Description: Data quality assessment is a critical process that involves analyzing and determining the suitability of the data used in analysis and decision-making. This process focuses on several key aspects, such as accuracy, completeness, consistency, relevance, and timeliness of the data. Data quality is essential to ensure that artificial intelligence (AI) models generate reliable and useful results. In the context of explainable artificial intelligence, data quality assessment becomes even more crucial, as the results of models must be understandable and justified. If the data is of low quality, the results may be misleading or erroneous, leading to inadequate decisions. Therefore, data quality assessment is not just about ensuring that the data is correct, but also about ensuring that it is suitable for the specific purpose for which it is being used. This process involves the implementation of metrics and tools that allow analysts and data scientists to identify issues in datasets and make necessary corrections before proceeding with analysis or building AI models.
