What is essential for ensuring data integrity when preparing datasets in SAP Analytics Cloud?

Applying data quality rules is fundamental for ensuring data integrity when preparing datasets in SAP Analytics Cloud. Data quality rules define the criteria that data must meet to be considered accurate, consistent, and reliable. By implementing these rules, analysts can filter out invalid data, identify duplicates, ensure values fall within expected ranges, and detect inconsistencies. This proactive approach to managing data quality enhances the overall trustworthiness of the dataset, ultimately leading to more reliable analyses and decision-making.

In contrast, while utilizing data enrichment, conducting data blending, and implementing data versioning are useful practices, they do not directly address the core issue of data quality. Data enrichment adds context or additional information to existing datasets but does not validate the quality of the data itself. Data blending involves combining data from different sources, which can introduce challenges related to data consistency and quality without proper validation. Finally, data versioning is a technique for tracking changes in datasets over time, which is useful for maintaining historical integrity but does not directly ensure the quality of the data at any given point. Therefore, the application of data quality rules is the key method for ensuring data integrity.