Tag Archives: X1

May 21, 2024 · 12:12 PM

Microsoft 365 eDiscovery Throttling is Structural and Won’t Be Going Away

By Chas Meier

Users of Microsoft 365 for eDiscovery and Information Governance continue to encounter significant problems with low throughput and defensibility. Many customers report to us that Purview eDiscovery Premium’s documented limitations, including a 2GB per hour indexing limit, prevent them from using the platform to handle anything other than small matters. A routine eDiscovery matter involving one hundred custodians each with about 10GB of M365 data typically requires several weeks to complete with MS Purview Premium. This is a non-starter for legal teams who are up against pressing litigation timelines.

It is important to understand that because M365 is built on a large-scale multi-tenancy SaaS architecture, such challenges are a feature, not a bug of the system. Multi-tenancy is an architecture where shared computing resources are apportioned across large numbers of users. This architecture enables Microsoft to provide the service at a lower cost since computing services are shared.

However, multi-tenant architecture enables scale (in terms of multitudes of users) and efficiency through uniformity. These architectures are not designed for outlier workloads like eDiscovery that routinely require intensive surges in computing resources to collect, process and search terabytes of data. In fact, multi-tenancy cloud architects would identify eDiscovery workloads as a “noisy neighbor” that threatens the overall performance and user experience of the system, and thus must be managed through quality-of-service mechanisms like throttling and time-outs.

I think of multi-tenant architectures like the business model utilized by a gym. The gym has more and better equipment than I have at home, which is attractive so many will join through a membership. The gym has a fixed amount of square footage and equipment which is more than any individual needs and is sufficient to support those that show up, occasionally having to coordinate access to the equipment but manageable. However, what if a small group showed up at the gym every day for most of the day and hogged the equipment? What if more people showed up, became frustrated, and dissatisfied? Gym management would be forced to act to ensure fair access to the equipment.

Throughout my career as an eDiscovery service provider, we made large investments in infrastructure and capacity to the point of overkill to equip ourselves to service a client’s need to address high volumes of data in short timelines without impacting their business-as-usual activities. We were like the fire department for big unstructured data needs.

A huge differentiator in X1’s approach is to divide and conquer large scale projects by leveraging the cumulative power of a decentralized computing orchestrated through a unified management, search, and collection console. Think of this like deploying a fire suppression system proactively before the fire.

Last year, X1 introduced M365 data connectors into our X1 Enterprise platform to satisfy a critical need for enterprises to conduct cost-efficient yet highly scalable eDiscovery search and collection of M365 data. The response has been tremendous, with X1 seeing record demand in large part, due to the architectural limitations and deficiencies noted above.

X1 Enterprise Collect provides users the unique ability to index and search M365 data in-place and then collect in a targeted and iterative manner. This at speeds and throughput far exceeding other tools, including Microsoft Purview Premium. X1 achieves such scalability through a decentralized custodian-based approach that does not rely on the M365 or Purview search Index, which has known issues with the number of file types supported, consistency of search results, and throughput. X1’s approach enables a very scalable, defensible, and robust data collection at speeds far exceeding that of M365 Purview and other approaches.

For a demonstration of the X1 Enterprise Collect Platform, contact us at sales@x1.com. For more details on this innovative solution, please visit www.x1.com/solutions/x1-enterprise-platform.

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April 23, 2024 · 10:04 AM

Index-In-Place eDiscovery Tech is in High Demand, but Beware of False Vendor Claims

By John Patzakis

Proportionality-based eDiscovery is a goal that all in-house corporate legal teams 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. However, most core eDiscovery costs (outside of attorney review) stem from over-collection of electronically stored information (ESI), and over-collection thwarts the ability to attain proportionality. Law firm Nelson Mullins notes that “over preservation tends to have its own costs relating to storage of large amounts of electronically stored information (ESI) and the resources needed to manage it; leads to increased downstream e-discovery costs associated with collection, processing, and review.”

This is why accurate pre-collection data insight is a game-changing capability that enables counsel to set reasonable discovery limits and ultimately process, host, review and produce much less ESI. Counsel can further use pre-collection proportionality analysis to gather key information, develop a litigation budget, and better manage litigation deadlines. Such insights can also foster cooperation by informing the parties early in the process about where relevant ESI is located, and what keywords and other search parameters can identify and pinpoint relevant ESI.

And the means to enable this capability is distributed index and search in-place technology. Indexing and search in-place in this context means that a software-based indexing technology is deployed directly onto fileservers, laptops, or in the cloud to address cloud-based data sources. This indexing occurs without a bulk transfer of the data to a central location. Once indexed, the searches are performed in a few seconds, with complex Boolean operators, metadata filters and regular expression searches. The searches can be iterated and repeated without limitation, which is critical for large data sets.

However, with this capability being highly valued, many vendors have parroted this messaging, but have offerings that do not qualify as true index-in-place. True distributed index-in-place means that the search indexes are forward-deployed, and are actually installed on the target laptop, Mac computer, fileserver or into the cloud near where the target cloud data sources exist. Transferring data in bulk to a central appliance or server farm via a collector agent or Robocopy function does not qualify. A true index-in-place capability uniquely enables scalability, targeted collection and also minimizes security and data governance risks in eDiscovery and information governance matters.

Conversely, a process requiring massive data copying, migration and centralization does not scale and creates significant data, governance and privacy issues by needlessly duplicating data. For instance, if a matter requires that 10 terabytes be scanned to determine if relevant ESI exists within that data corpus, and the eDiscovery collection platform being used has no index-in-place capability, then all 10 terabytes must be copied and transferred to the tool for indexing and analysis. These limitations stem from tool vendors simply utilizing open source indexing platforms like Lucene or Elastic Search that are not forward-deployable and must reside in centralized locations with a very large amount of computing resources to make them viable for the type of data and data volumes typically seen in discovery and information governance matters.

This is why X1 leverages proprietary and patented index and search technology that is readily forward deployable and thus can scale and allow true distributed indexing in-place. X1 Enterprise Collect significantly streamlines the eDiscovery workflow with integrated culling and deduplication, thereby eliminating the need for expensive and cumbersome ESI processing tools. That way, the ESI can be populated straight into Relativity from an X1 collection without multiple hand offs, extensive project management and inefficient data processing.

The ability to directly and transparently collect data from custodian laptops, desktops, Microsoft 365 and other cloud sources into a RelativityOne/Relativity workspace is a game-changer that enables attorneys to begin review in hours rather than weeks.

For a demonstration of the X1 Enterprise Collect Platform, contact us at sales@x1.com. For more details on this innovative solution, please visit www.x1.com/x1-enterprise-collect-platform.

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February 27, 2024 · 11:38 AM

Modern, Targeted ESI Collection Can Cut eDiscovery Costs by Over 90 Percent

By John Patzakis and Chas Meier

eDiscovery can be an expensive and time-consuming process when traditional data collection methods are employed. With legacy processes, it can take weeks for electronically stored information (ESI) collections to finally end up in review. Time is money, and utilizing dated processes can dramatically increase costs as well as risk. One of the biggest drivers of excessive eDiscovery costs is over-collection of irrelevant or unnecessary information. This in turn leads to a larger amount of data entering the processing and initial review funnel.

In fact, old school manual collection efforts require employing multiple tools, data copies, and manual steps into your litigation 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. Some ESI processing solutions require deployment of non-integrated on-premise hardware appliances that further increase costs and add time delays. Manual collections, multiple generations of data duplication, and disjointed handoffs exponentially increase costs and risk while significantly delaying documents being available for review.

If you are still using stand-alone processing tools, you are doing it wrong and subjecting your clients to extensive costs and time delays. Modern collection technologies combine targeted collection with in-place processing of data which is automatically collected, processed, and uploaded into a review platform such as Relativity in one fell swoop.

The graph below established that the cost for collection, processing and first month hosting under a traditional preservation process can be upwards of $12,000 per custodian:

Properly targeted preservation initiatives are favored by the courts for purposes of civil eDiscovery and enabled by next generation software to search data sources quickly and effectively in-place throughout the enterprise. The value of targeted and proportional preservation is recognized in the Committee Notes to the recent FRCP amendments, which urge the parties to reach agreement on the preservation of data, keywords, and other metadata to identify responsive materials. See also, In re Genetically Modified Rice Litigation, (“Preservation efforts can become unduly burdensome and unreasonably costly unless those efforts are targeted to those documents reasonably likely to be relevant or lead to the discovery of relevant evidence.”)

X1 Enterprise Collect significantly streamlines the eDiscovery workflow with integrated culling and deduplication, thereby eliminating the need for expensive and cumbersome ESI processing tools. That way, the ESI can be populated straight into Relativity from an X1 collection without multiple hand offs, extensive project management and inefficient data processing.

The ability to directly and transparently collect data from custodian laptops, desktops, Microsoft 365 and other cloud sources into a RelativityOne / Relativity workspace is a game-changer that enables Attorney’s to begin review in hours rather than weeks.

The second chart shows how this streamlined approach, based upon a detailed ROI analysis, reduces eDiscovery costs by over 90 percent:

So, in terms of the big picture, X1 Enterprise Collect provides a complete platform to implement a properly targeted preservation strategy in the enterprise enabling organizations to 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 and inefficient processing, you gain much better early insight into your data and can increase efficiencies on many levels.

Finally, the calculations represented in the charts were generated from a customizable ROI cost calculator created by Chas Meier, based upon his more than 20 years’ experience in eDiscovery service provider roles. If you would like a copy of this ROI calculator, please contact Chas at CMeier@x1.com.

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June 13, 2023 · 10:14 AM

Law Firms Are Demanding Native, Post-Level Collection of Social Media Evidence to Support Standard Review and Analytics Workflows

By John Patzakis

Two months ago, X1 launched version 7 of our X1 Social Discovery social media and web collections solution. This major upgrade features a new cutting-edge Instagram connector, along with our revamped Facebook support, where all our users now have native post-level collection and parsing to ensure accuracy, key metadata collection, court authentication, and evidentiary completeness of the collected data. As stated by X1 CEO Larry Gill: “Nothing short of those capabilities is acceptable and that is why I can confidently say that X1 Social Discovery is the only solution in the industry capable of delivering all those requirements.”

Version 7 has been a huge success, with record sales seen since its release. In fact, in speaking recently with several attorneys at top law firms, it’s very clear both how important such native post-level collection is in order to support standard attorney review and data analytics workflows, and that there is a disconnect with some practitioners who are utilizing alternative web plug-in based screen shot generation tools that do not support these now basic but essential requirements.

It is very important to understand the unique ability of X1 Social Discovery to perform post-level native collection of each social media post. This means that the social media post is collected in its native format and parsed so that it has its own associated metadata, photos, indexed text and in-line comments. This allows for the individual Facebook or Instagram posts, Tweets, etc. to be searched, filtered, tagged, and exported individually at the post-level with all associated metadata and comments inline. The ability with X1 to tag, sort, authenticate (with individually associated hash values), and upload individual posts by up to thousands at a time without manual processing into an attorney review platform is essential for an efficient and scalable workflow. And X1 is the only solution that can deliver this critical functionality.

In contrast, the web plug-in tools generate flat file image PDFs as its final output. So, if there are 30 posts that are in a feed, a single monolithic image PDF will be generated as the collection output. These bulk screenshots are of very limited value as they are not collected in native format, retain no post-level metadata, and are not searchable (absent a secondary and inferior OCR process). This output is practically useless for a review platform (as the entire output is one object).

Additionally, as we all know, AI-based analytics is a game-changing capability that is now a standard feature of the leading eDiscovery review platforms. However, ESI must be properly collected in order to support such AI. Semantic AI engines ingest and analyze the extracted text and metadata of individual emails, electronic documents, social media posts and web pages. Only X1 Social Discovery collects full text & post-level metadata and performs and maintains the collection at the individual object-level, all of which is necessary to support upstream AI analysis. Using the web plug-in tools is a decision to not use AI on your case for social media and website evidence.

Another key differentiator is that X1 Social Discovery is a robust software application that can be installed on a computer, including virtual cloud servers. This enables X1 to take advantage of the full CPU and RAM resources available to address heavy workloads as needed with multi-threading capabilities. As a result, X1 can run multiple collections at the same time, auto-expand and auto-scroll across thousands of posts, and collect posts with thousands of comments. For instance, a web crawl collection, a Facebook collection, and a YouTube collection can be run at the same time. Data can be reviewed and analyzed immediately as it populates.

Further, social media collections often involve thousands of posts with hundreds of associated comments and multiple images. This requires adequate resources to perform auto-expanding and auto-scrolling of pages, comments, and images. X1’s architecture provides for such a scalable workflow.

In contrast, the web plug-in tools are web browser plug-ins that face very limiting performance ceilings and functionality constraints inherent to any such plug-in. Many users report limitations beyond capturing a few dozen posts at a time at best, with an inability to capture all comments when they number in the hundreds or numerous clustered images in thumbnail format. For this reason and many others, the web plug-in tools are generally considered a low market tool for one-off small collections.

Additionally, the legal evidentiary defensibility provided by X1 Social Discovery is in a class by itself. This is because X1 captures over a dozen unique item-level metadata fields, including the URL of the individual post (not bulk feed), User ID, Post ID, date stamps and other key metadata. Importantly, each post, which is an individual object, has its own hash value generated at the time of collection. So, when thousands of individual posts, YouTube videos, and web pages are collected into a case, and there is then a need to provide a subset of those items into a review platform or produced in discovery, the hash value and post-level metadata for each item remains intact.

The web plug-in tools only record general URLs of the bulk collection, a hash of the entire “all or nothing” bulk PDF export containing up to dozens of individual posts, with no individual hash values or metadata fields specific to any of those posts. There is an important distinction here between object-level metadata (i.e. each post) and metadata associated with the bulk feed image. If the web plug-in tools capture 50 posts in a single image feed, and the client may only want 4 of them moved into a review platform for further review, there is no way to do that except for taking secondary Snagit-type screenshots, rendering the chain of custody broken and the hash value inapplicable.

Finally, the court in Edwards v. Junior State of America Foundation, No. 4:19-CV-140-SDJ, (US Dist. CT, E.D. TX 2021), specifically ruled that only HTML or JSON social media collections are defined as “native file collection.” Flat PDFs are not.

To see this critical and unique functionality in X1 Social Discovery v7, please watch our product tour on-demand. Alternatively, we would welcome the opportunity to brief you and your colleagues directly. Please contact us to speak with a member of the X1 Team.

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November 1, 2022 · 11:42 AM

Proportionality in eDiscovery is Ideal, but Difficult to Realize Without an Optimized Process

By John Patzakis

(Originally published October 24, 2022 by JD Supra and EDRM)

Image: Kaylee Walstad, EDRM

Proportionality-based eDiscovery is a goal that all corporate litigants seek 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. Litigants that take full advantage of the proportionality rule can greatly reduce cost, time and risk associated with otherwise inefficient eDiscovery.

While there is a keen awareness of proportionality in the legal community, realizing 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 incurs extensive labor 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.

However, traditional eDiscovery services typically involve manual collection, followed by manual on-premises hardware-based processing, and finally manual upload to review. These inefficiencies extend projects by often weeks while dramatically increasing cost and risk with purposeful data over-collection and numerous manual data handoffs. The good news is that solutions and processes addressing the first half of the EDRM involving collection and processing are now far more automated than they were even a few years ago.

Recently EDRM hosted a webinar addressing these issues – “Operationalizing your eDiscovery Process to Realize Proportionality Benefits” – and more specifically, as the title reflects, explored how to operationalize your eDiscovery process to achieve lower costs, improve early case strategy, realize faster time to review and reduce overall legal risk.

Here are some key takeaways from the webinar:

  • A detailed legal analysis was provided highlighting the case of Raine Group v. Reign Capital, (S.D.N.Y. Feb. 22, 2022), which applied proportionality at the point of identification and collection, not just production. The court endorsed the use of detailed and iterative keyword searches to identify and preserve potentially relevant ESI.
  • A demonstration was shown on how to enable detailed and proportional search criteria, applied in-place, at the point of collection. Such a capability is key to realizing the blueprint for targeted and proportional ESI collection outlined in Raine Group.
  • The speakers also discussed how organizations should move upstream to focus on information governance to reduce the data funnel as soon as possible. The new generation of eDiscovery technology in the areas of collection, identification, analytics, and early data assessment, enables enterprises to operationalize proportionality principles.

The webinar culminated with the notion that an optimized process that applies proportionality upstream at the collection and identification stage reduces the data volume funnel by as much as 98 percent from over-collection models, yet with increased transparency and compliance. A link to the recording from the webinar can also be accessed here.

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