Improving internal procedures in a field is a benefit associated with which practice?

The practice of data analytics is closely associated with improving internal procedures because it involves the systematic examination of data to uncover trends, patterns, and insights that can inform decision-making. By analyzing various data sets, organizations can identify inefficiencies or areas needing enhancement within their processes. This can lead to streamlined operations, better resource allocation, improved productivity, and overall better performance.

When data analytics is applied effectively, it enables organizations to make informed decisions based on evidence rather than intuition, thereby significantly enhancing their internal procedures. In contrast, the other practices—data mining, data cleaning, and data visualization—play important roles but focus on specific aspects of data handling. Data mining is more about extracting patterns from large datasets, data cleaning deals with correcting or removing inaccurate data, and data visualization is primarily about presenting data in a visually impactful way to aid understanding. While these practices are integral to the data analysis process, it is data analytics comprehensively that drives improvements in internal procedures based on insights derived from data.