_decision_support/data_science_decision_support/16
Data Quality: Success Factors
With the new technological developments, we are in the era of "big data", where organizations have more and complex data than before. However, "big data" doesn't automatically mean that it is useful. Not having high-quality data not only disables organizations from creating knowledge but also harms the organizations as it leads to wrong strategic decisions and wrongdoings in operations. The cost of dirty data is not only money and time but also customer dissatisfaction, loss of trust, and employee dissatisfaction. In this study, by utilizing the Technology–Organization– Environment Framework (TOE), we have identified the factors affecting data quality and proposed a model for achieving high-quality data. One of the contributions of this study is the identification of factors affecting data quality from different perspectives. Such understanding can bring new insights to the discussion and provide contributions to both practitioners and researchers alike.
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