Data quality and its usage
Data quality is multifaceted, encompassing several characteristics or dimensions that collectively determine its suitability for use in operations, decision-making, and planning. Understanding these dimensions is crucial for assessing, improving, and maintaining the quality of data in any context. It refers to the overall utility of data based on attributes such as accuracy, completeness, reliability, relevance, and timeliness. It is an assessment of how well-suited data is for making decisions, driving processes, and achieving business objectives.
A high-quality dataset in the context of data science and machine learning is a collection of data that is accurate, complete, consistent, relevant, timely, and reliable. Such datasets are crucial for developing robust machine learning models that perform well in real-world applications.
Data quality needs to be maintained throughout the machine learning life cycle, from data collection through the data preprocessing...
