Which validation method includes checking data against set expectations?

The method of "reasonable expectations" is centered on validating data by comparing it to predefined criteria or benchmarks that are deemed acceptable. This approach focuses on ensuring that the data collected meets certain expectations based on historical trends, statistical ranges, or business rules. By establishing what is considered reasonable or expected, organizations can effectively identify anomalies or inconsistencies in the data.

This validation process is critical in data management because it ensures that the data used for analysis, reporting, or decision-making is not only accurate but also aligns with the organization's objectives and standards. The use of reasonable expectations helps to maintain data quality by allowing for the identification of outliers or unexpected values that may indicate errors or issues in data collection.

While other options related to data validation exist, they serve different purposes. Cross-validation primarily relates to assessing the accuracy of models rather than directly validating the data itself. Data profiling involves analyzing the data to understand its characteristics, but it does not inherently check against expectations. Data audits focus on reviewing data processes and systems for compliance and integrity rather than specifically validating data against set expectations. Hence, utilizing reasonable expectations is essential for ensuring data accuracy and reliability within an organization's data governance framework.