Category Archives: Case Law

On TAP: Targeted, Automated, and Proportional Collection for Modern e-Discovery

By John Patzakis

Proportionality is now the hottest legal issue in the area of eDiscovery, with the largest number of eDiscovery-related cases in the past year addressing the subject. eDiscovery attorney Kelly Twigger leads a team who produced an excellent analysis of 2020 case law, noting “a big jump to 889 in 2020” of cases addressing proportionality, “which represented nearly a third (31%) of all (eDiscovery) case law decisions last year.” The report notes that “[p]roportionality arguments have become a weapon in arguing scope of discovery and the sharp rise in disputes has illustrated the need for more systematic and standardized approaches to assessing proportionality in cases today.” 

Proportionality-based eDiscovery is a goal that all judges and corporate attorneys want to attain. Under Federal Rule of Civil Procedure 26(b)(1), parties may discover any non-privileged material that is relevant to any party’s claim or defense and proportional to the needs of the case. Lawyers that take full advantage of the proportionality rule can greatly reduce cost, time and risk associated with otherwise inefficient eDiscovery.

Proportionality is getting a further boost as George Washington University Law School is developing an important proportionality benefit-and-burden model that provides a practical structure for assessing claims of proportionality. The model features a heat map mechanism to identify relevant custodians and data sources to enable a more objective application of proportionality, thereby facilitating negotiations and better informing the bench.

The GW Law model is much needed, as while there is keen awareness of proportionality in the legal community, attaining the benefits requires the ability to operationalize workflows as far upstream in the eDiscovery process as possible. For instance, when you’re engaging in data over-collection, which in turn runs up of a lot of human time and processing costs, the ship has largely sailed before you are able to perform early case assessments and data relevancy analysis, as much of the discovery costs have already been incurred at that point. The case law and the Federal Rules provide that the duty to preserve only applies to potentially relevant information, but unless you have the right operational processes in place, you’re losing out on the ability to attain the benefits of proportionality.

An example of a process that effectively applies proportionality on an operational basis would be an iterative exercise to identify relevant custodians, their data sources, applicable data ranges, file types and agreed upon keywords, following the process outlined in  McMaster v. Kohl’s Dep’t Stores, Inc., No. 18-13875 (E.D. Mich. July 24, 2020), and collect only the data that is responsive to this specific criteria. The latest enterprise collection tech from Relativity and X1 enable such detailed and proportional criteria to be applied in-place, at the point of collection. This reduces the data volume funnel by as much as 98 percent from over-collection models, yet with increased transparency and compliance. In other words, a collection process that targeted, automated and proportional, instead of one that is overbroad and manual.

To learn more about these concepts, please tune in on April 13, where attorney David Horrigan of Relativity and Mandi Ross of Prism Litigation Technology will be leading a webinar to discuss the legal and operational considerations and benefits of proportionality. The webinar will also feature a live exercise performing a pre-collection proportionality analysis on remote employee data. You can register here.

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Filed under Best Practices, Case Law, ECA, eDiscovery, eDiscovery & Compliance, Enterprise eDiscovery, ESI, law firm, Preservation & Collection, proportionality

Federal Judge: Custodian Self-Collection of ESI is Unethical and Violates Federal Rules of Civil Procedure

By John Patzakis

In E.E.O.C. v. M1 5100 Corp., (S.D. Fla. July 2, 2020), Federal District Judge Matthewman excoriated defense counsel for allowing the practice of unsupervised custodian ESI self-collection, declaring that the practice “greatly troubles and concerns the court.” In this EEOC age discrimination case, two employees of the defendant corporation were permitted to identify and collect their own ESI in an unsupervised manner. Despite no knowledge of the process the client undertook to gather information (which resulted in only 22 pages of documents produced), counsel signed the responses to the RFP’s in violation of FRCP Rule 26(g), which requires that the attorney have knowledge and supervision of the process utilized to collect data from their client in response to discovery requirements.Gavel and books

This notable quote from the opinion provides a very strong legal statement against the practice of ESI custodian self-collection:

“The relevant rules and case law establish that an attorney has a duty and obligation to have knowledge of, supervise, or counsel the client’s discovery search, collection, and production. It is clear to the Court that an attorney cannot abandon his professional and ethical duties imposed by the applicable rules and case law and permit an interested party or person to ‘self-collect’ discovery without any attorney advice, supervision, or knowledge of the process utilized. There is simply no responsible way that an attorney can effectively make the representations required under Rule 26(g)(1) and yet have no involvement in, or close knowledge of, the party’s search, collection and production of discovery…Abdicating completely the discovery search, collection and production to a layperson or interested client without the client’s attorney having sufficient knowledge of the process, or without the attorney providing necessary advice and assistance, does not meet an attorney’s obligation under our discovery rules and case law. Such conduct is improper and contrary to the Federal Rules of Civil Procedure.”

In his ruling, Judge Matthewman stated that he “will not permit an inadequate discovery search, collection and production of discovery, especially ESI, by any party in this case.” He gave the defendant “one last chance to comply with its discovery search, collection and production obligations.”  He then also ordered “the parties to further confer on or before July 9, 2020, to try to agree on relevant ESI sources, custodians, and search terms, as well as on a proposed ESI protocol.” The Court reserved ruling on monetary and evidentiary sanctions pending the results of Defendants second chance efforts.

A Defensible Yet Streamlined Process Is Optimal

EEOC v. M1 5100, is yet another court decision disallowing custodian self-collection of ESI and underscoring the importance of a well-designed and defensible eDiscovery collection process. At the other end of the spectrum, full disk image collection is another preservation option that, while being defensible, is very costly, burdensome and disruptive to operations. Previously in this blog, I discussed at length the numerous challenges associated with full disk imaging.

The ideal solution is a systemized, uniform and defensible process for ESI collection, which also enables targeted and intelligent data collection in support of proportionality principles. Such a capability is only attainable with the right enterprise technology. With X1 Distributed Discovery (X1DD), parties can perform targeted search and collection of the ESI of hundreds of endpoints over the internal network without disrupting operations. The search results are returned in minutes, not weeks, and thus can be highly granular and iterative, based upon multiple keywords, date ranges, file types, or other parameters. This approach typically reduces the eDiscovery collection and processing costs by at least one order of magnitude (90%), thereby bringing much needed feasibility to enterprise-wide eDiscovery collection that can save organizations millions while improving compliance by maintaining metadata, generating audit logs and establishing chain of custody.

And in line with the Judge’s guidance outlined in EEOC v. M1 5100, X1DD provides a repeatable, verifiable and documented process for the requisite defensibility. For a demonstration or briefing on X1 Distributed Discovery, please contact us.

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Filed under Best Practices, Case Law, collection, eDiscovery, ESI, Uncategorized

Lawson v. Spirit Aerosystems: Federal Court Blasts “Bloated” ESI Collection, Rendered TAR Ineffective

By John Patzakis

Technology Assisted Review (TAR), when correctly employed, can significantly reduce legal review costs with generally more accurate results than other traditional legal review processes. However, the benefits associated with TAR are often undercut by the over-collection and over-inclusion of Electronically Stored Information (ESI) into the TAR process. These challenges played out in spades in the recent decision in Lawson v. Spirit Aerosystems, where a Kansas federal judge issued a detailed ruling outlining the parties’ eDiscovery battles, use of Technology Assisted Review (TAR), and whether further TAR costs should be shifted to the Plaintiff. The ex-CEO of Spirit Aerosystems brought his suit accusing Spirit of unlawfully withholding $50 million in retirement benefits over his alleged violation of a non- compete agreement.

Lessons Learned from New Technology-Assisted Review Case Law ...

The Lawson court outlined two ways in particular how ESI over-collection can detrimentally impact TAR. First, the more data introduced into the process, the higher the cost and burden. Some practitioners believe it is necessary to over-collect and subsequently over-include ESI to allow the TAR process to sort everything out. Many service providers charge by volume, so there can be economic incentives that conflict with what is best for the end-client. In some cases, the significant cost savings realized through TAR are erased by eDiscovery costs associated with overly aggressive ESI inclusion on the front end. Per the judge in Lawson, “the TAR set was unnecessarily voluminous because it consisted of the bloated ESI collection” due to overbroad collection parameters.

The court also outlined how the TAR process is much more effective when the initial set of data has a higher richness (also referred to as “prevalence”) ratio. In other words, the higher the rate of responsive data in the initial data set, the better. It has always been understood that document culling is very important to successful, economical document review, and that includes TAR. As noted by Lawson court, “the ‘richness’ of the dataset…can also be a key driver of TAR expenses. This is because TAR is not as simple as loading the dataset and pushing a magic button to identify the relevant and responsive documents. Rather, the parties must devote the resources (usually a combination of attorneys and contract reviewers) necessary to “educate” or “train” the predictive algorithm, typically through an ongoing process…” According to the courts’ decision, the inefficiencies in the process resulted in an estimated TAR bill of $600,000 involving the review of approximately 200 GBs of data. This is far too expensive for TAR to be feasible as a standard litigation process, and the problems all started with the “bloated” ESI collection.

To be sure, the volume of ESI is growing exponentially and will only continue to do so. The costs associated with collecting, processing, reviewing, and producing documents in litigation are the source of considerable pain for litigants, including the Plaintiff in Lawson, who will, per the courts’ ruling, incur at least a substantial amount of the TAR bill under the cost-shifting order. The only way to reduce that pain to its minimum is to use all tools available in all appropriate circumstances within the bounds of reasonableness and proportionality to control the volumes of data that enter the discovery pipeline, including TAR.

Ideally, an effective and targeted collection capability can enable parties to ultimately process, host, review and produce less ESI.  This capability should enable a pre-collection early case assessment capability (ECA) to foster cooperation and proportionality in discovery by informing the parties early in the process about where relevant ESI is located and what ESI is significant to the case. And with such benefits also comes a much more improved TAR process. X1 Distributed Discovery (X1DD) uniquely fulfills this requirement with its ability to perform pre-collection early case assessment, instead of ECA after the costly, time consuming and disruptive collection phase, thereby providing a game-changing new approach to the traditional eDiscovery model.  X1DD enables enterprises to quickly and easily search across hundreds of distributed endpoints from a central location.  This allows organizations to easily perform unified complex searches across content, metadata, or both and obtain full results in minutes, enabling true pre-collection ECA with live keyword analysis and distributed processing and collection in parallel at the custodian level. To be sure, this dramatically shortens the identification/collection process by weeks if not months, curtails processing and review costs from not over-collecting data, and provides confidence to the legal team with a highly transparent, consistent and systemized process. And now we know of another key benefit of an effective collection and ECA process: much more accurate and feasible technology assisted review.

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Filed under Best Practices, Case Law, Case Study, collection, ECA, eDiscovery, Enterprise eDiscovery, ESI