Data quality is correctness and dependability of the data in a dataset are referred to as data quality. It contains several characteristics, including timeliness, correctness, completeness, consistency, and relevancy. Error-free data ensures that it represents the values that are intended to be represented in the real world. Being complete means that no information is missing that might interfere with analysis. Because out-of-date data can impede decision-making processes, timeliness highlights the significance of current information. Uniformity both inside and between datasets is a key component of consistency, since it eliminates potential sources of misunderstanding. Finally, relevancy keeps unnecessary information from being included by ensuring that the data is relevant to the intended use. In many different domains and sectors, maintaining excellent data quality is essential for dependable studies, sound judgment, and insightful conclusions. More in depth explanation on data quality on the video link below.