High-quality artificial intelligence (AI) data provides accurate information for developing AI models. These results in increasing the efficiency of the model. On the other hand, if low-quality data is used, it may adversely affect the development of AI models. To improve the quality of our research, we need to increase the quality of AI data. This is possible through systematic quality control and verification of the data. Currently, there are various guidelines such as the data quality act of the US, the ISO 8000 series of the International Organization for Standardization, and the Big Data quality verification standard of the United Nations, as well as Korea's database quality certification. In this study, the current status of data quality management is identified and its implications are considered.
Citations
Citations to this article as recorded by
Synthetic data generation with hybrid quantum-classical models for the financial sector Otto M. Pires, Mauro Q. Nooblath, Yan Alef C. Silva, Maria Heloísa F. da Silva, Lucas Q. Galvão, Anton S. Albino The European Physical Journal B.2024;[Epub] CrossRef