In the fast-paced and dynamically changing landscape of e-discovery technology, a clear grasp of metadata and how it plays a pivotal role in organizing, analyzing, and filtering data, has never been so important. Among some of the most crucial metadata fields are the Date values, which can significantly impact the scope and defensibility of a review and production strategy. However, using dates in metadata filtering is not always straightforward. This article explores key considerations, challenges, and best practices for working with metadata dates in e-discovery.
The “Master Date” Dilemma
One commonly used approach in metadata filtering is the creation of a “Master Date.” This unified date field aggregates various metadata points—such as Date Sent, Date Received, Date Modified, and Creation Date—to represent a single timeline. While this method aims to simplify filtering, it introduces challenges. The Master Date is not an actual metadata field but rather a synthesized value. Consequently, it may not consistently align with the underlying data set, especially when dealing with different data categories and specific and unique applications such as date cut-offs or when multimedia was originally created, for example. These inconsistencies can create confusion and reduce the reliability of the review process.
Ensuring Defensibility in Date Use
The defensibility of date usage is crucial in effective e-discovery. Reviewer managers and stakeholders must clearly understand how dates are applied in the review strategy. Discrepancies between fields such as Date Sent and Date Received are common, particularly when syncing issues occur between workstations and email servers. These issues can result in significant delays, with emails being received hours, days, or even weeks after being sent.
Additionally, some files may lack specific dates or include dates that represent the collection date rather than the original creation date, as is often the case with photographs or videos. In other instances, dates may not be properly fielded or utilized during filtering, leading to inconsistencies in the review process. Without a clear and comprehensive strategy to address these anomalies, there is a heightened risk of oversights, misinterpretations, last-minute delays, and deficiency claims.
Defining Dates in ESI Protocols
To minimize confusion, it is essential to explicitly define which dates are being used for filtering. This can be achieved internally by case leadership and review teams or externally through ESI (Electronically Stored Information) protocols or meet-and-confer negotiations with opposing counsel. Clear definitions ensure consistency and prevent some disputes during litigation. A well-defined approach also provides a framework for addressing date anomalies, such as the aforementioned syncing issues.
Addressing Time Zone Disparities
Time zone adjustments are another critical factor in metadata filtering. Consider a scenario where one data set is processed in GMT and another in EST. Applying a date range filter without accounting for these differences could inconsistently exclude relevant documents. For example, the same email may appear within the filter set for one time zone but outside the range for another. Establishing a consistent time zone standard—and converting all data accordingly—can mitigate this risk.
Challenges with Cloud Data
Cloud-based platforms like Google Workspace introduce unique challenges. For instance, documents in Google Drive or linked attachments “aka. modern attachments” may retain the collection date rather than the original Creation or Date Modified values. This discrepancy often occurs because the act of collecting data from a database, which is what Google Drive inherently is, alters certain metadata fields while converting Google proprietary documents to Windows compatible formats. In such cases, a secondary process may be necessary to overlay the original metadata (maintained in a separate XML document) into the review platform. By carefully managing these adjustments, reviewers can more accurately ensure reliable filtering.
The Problem with Draft Emails
Draft emails add complexity to metadata filtering. Unlike sent or received emails, drafts often lack critical dates. Instead, they sometimes rely on Modified or Creation dates, which are used later in sequencing the Master Date and may not accurately reflect when the draft was first created by the author. This reliance can create timeline gaps and inconsistencies and compromise the comprehensiveness of the review.
Avoiding Data Subset Exclusions
Case managers responsible for document review and productions must take an active role in understanding their data sets. With the support of discovery experts, they should ensure they are guided through a strategy that aligns with their data categories, their requirements, and the target needs for the case. This collaboration is essential for identifying potential pitfalls and implementing a robust and sometimes custom approach to metadata filtering.
One of the most significant risks in metadata filtering is inadvertently excluding subsets of data. Applying a date range filter across all data categories without accounting for specific anomalies—such as syncing issues or missing metadata fields—can result in overlooked evidence. A more robust approach involves tailoring date ranges to each data category and proactively identifying potential pitfalls unique to the data being reviewed.
Mastering Metadata: Key Steps for Review Success
In e-discovery, understanding and effectively managing metadata dates is critical to ensuring a defensible and efficient review process. The complexities of metadata—such as inconsistencies in Master Dates, time zone discrepancies, and anomalies with cloud data or draft emails—demand a strategic and tailored approach. By clearly defining date parameters in ESI protocols, addressing time zone differences, and collaborating closely with discovery experts, review teams can mitigate risks of exclusion and misinterpretation. A proactive and detailed methodology enhances the reliability of metadata filtering, supports defensible decision-making, and ensures that all relevant evidence is accounted for.
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