What method is commonly used for data validation in business data analytics?

Data cleansing is a crucial method used for data validation in business data analytics. This process involves identifying and rectifying errors or inconsistencies within datasets to ensure that the data is accurate, complete, and reliable. By applying data cleansing techniques, analysts can remove duplicate entries, correct misspellings, and address discrepancies, which enhances the overall quality of the data being analyzed.

In business data analytics, validated data is essential because it directly impacts the conclusions drawn from the data analysis and the subsequent business decisions made based on that data. High-quality data leads to more reliable insights, which are vital for effective strategic planning and operational efficiency.

While the other options serve important roles in data management and analysis, they do not primarily focus on data validation. Data visualization refers to the graphical representation of data, which assists in understanding data trends and patterns but does not involve validating the underlying data. Data segregation involves separating data into distinct categories or groups, which may be useful for organizational purposes, but it does not validate the accuracy of the data itself. Data estimation is a process of inferring or predicting values based on existing data, rather than ensuring the validity of that data. Thus, data cleansing stands out as the primary approach for ensuring data integrity in analytics.