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Relativity Product Team Highlights Compelling X1 Integration for ESI Collection

By John Patzakis

Recently we hosted a webinar with Relativity highlighting the very compelling integration of our X1 Distributed Discovery platform with the 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.”

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The webinar featured a live demonstration showing X1 quickly collecting data across multiple custodians and seamlessly importing that data into RelativityOne in less than two minutes. Relativity Collect currently supports Office 365 and Slack sources, and this X1 integration will now enable Relativity Collect to also reach emails and files on laptops and file servers. Relativity Senior Product Manager Barry O’Melia commented that the integration with X1 will “greatly streamline eDiscovery process by collapsing the many hand-offs built into current EDRM workflows to provide greater speed and defensibility.”

ComplianceDS President Marc Zamsky, a customer of both X1 and Relativity, recently commented that the “ability to collect directly from custodian laptops and desktops into a RelativityOne workspace without impacting custodians is a game-changer,” which will “reduce collection times from weeks to hours so that attorneys can quickly begin reviewing and analyzing ESI in RelativityOne.”

The live demonstration performed by O’Melia 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 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.

With the ability to search and collect emails and documents across up to thousands of endpoints and network sources with industry-leading speed, X1 Distributed Discovery revolutionizes enterprise eDiscovery. For example, X1 empowers legal and consulting teams to iterate their search parameters in real time before collection, providing a revolutionary true pre-collection early case assessment capability. Additionally, with its intelligent collection capability, X1 performs instantaneous data processing (culling, de-duplication, text and metadata extraction, etc) in a fully automated manner.

And with the integration with Relativity, the X1 platform is even more compelling. As Marc Zamsky exclaimed “My clients are going to love this!”

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Filed under collection, eDiscovery, Preservation & Collection, SaaS, Uncategorized

How Case Teams Can Streamline Collections with X1 in RelativityOne

Editor’s’ Note: This article originally appeared on The Relativity Blog. It is reprinted here in full with permission. 

by Sam Bock on November 07, 2019

Our September 2019 release for RelativityOne debuted some game-changing functionality in the platform. Collect for RelativityOne enables fast, secure, and defensible collections right within the cloud, allowing RelativityOne users to pull data directly from Microsoft Office 365 without ever leaving the platform or Azure.

One of our developer partners—X1joined up with us on building this functionality, bringing their patented technology into Collect to help simplify traditionally complex workflows.

To get a better picture of just what Collect and X1 Distributed Discovery are capable of now that they’ve teamed up, we sat down with X1 Executive Chairman and Chief Legal Officer John Patzakis. Check out the most impactful takeaways from our conversation, and sign up for X1’s upcoming webinar to learn more.

Sam: What makes collection challenging for today’s legal teams?

John: Traditional e-discovery collection methods consist of either unsupervised custodian self-collection or manual services, driving up costs while increasing risk and disruption to business operations. On the other end of the spectrum, endpoint forensic imaging is burdensome, expensive, and not legally required for civil litigation discovery. Additionally, these manual and disjointed efforts are not technically integrated with Relativity, thus requiring multiple hand-offs, which increases risk, expense, and cumbersome project management efforts.

How does your team think creatively to tackle those challenges in the interest of conducting faster, more defensible collections for your customers?

We tackle collection from the enterprise and also enable significant scalability. X1 Distributed Discovery enables enterprises and their service providers to search, assess, and analyze electronically stored information (ESI) across hundreds or even thousands of custodians, enterprise-wide, where the data resides and before collection, with direct upload into Relativity. Instead of the expensive and disruptive “image then stage then process then load into review workspace” process, X1 Distributed Discovery allows for access to ESI where it sits within hours.

What sorts of variables exist in today’s collection workflows, and how does your team accommodate for those differences?

One of the biggest challenges with modern enterprise ESI collection comes from remote employees who only log into the network intermittently. Most network-enabled collection tools require custodians to be on the domain in order to work. However, X1 is architected to feature SSL security certificates—creating secure tunnels that enable collection from custodians wherever they are, including on WiFi in a Starbucks or on a plane.

Another key challenge is email collection. Traditional workflows often require collecting an entire PST email container or Exchange email account back to a central location for processing, identification, and preservation of potentially responsive email messages. This approach involves the transferring and processing of large files, which takes a lot of time, before even beginning to identify individually responsive email messages. Our solution eliminates the need to transfer entire email containers by allowing the identification and collection of individual messages in place on a custodian’s computer.

How is Collect for RelativityOne built to manage modern collections more effectively?

Collect integrates the X1 Distributed Discovery architecture to leverage patented search technology that indexes Microsoft Office 365 data directly on the laptop, desktop, or file server, allowing e-discovery, investigatory, or forensic professionals to globally query thousands of individual endpoints simultaneously. Individual emails and files can be identified by keyword, dates, and other metadata content without having to first retrieve the entire PST or ZIP across the network.

Collecting enterprise ESI can be one of the most daunting parts of the e-discovery process, and X1’s technical integration with RelativityOne seeks to make it less intimidating. The software helps streamline the e-discovery workflow by eliminating expensive and cumbersome processing steps and dramatically increasing speed to review. Collect for RelativityOne provides legal teams with a solution that compresses project timeframes; reduces risk by integrating collection with the rest of Relativity’s suite of features for review and analysis; and creating a repeatable process that helps reduce overall efforts and costs that might otherwise be spent outside of the platform. Additionally, the tight integration between X1’s technology and Relativity provides a unified chain of custody for optimal defensibility.

In short, we’re excited to see how this functionality, built into Relativity’s collection tool, can help revolutionize the current e-discovery process by collapsing the many hand-offs involved in the EDRM into a few short steps manageable by one or two people.

What tips and best practices would you share with a team conducting complex collections? How can they set themselves up for success from the start?

When collecting data, plan your collection criteria carefully. Focus on granular search criteria including file types, data ranges, and other key metadata in addition to detailed Boolean search terms to help your team strategically reduce collection volumes.

Sam Bock is a member of the marketing team at Relativity, and serves as editor of The Relativity Blog.

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Filed under collection, Corporations, eDiscovery, Enterprise eDiscovery, Uncategorized

In-Place Data Analytics For Unstructured Data is No Longer Science Fiction

By John Patzakis

AI-driven analytics supercharges compliance investigations, data security, privacy audits and eDiscovery document review.  AI machine learning employs mathematical models to assess enormous datasets and “learn” from feedback and exposure to gain deep insights into key information. This enables the identification of discrete and hidden patterns in millions of emails and other electronic files to categorize and cluster documents by concepts, content, or topic. This process goes beyond keyword searching to identify anomalies, internal threats, or other indicators of relevant behavior. The enormous volume and scope of corporate data being generated has created numerous opportunities for investigators seeking deep information insights in support of internal compliance, civil litigation and regulatory matters.

The most effective use of AI in investigations couple continuous active learning technology with concept clustering to discover the most relevant data in documents, emails, text and other sources.  As AI continues to learn and improve over time, the benefits of an effectively implemented approach will also increase. In-house and outside counsel and compliance teams are now relying on AI technology in response to government investigations, but also increasingly to identify risks before they escalate to that stage.

Stock Photo - Digital Image used in blog

However, logistical and cost barriers have traditionally stymied organizations from taking advantage of AI in a systematic and proactive basis, especially regarding unstructured data, which, according to industry studies, constitutes 80 percent or more of all data (and data risk) in the enterprise. As analytics engines ingest the text from documents and emails, the extracted text must be “mined” from their native originals. And the natives must first be collected and migrated to a centralized processing appliance. This arduous process is expensive and time consuming, particularly in the case of unstructured data, which must be collected from the “wild” and then migrated to a central location, creating a stand-alone “data lake.”

Due to these limitations, otherwise effective AI capabilities are utilized typically only on very large matters on a reactive basis that limits its benefits to the investigation at hand and the information within the captive data lake.  Thus, ongoing active learning is not generally applied across multiple matters or utilized proactively. And because that captive information consists of migrated copies of the originals, there is a very limited ability to act on data insights as the original data remains in its actual location in the enterprise.

So the ideal architecture for the enterprise would be to move the data analytics “upstream” where all the unstructured data resides, which would not only save up to millions per year in investigation, data audit and eDiscovery costs, but would enable proactive utilization for compliance auditing, security and policy breaches and internal fraud detection.  However, analytics engines require considerable computing resources, with the leading AI solutions typically necessitating tens of thousands of dollars’ worth of high end hardware for a single server instance. So these computing workloads simply cannot be forward deployed to laptops and multiple file servers, where the bulk of unstructured data and associated enterprise risk exists.

But an alternative architecture solves this problem. A process that extracts text from unstructured, distributed data in place, and systematically sends that data at a massive scale to the analytics platform, with the associated metadata and global unique identifiers for each item.  As mentioned, one of the many challenges with traditional workflows is the massive data transfer associated with ongoing data migration of electronic files and emails, the latter of which must be sent in whole containers such as PST files. This process alone can take weeks, choke network bandwidth and is highly disruptive to operations. However, the load associated with text/metadata only is less than 1 percent of the full native item. So the possibilities here are very compelling. This architecture enables very scalable and proactive compliance, information security, and information governance use cases. The upload to AI engines would take hours instead of weeks, enabling continual machine learning to improve processes and accuracy over time and enable immediate action to taken on identified threats or otherwise relevant information.

The only solution that we are aware of that fulfills this vision is X1 Distributed GRC. X1’s unique distributed architecture upends the traditional collection process by indexing at the distributed endpoints, enabling direct pipeline of extracted text to the analytics platform. This innovative technology and workflow results in far faster and more precise collections and a more informed strategy in any matter.

Deployed at each end point or centrally in virtualized environments, X1 Enterprise allows practitioners to query many thousands of devices simultaneously, utilize analytics before collecting and process while collecting directly into myriad different review and analytics applications like RelativityOne and Brainspace. X1 Enterprise empowers corporate eDiscovery, compliance, investigative, cybersecurity and privacy staff with the ability to find, analyze, collect and/or delete virtually any piece of unstructured user data wherever it resides instantly and iteratively, all in a legally defensible fashion.

X1 displayed these powerful capabilities with ComplianceDS in a recent webinar with a brief but substantive demo of our X1 Distributed GRC solution, emphasizing our innovative support of analytics engines through our game-changing ability to extract text in place with direct feed into AI solutions.

Here is a link to the recording with a direct link to the 5 minute demo portion.

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Filed under Best Practices, collection, compliance, Corporations, eDiscovery & Compliance, Enterprise eDiscovery, Enterprise Search, GDPR, Uncategorized