Category Archives: Enterprise eDiscovery

February 22, 2021 · 10:49 AM

Meeting Modern Discovery Demands with RelativityOne Collect and X1

By John Patzakis

As we’ve all heard time and again, 2020 was a transformative year—and in our space, it has had a huge impact and really changed the way people work.

With widespread teams, evolving data types, growing data volumes, and deadlines getting shorter—well, the entire e-discovery process has the potential to spiral out of control.

But not for those who are well prepared to meet these modern challenges.

Here at X1, we’ve been working hard on giving modern organizations the technology they need to get data identified, collected, and ingested with maximum effectiveness for years. Now, with X1 integrated into RelativityOne via RelativityOne Collect, users of the industry-leading SaaS e-discovery platform can accomplish this in more targeted and faster ways than ever before.

Let’s take a look at what this integration means, and why it offers non-negotiable capabilities to today’s legal teams.

A Remote Workforce

Work from home has rapidly accelerated and will likely not dramatically reverse in the foreseeable future. Many of us will continue to work remotely for months to come—or perhaps permanently.

These trends were already ramping up, but 2020 hammered the accelerator on telecommuting and remote working. According to Global Workplace Analytics, before the COVID-19 pandemic, just 3.6 percent of US workers worked from home multiple days a week. That number is now estimated at 25-30 percent.

This may be a boon for work-life balance, but it poses big complications for data collection in response to litigation and investigations. Historically, this process has required disk imaging or other methods that often prompted collections to be performed in person. In a shared office, that might be easy to accomplish (in fact, it might be too easy, resulting in vast over-collections of data in many cases). But with everyone working from home and confronted by concerns about social distancing, travel restrictions, and possible quarantines, it quickly became untenable last year.

Thanks to those circumstances and the increased use of the cloud for data storage, demand for web-enabled collections is up—by a lot.

RelativityOne Collect gives legal teams the ability to index and search on data in place, analyze the contents of a data source, and categorize data quickly to identify what warrants collection and what can be eliminated—all before it’s pulled from the source and brought into a workspace, and from anywhere. Previously, RelativityOne Collect was able to directly connect with Office 365 and Slack sources to perform these remote collections; with the integration of X1’s innovative endpoint technology, Collect can now gather data from additional sources like email and files on laptops, servers, or network locations.

Then, the targeted data is seamlessly imported into Relativity—no extra processing, downloads, uploads, or risky data hand-offs required.

This means a streamlined process that can be performed from anywhere, on multiple custodians at a time, and across many of the most common data sources. Forward-thinking teams are saying goodbye to cumbersome and expensive ESI collection and processing tools in favor of this bright new world.

Proportional Data Decisions

Another trend that began to take hold over the last decade is the move toward targeted collections. Gone are the days when full disk imaging was standard practice. Today’s sources are far too densely packed with data to assume everything needs to be captured for every matter. Over-collecting means not just increased costs for data storage on your matters, but huge amounts of time wasted on reviewing unnecessary documents—and all of this adds up to proportionality violations.

The courts agree: Complete disk imaging is by and large unwarranted in civil litigation. (In particular, see Diepenhorst v. City of Battle Creek.)

Instead, what is needed is a middle ground approach in the form of a targeted, automated, and remote collection that provides documentation for defensibility and an emphasis on speed to review.

With traditional processes, there is an inability to quickly and remotely search across and access distributed unstructured data in-place. e-Discovery teams may end up spending weeks or more collecting data, with traditional workflows taking as long as 30 days to complete before data is available for review.

In addition to putting deadlines and case strategy efforts in jeopardy, these delays can increase the risk of errors and security vulnerabilities as data is moved between systems and team members rush to get things done. With X1 endpoint collections integrated into Collect, data can be accessed, searched upon, culled, and ingested directly into your review workspace with no go-betweens required—so your targeted data sets are defensible and in good hands from start to finish. Oh, and that 30 days is cut down to mere hours.

This enables much needed efficiencies in the e-discovery process in the face of growing data volumes, widespread teams and data sources, and diversified data types, because you can target which data you bring into your workspace before it’s published (and have detailed reports on those decisions to back up your final collection). You’ll see benefits not just in greater speed to review, but also greater speed in review, because you’ve eliminated a lot of inefficiencies from the get-go. Plus, you’re protecting potentially privileged or secret information that doesn’t need to be pulled into a project in the first place.

Process Democratization

Finally, there’s a third evolving trend in the collection space. For a long time, there has been a perception that doing collections is difficult, and requires a lot of specialized training or certifications. With the proliferation of the cloud and new data sources, however, this has started to shift. Most e-discovery cases do not require collection by a certified forensics examiner, especially since not every drive needs to be imaged. Instead, as the industry has moved more toward targeted collections, the accessibility of the process has greatly improved.

Additionally, today’s legal teams are under great pressure to do more with less—less money, less time, and less help. As a result, they need to be empowered to perform some collections themselves even if they don’t have that highest degree of training and expertise. Fortunately, cases using targeted e-discovery collections and collections from cloud sources don’t generally require such extensive training.

When organizations are given the tools to do some of this work internally, they can save forensic resources for when they’re truly needed (on really hairy or dicey matters).

RelativityOne Collect’s easy-to-use interface lets any individual perform those type of targeted e-discovery and cloud collections with minimal training. And as a growing number of organizations are experiencing a greater need to remotely collect from computer endpoints as well, Relativity and X1 have partnered to build an integration to help in-house teams do that, too. 

So, while numerous courts have held that custodian self-collection is simply not defensible, capable and well-equipped legal teams can and do collect data from custodians in a defensible and secure manner. Then, those same team members can take what they’ve learned from this at-a-glance view of the origins of their data sets, and bring that knowledge to the rest of the e-discovery or investigation project.

The result is streamlined, end-to-end e-discovery in a single, secure, and easy-to-use platform.

And we will be demonstrating this integration live on our February 24 joint webinar with Relativity: “RelativityOne Collect and X1: Streamlining the Global Collection Process.” Please join us by registering here.

This blog post is also prominently featured on the Relativity blog site here.

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Filed under eDiscovery & Compliance, Enterprise eDiscovery, Information Management, law firm, Preservation & Collection

December 9, 2020 · 10:44 AM

Traditional eDiscovery Processing is Now Obsolete

By John Patzakis

eDiscovery can be a very expensive process and time consuming when traditional methods are employed. With legacy processes, from the time ESI collection starts, it often takes weeks for the data to finally end up in review. Time is money, and this dramatically increases costs as well as risk.

ESI processing is a dedicated and often expensive step in the EDRM workflow. The majority of ESI processing consists of data culling and filtering, deduplication, text extraction, metadata preservation, and then staging the data for upload into a review platform, often in the form of a load (DAT) file.  Using ESI processing methods that involve on-premise hardware appliances that are not integrated with the collection process and do not integrate with review platforms like Relativity significantly increase cost and time delays. This means practitioners have to spend the often several weeks that are required by other cumbersome solutions through manual collections and multiple hand-offs.

However, the latest in collection technologies will now combine targeted collection with these processing steps that are performed “on the fly” and in the background so that the data is automatically collected, processed and uploaded into a review platform such as Relativity in one fell swoop.

The graphic below is an illustration contrasting the challenges associated with traditional eDiscovery processes, with the far more efficient new paradigm. When you engage in manual collection, and then manual on-premise hardware-based processing, and finally manual upload to review, you are extending the process by often weeks, you are dramatically increasing cost and risk with many manual data handoffs.

Providing a contrast to traditional methods, a recent Relativity webinar featured the integration of the X1 Distributed Discovery platform with its RelativityOne Collect solution. A live demonstration performed by Relativity Product Manager Greg Evans highlighted in real time how the integration dramatically improves the enterprise eDiscovery process by enabling a targeted and efficient search and collection process, with full and integrated ESI processing. Within minutes, data collected from endpoints with X1 is populated straight into a Relativity workspace, fully processed and ready for review, without any human interaction once the collection is started.

So in terms of the big picture, this X1/Relativity integration not only streamlines enterprise ESI collection, but it relegates ESI processing to a completely automated background function as an afterthought. That’s what disruption looks like.

A recording of the X1/Relativity integration webinar can be accessed here.

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Filed under Best Practices, collection, eDiscovery, Enterprise eDiscovery, ESI, Uncategorized

December 1, 2020 · 9:53 AM

Relativity Highlights Its X1 Integration for ESI Collection

By John Patzakis

Recently, Relativity hosted a live webinar featuring the integration of the X1 Distributed Discovery platform with its RelativityOne Collect solution. This X1/Relativity integration enables game-changing efficiencies in the eDiscovery process by accelerating speed to review, and providing an end-to-end process from identification through production. As stated by Relativity Chief Product Officer Chris Brown: “Our exciting new partnership with X1 highlights our continued commitment to providing a streamlined user experience from collection to production…RelativityOne users will be able to combine X1’s innovative endpoint technology with the performance of our SaaS platform, eliminating the cumbersome process of manual data hand-offs and allowing them to get to the pertinent data in their case – faster.”

The webinar featured a live demonstration showing X1 quickly collecting data across multiple custodians and seamlessly importing that data into RelativityOne in minutes. Relativity Collect currently supports Office 365 and Slack sources, and Relativity Product Manager Greg Evans noted that “this X1 integration will now enable Relativity Collect to also reach emails and files on laptops, servers,” and other network sources. The webinar outlined how the Relativity/X1 integration streamlines eDiscovery processes by collapsing the many hand-offs built into current EDRM workflows to provide greater speed and defensibility. Evans also said that new normal of web-enabled collections of remote custodians and data sources was a major driver for the Relativity/X1 alliance, as “remote collections now represent 90 percent of all eDiscovery collections happening right now.”

Adam Rogers, of Complete Discovery Source, a customer of both X1 and RelativityOne, highlighted a recent major multi-national litigation where the X1 and Relativity integration was critical to the success of the project. Adam noted that the effort would have taken about 30 days utilizing traditional methods, “but with this X1 and Relativity integration, we cut it down to 3 days, because with X1, we were able to index everything in-place, search, analyze and categorize that data right away, and then release that data to Relativity for review.”

The live demonstration performed by Greg Evans highlighted in real time how the integration improves the enterprise eDiscovery collection and ECA process by enabling a targeted and efficient search and collection process, with immediate pre-collection visibility into custodial data. X1 Distributed Discovery enhances the eDiscovery workflow with integrated culling and deduplication, thereby eliminating the need for expensive and cumbersome electronically stored information (ESI) processing tools. That way, the ESI can be populated straight into Relativity from an X1 collection.

The X1 and Relativity integration addresses several pain points in the existing eDiscovery process. For one, there is currently an inability to quickly and remotely search across and access distributed unstructured data in-place, meaning eDiscovery teams have to spend weeks or even months to collect data as required by other cumbersome solutions. Additionally, using ESI processing methods that involve appliances that are not integrated with the collection will significantly increase cost and time delays.

So in terms of the big picture, with this integration providing a complete platform for efficient data search, eDiscovery and review across the enterprise, organizations will save a lot of time, save a lot of money, and be able to make faster and better decisions. When you accelerate the speed to review and eliminate over-collection, you are going to have much better early insight into your data and increase efficiencies on many levels.

A recording of the X1/Relativity integration webinar can be accessed here.

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Filed under Best Practices, collection, ECA, eDiscovery, Enterprise eDiscovery, ESI

July 15, 2020 · 9:38 AM

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

June 2, 2020 · 10:04 AM

How to Implement an Effective eDiscovery Search Term Strategy

By Mandi Ross and John Patzakis

A key Federal Rules of Civil Procedure provision that greatly impacts eDiscovery processes is Rule 26(f), which requires the parties’ counsel to “meet and confer” in advance of the pre-trial scheduling conference on key discovery matters, including the preservation, disclosure and exchange of potentially relevant electronically stored information (ESI). With the risks and costs associated with eDiscovery, this early meeting of counsel is a critically important means to manage and control the cost of eDiscovery, and to ensure relevant ESI is preserved.

A very good authority on the Rule 26(f) eDiscovery conference is the “Suggested Protocol for Discovery of Electronically Stored Information,” provided by then Magistrate Judge Paul W. Grimm and his joint bar-court committee. Under Section 8 of the Model Protocol, the topics to be discussed at the Rule 26(f) conference include: “Search methodologies for retrieving or reviewing ESI such as identification of the systems to be searched;” “the use of key word searches, with an agreement on the words or terms to be searched;” “limitations on the time frame of ESI to be searched;” and “limitations on the fields or document types to be searched.”x1-collection-img

Optimizing the process of developing keyword searches, however, is no easy task, especially without the right technology and expertise. The typical approach of brainstorming a list of terms that may be relevant and running the search on a dataset to be reviewed results in a wide range of inefficiencies. Negotiations over proper usage of search terms may become onerous and contentious. Judges are often tasked with making determinations regarding the aptness of the methodology, and many are reluctant to do so. Thus, the use of outside expertise leveraging indexing in place technology is beneficial in building an effective and comprehensive search term strategy.

The courts agree. In Victor Stanley v. Creative Pipe, U.S. District Court Judge Paul Grimm explains, “Selection of the appropriate search and information retrieval technique requires careful advance planning by persons qualified to design effective search methodology.”

Building a sound search strategy is akin to constructing a building. First, lay the foundation with a clear understanding of the claims and defenses of the case and the types of documents that will support a legal strategy. Once a solid foundation is built, the structure of language, logical expressions, and metadata are blended as necessary to create the appropriate set of robust Boolean searches. These searches then target the retrieval of responsive documents, and consistently achieve a staggering 80 percent reduction in data volumes to be reviewed.

It’s quite simple. If a document does not contain the defined language, then the document is unlikely to be relevant. The best way to find the language specific to the claims and defenses is to create a linguistic narrative of the case. This not only helps construct a roadmap for a comprehensive strategy designed to reduce the volume of data, it also creates a thorough categorization system for organization and prioritization of review. The approach is straightforward, flexible, and adaptive to client objectives, whether during early case assessment, linear or technology-assisted review, or anything in between.

The narrative search approach includes the following steps:

  1. Issue Analysis: Create an unambiguous definition of each issue that characterizes the claims being made and the defenses being offered.
  2. Logical Expression Definition: Define the specific expressions that encapsulate each issue. There may be multiple expressions required to convey the full meaning of the issue.
  3. Component Identification and Expansion: Distill each logical expression into specific components. These components form the basis for the expansion effort, which is the identification of words that convey the same conceptual meaning (synonyms).
  4. Search Strategies: Determine the appropriate parameters to be used for proximity, as well as developing a strategy for searching non-standard, structured data, such as spreadsheets, non-text, or database files.
  5. Test Precision and Recall: In tandem with the case team, review small sample sets to refine the logical expression statements to improve precision and recall.

The effectuation of this process requires the right technology that enables its application in real time. The ability to index data in place is a game changer, as it provides legal teams early insight into the data and validates search term sampling and testing instantly, without first requiring data collection. This is in contrast to the outdated, costly, and time-consuming process involving manual data collection and subsequent migration into a physical eDiscovery processing platform. The latter process negates counsel’s ability to conduct any meaningful application of search term proportionality, without first incurring significant expense and loss of time.

X1 Distributed Discovery enables enterprises to quickly and easily search across thousands 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 search analytics with live keyword analysis and distributed processing and collection in parallel at the custodian level. This dramatically shortens the identification/collection process by weeks if not months, curtails processing and review costs by not over-collecting data, and provides confidence to the legal team with a highly transparent, consistent and systemized process.

Led by an experienced consulting team that leverages cutting-edge technology, this innovative narrative methodology, created by the experts at Prism Litigation Technology, enriches common search terms by adding layers of linguistic and data science expertise to create a fully defensible, transparent, and cogent approach to eDiscovery. For more on this workflow, please see the white paper: Don’t Stop Believin’: The Staying Power of Search Term Optimization.

Mandi Ross is the CEO of Prism Litigation Technology (www.prismlit.com)

John Patzakis is Chief Legal Officer and Executive Chairman at X1 (www.X1.com)

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Filed under Best Practices, ECA, eDiscovery, Enterprise eDiscovery, ESI, Preservation & Collection