When is it advisable to use Sampling for Repeated Content rather than analyzing the full dataset?

Using Sampling for Repeated Content is most advisable when dealing with larger document sets, especially those exceeding 100K documents. This approach is beneficial because analyzing a full dataset of that size can be resource-intensive and time-consuming. Sampling allows administrators and analysts to draw conclusions from a manageable subset of data, making the process more efficient while still maintaining a level of accuracy that is often sufficient for insights into the overall content.

With larger datasets, the likelihood of redundancy in the content increases. By employing Sampling, you can effectively minimize the workload while still ensuring that the results reflect the distribution of information within the entire dataset. This method can help streamline the review process and significantly reduce the time and resources required for analysis.

Additionally, more significant datasets often contain varying types of content, and Sampling helps in identifying patterns or prevalence of specific types without the need to sift through every individual document, which could be unfeasible in practice given their volume.