Running summarizing tools on a new article

Abstract: Over the past decades, the topic of data quality became extremely important in various application fields. Originally developed for data warehouses, it received a strong push with the big data concept and artificial intelligence systems. In the presented chapter, we are looking at traditional data quality dimensions, which mainly have a more technical nature. However, we concentrate mostly on the idea of defining a single data quality determinant, which does not substitute the dimensions but allows us to look at the data quality from the point of view of users and particular applications. We consider this approach, which is known as a fit-to-use indicator, in two domains. The first one is the test data for complicated multi-component software systems on the example of a stock exchange. The second domain is scientific research on the example of validation of handwriting psychology. We demonstrate how the fit-to-use determinant of data quality can be defined and formalized and what benefit to the improvement of data quality it can give.