An organization is working to minimize the eDiscovery costs associated with the extensive analysis of large sets of electronic dat a. To achieve…

The organization is implementing practices to reduce the volume of data for manual review, which directly addresses the high costs of eDiscovery. Technology-Assisted Review (TAR) uses predictive coding and machine learning to prioritize relevant documents, while data reduction techniques like deduplication and de-NISTing eliminate duplicate and known system files. This combination, described in option B, directly aligns with the goal of narrowing down data for detailed examination, enhancing efficiency, and reducing costs, which is the core of the scenario.

A. Data retention is a broader information governance policy for data lifecycle management, not a specific technique used during the eDiscovery analysis phase to reduce review volume.

C. Ensuring a secure chain of custody is critical for evidence integrity and admissibility but does not reduce the volume of data to be analyzed for cost savings.

D. Data mapping is an initial step in the eDiscovery process to identify data sources and custodians, preceding the data reduction and analysis phase described.

1. The Sedona Conference. (2017). The Sedona Principles

Third Edition: Best Practices

Recommendations & Principles for Addressing Electronic Document Production. 19 SEDONA CONF. J. 1. See Principle 6

which discusses the use of technology to improve the efficiency of eDiscovery.

2. Grossman

M. R.

& Cormack

G. V. (2013). Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review. Richmond Journal of Law & Technology

17(3)

1-48. This paper provides an in-depth analysis of TAR's effectiveness.

3. NIST. (2006). NIST Special Publication 800-86

Guide to Integrating Forensic Techniques into Incident Response. Section 3.3.2 discusses data reduction techniques in the context of forensic analysis.