The emphasis of data quality in the data harmonization process
Hoang, Dung (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023052614787
https://urn.fi/URN:NBN:fi:amk-2023052614787
Tiivistelmä
The thesis aims to analyze data quality and its impact on the data harmonization process. Its purpose is to discover problems, caused by the poor quality of the data, in the data harmonization process and the main goals are to find out methods used to guarantee the data immaculacy for the afterward process as well as to evaluate the room for developing methods that ameliorate the data harmonization process in the future.
In the theoretical framework, the definition of data and data harmonization are explained. Furthermore, the perception of data quality is discussed. From this proposition, the advantages of good data quality and the disadvantages of poor data quality, as well as the measuring methods for data quality, are investigated. Once the causes and the effects are determined, solutions for improving data quality are brought up, providing thought-provoking ideas for similar issues in the future.
The research adopted a qualitative method. An interview was held with a professional in the field of data management. Through the interview, a thorough and practical knowledge of the impact of data quality on the data harmonization process was acquired. Furthermore, the interview revealed criteria for a good data quality source and showed the interviewee's perspective on the Artificial Intelligence support in improving data quality in the future.
The findings of the research indicated that the impact of poor data quality on the data harmonization results in bad decision-making, which negatively affects the revenue of the company. For the purpose of providing a good quality data source, Completeness, Accuracy, and Consistency are obligatory criteria. However, the majority of firms are not aware of the importance of data quality. Moreover, AI can contribute a great support to humans on the data cleaning process in the future, depending on the human’s ability in innovation.
To conclude, firms currently do not take great consideration about data quality management, which is crucial to various business operations.
In the theoretical framework, the definition of data and data harmonization are explained. Furthermore, the perception of data quality is discussed. From this proposition, the advantages of good data quality and the disadvantages of poor data quality, as well as the measuring methods for data quality, are investigated. Once the causes and the effects are determined, solutions for improving data quality are brought up, providing thought-provoking ideas for similar issues in the future.
The research adopted a qualitative method. An interview was held with a professional in the field of data management. Through the interview, a thorough and practical knowledge of the impact of data quality on the data harmonization process was acquired. Furthermore, the interview revealed criteria for a good data quality source and showed the interviewee's perspective on the Artificial Intelligence support in improving data quality in the future.
The findings of the research indicated that the impact of poor data quality on the data harmonization results in bad decision-making, which negatively affects the revenue of the company. For the purpose of providing a good quality data source, Completeness, Accuracy, and Consistency are obligatory criteria. However, the majority of firms are not aware of the importance of data quality. Moreover, AI can contribute a great support to humans on the data cleaning process in the future, depending on the human’s ability in innovation.
To conclude, firms currently do not take great consideration about data quality management, which is crucial to various business operations.