The BEST way to mitigate the high cost of retrieving electronic evidence associated with potential litigation is to implement policies and procedures for.

Data logging and monitoring are crucial for security and operational oversight but do not directly address the *cost* or *efficiency* of *retrieving* specific evidence during litigation.

Data mining and analytics focus on extracting patterns and insights from data, which is a different objective than efficiently locating and retrieving specific evidence for legal purposes.

Data classification and labeling policies enable an organization to categorize and mark data based on its sensitivity, criticality, and relevance. When litigation arises, this allows for the efficient identification and retrieval of specific, pertinent electronic evidence, significantly reducing the cost and effort of sifting through vast amounts of unclassified data during e-discovery.

Data retention and destruction policies are important for legal compliance and reducing data sprawl, but data *classification and labeling* directly addresses the ability to *find and retrieve* relevant data efficiently when it *is* retained, which is the core challenge of e-discovery costs.