Which method helps to ensure reliability of data across multiple systems?

Cross-validation is a method that enhances the reliability of data across multiple systems by ensuring consistency and accuracy through repeated checks and comparisons. This technique involves partitioning the data into subsets, where certain parts are used for training a model while others are used for testing. By validating the results across different segments of data, discrepancies can be identified and addressed, ensuring that the data remains reliable regardless of the system it is processed in.

Cross-validation effectively mitigates overfitting and helps in understanding how the results can generalize to an independent dataset, which is crucial for maintaining data integrity across different systems. Therefore, its use in confirming that data behaves consistently across various scenarios and systems strengthens the trustworthiness of the information being managed.

While other methods such as data profiling, data audits, and understanding reasonable expectations also play roles in data management and quality assurance, they do not specifically focus on the iterative validation process that cross-validation offers. Data profiling focuses on analyzing data quality attributes, data audits entail systematic reviews, and reasonable expectations pertain to the anticipated behavior of data rather than the validation process itself.