What is the purpose of data cleansing in the Relativity processing workflow?

The purpose of data cleansing in the Relativity processing workflow is primarily to enhance quality and minimize errors during the review process. Data cleansing involves identifying and correcting inaccuracies, inconsistencies, and irrelevant data in the dataset before it undergoes processing. By ensuring that the data is clean and well-organized, the accuracy of the subsequent review is improved, enabling reviewers to focus on relevant and high-quality information.

This step is crucial because any errors or poor-quality data could lead to incorrect conclusions or ineffective legal strategies. Therefore, thorough data cleansing helps in producing a more reliable dataset, which is essential for making informed decisions in legal contexts.