What does data cleansing aim to minimize during the Relativity processing workflow?

Data cleansing is an essential step in the Relativity processing workflow, primarily aimed at ensuring data accuracy and integrity. By minimizing errors during the review process due to inaccurate records, data cleansing helps to create a reliable dataset for analysis and review. When records are accurately represented, it reduces the risk of misinterpretation or incorrect decisions based on flawed data. This enhancement in data quality leads to more effective legal review processes, where attorneys and reviewers depend on precise information to make informed judgments.

In contrast, while reducing redundant data entries, improving processing time, and minimizing user errors are all important considerations in data management, they do not directly reflect the primary purpose of data cleansing, which focuses on the accuracy of records to avoid errors in subsequent analysis and review stages.