Category Archives: eDiscovery

New York Appellate Court Allows “Data Mining” of Social Media accounts for Relevant Information

By John Patzakis

The New York Appellate Division allowed discovery into the non-public information of the social media accounts of a former professional basketball player relevant to his personal injury claims arising out of an automobile accident. In Vasquez-Santos v. Mathew 2019 NY Slip Op 00541 (January 24, 2019), the court held that the defendant may utilize the services of a “data mining” company for a widespread search of the plaintiff’s devices, email accounts, and social media.social-media-cases3

Vasquez-Santos is an extension of a large body of court decisions that allow discovery of a user’s “private” social media messages, posts and photos where that information is reasonably calculated to contain evidence material and necessary to the litigation. Private social media information can be discoverable to the extent it “contradicts or conflicts with [a] plaintiff’s alleged restrictions, disabilities, and losses, and other claims” according the Vasquez-Santos Court.

The Court found that the defendant “is entitled to discovery to….defend against plaintiff’s claims of injury,” and noted that the requested access to plaintiff’s accounts and devices “was appropriately limited in time, i.e., only those items posted or sent after the accident, and in subject matter, i.e., those items discussing or showing defendant engaging in basketball or other similar physical activities.”

Also noteworthy was the Court’s finding that while plaintiff did not take the pictures himself, that was of no import to the decision. He was “tagged,” thus allowing him access to the pictures, and thus populated his social media account.

This decision is consistent with the general rule that while social media is clearly discoverable, there must be a requisite showing of relevance before the court moves to compel full production of a litigant’s “private” social media.

This case illustrates that any solution purporting to support eDiscovery for social media must have robust public search and collection capabilities. This means more than merely one-off screen scrapes but instead an ability to search, identify and capture up to thousands of social media posts on an automated and scalable basis.

X1 Social Discovery has the ability to find an individual’s publicly available content and to collect it in an automated fashion in native format with all available metadata intact to enable systematic and scalable search, review, tagging and analysis. We heard from one major law firm that screen captures of a single public Facebook account took several hours, with the resulting images not searchable or organized into a case-centric workflow. Now with X1 Social Discovery, they are able to accomplish this full capture in seconds. This is critically important to conduct proper due diligence on a case and to better assist legal and investigative professionals to make the requisite showings for the full discovery of social media evidence in civil discovery, as in Vasquez-Santos.

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Filed under Best Practices, Case Law, Case Study, eDiscovery, law firm, Social Media Investigations

eDiscovery Collection 3.0: Much Better, Much Faster, Much Cheaper

In his recent blog post, X1 CEO Craig Carpenter discussed the inability of any software provider to solve a critical need by delivering a truly scalable eDiscovery preservation and collection solution. As Craig pointed out, in the absence of such a “holy grail” solution, eDiscovery collection remains dominated by either unsupervised custodian self-collection or manual services, driving up costs while increasing risk and disruption to business operations.

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Craig outlined how endpoint forensic imaging are still employed on a limited basis. Many companies have also tried network crawling methods with repurposed forensic tools. (A “collection 2.1” method, if you will).  While this can be feasible for a small number of custodians, network bandwidth constraints coupled with the requirement to migrate all endpoint data back to the forensic crawling tool renders the approach ineffective. For example, to search a custodian’s laptop with 10 gigabytes of email and documents, all 10 gigabytes must be copied and transmitted over the network, where it is then searched, all of which takes at least several hours per computer. So, most organizations choose to force collect all 10 gigabytes. The case of U.S. ex rel. McBride v. Halliburton Co.  272 F.R.D. 235 (2011), illustrates this specific pain point well. In McBride, Magistrate Judge John Facciola’s instructive opinion outlines Halliburton’s eDiscovery struggles to collect and process data from remote locations:

“Since the defendants employ persons overseas, this data collection may have to be shipped to the United States, or sent by network connections with finite capacity, which may require several days just to copy and transmit the data from a single custodian . . . (Halliburton) estimates that each custodian averages 15–20 gigabytes of data, and collection can take two to ten days per custodian. The data must then be processed to be rendered searchable by the review tool being used, a process that can overwhelm the computer’s capacity and require that the data be processed by batch, as opposed to all at once.”

Halliburton represented to the court that they spent hundreds of thousands of dollars on eDiscovery for only a few dozen remotely located custodians. The need to force-collect the remote custodians’ entire set of data and then sort it out through the expensive eDiscovery processing phase, instead of culling, filtering and searching the data at the point of collection drove up the costs. As such, this network crawling based architecture is fundamentally flawed and cannot scale.

What is needed is the ability to gain immediate visibility into unstructured distributed data across the enterprise, through the ability to search and collect across several hundred endpoints and other unstructured data sources such as file shares, and return results within minutes instead of days or weeks. The approaches outlined above and by Craig Carpenter do not come close to meeting this requirement and in fact actually perpetuate eDiscovery pain.

Solving this collection challenge once and for all is basis for X1 Insight and Collection, which is our eDiscovery collection 3.0 solution.  X1 Insight and Collection (XIC) enables enterprises to quickly and easily search across up to thousands of distributed endpoints and data servers from a central location.  Legal and compliance teams can easily perform unified complex searches across both unstructured content and metadata, obtaining statistical insight into the data in minutes, and full results with completed collection in hours, instead of days or weeks. Built on our award-winning and patented X1 Search technology, XIC is the first product to offer true and massively scalable distributed data discovery across an organization. XIC replaces expensive, cumbersome and highly disruptive approaches to meet enterprise discovery, preservation, and collection needs.

Targeted and iterative end point search is a quantum leap in early data assessment, which is critical to legal counsel at the outset of any legal matter. However, under today’s industry standard, the legal team is typically kept in the dark for weeks, if not months, as the manual identification and collection process of distributed, unstructured data runs its expensive and inefficient course.  To illustrate the power and capabilities of XIC, imagine being able to perform multiple, detailed, Boolean keyword phrase searches with metadata filters across the targeted end points of your global enterprise. The results start returning in minutes, with granular statistical data about the responsive documents and emails associated with specific custodians or groups of custodians.

Once the legal team is satisfied with a specific search string, after sufficient iteration, the data can then be collected by XIC by simply hitting the “collect” button. The responsive data is “containerized” at each end point and automatically transmitted to either a central location, or uploaded directly to Relativity, using Relativity’s import API where all data is seamlessly ready for review. Importantly, all results are tied back to a specific custodian, with full chain of custody and preservation of all file metadata. Here is a recording of a live public demo with Relativity, showing the very fast direct upload from XIC straight into RelativityOne.

This effort described above — from iterative, distributed search through collection and transmittal straight into Relativity from hundreds of endpoints — can be accomplished in a single day. Using manual consulting services, the same project would require several weeks and hundreds of thousands of dollars in collection costs alone, not to mention significant disruption to business operations. Substantial costs associated with over-collection of data would mount as well, and could even dwarf collection costs through unnecessary attorney review time.

XIC operates on-demand where your data currently resides — on desktops, laptops, servers, or even the cloud — without disruption to business operations and without requiring extensive or complex hardware configurations. Beyond enterprise eDiscovery and investigation functionality, organizations can offer employees the award-winning X1 Search, improving productivity while maintaining compliance.

As Relativity Product Manager Barry O’Melia said in the live X1/R1 integration demo, it is something you have to see for yourself to believe. So please check out the demo here, or contact us to arrange for a private demo.

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X1 Insight and Collection & RelativityOne Integration: Testing and Proof of Concept

Editor’s Note: The following is a blog post published by eDiscovery expert Chad Jones, Director at D4 Discovery, regarding D4’s extensive testing and validation of the integration of R1 and X1 Insight and Collection.  It is republished here with permission. 

Discovery is a complicated business. For a typical litigation, there are at least five separate stages, collection, processing, review, analysis, and production, and while the average discovery period lasts eight to ten months, the matters themselves can run for years. During the lifecycle of a common eDiscovery project, these five stages are usually performed by several different parties, which further complicates the process by introducing a variety of hand-offs and delays between organizations and individuals.

The proof of concept that follows was designed to validate Insight and Collection, a product created by X1 Discovery, Inc, and that now features a direct upload to Relativity and RelativityOne. With this product, X1 proposes to streamline the five-stage process by allowing enterprises to search locally, collect those search hits, process the results and push them directly to RelativityOne in a matter of minutes.

To evaluate the viability of the X1 Insight and Collection, D4, LLC. designed and executed the following Proof of Concept (POC). A leader in forensic collection services and a seven-time Relativity Best in Service, Orange Levelhosting partner, D4 staff leveraged its expertise in end to end eDiscovery to implement the workflow and document the results.

Background

Project

eDiscovery is a multi-stage process with a series of hand-offs between disconnected parties. This process can be extremely expensive and error prone. In addition to the costs, the time to review can often span weeks or even months to complete.

Stakeholders

Those who stand to benefit from X1 Insight and Collection are business and organization leaders looking to manage and control the cost and risks of discovery.

Solution Features and Benefits

There are several features of the X1 Insight and Collection: search-in-place, early case assessment visualizations, remote collection, processing on demand, publish to review in RelativityOne. Searching in place on the local machine has several benefits. It prevents needless over collection and saves the end user from the hassle of turning over her machine and losing productivity. It also gives case teams the opportunity to iterative refine search terms and review search hits on the fly.

Finally, searching in place replaces the need to collect data and load to a master repository for indexing and searching. This includes email containers – the ability to index, search and collect all email in place on the custodian’s computer or the corporate Exchange server without the need to migrate the entire container or full account is a strong and unique capability. With X1’s remote collection, once users target the specific files and emails they need, they can immediately collect and process that information. Once collected and processed, enterprise users have the option of creating standard load files or sending text, metadata and native files directly to RelativityOne.

Practical Details of POC

To test and vet the software, D4 built a mini-cloud environment, consisting of five custodian machines; one enterprise server; and one client server meeting the specs listed below:

Server 1

  • OS: Microsoft Server 2012 R2
  • CPU: 2.6 GHz minimum 8 processors
  • Memory: 16 GB RAM
  • Disk: 180 GB free hard disk space (software)
  • Disk 2: 1TB for collected data (or available network drive)

Server 2

  • OS: Microsoft Server 2012 R2
  • CPU: 2.6 GHz minimum 8 processors
  • Memory: 32 GB RAM
  • Disk: 180 GB free hard disk space (software)

Testing Desktop: (QTY 5)

  • OS: Microsoft Windows 7, 8 or 10
  • CPU: 1.8 GHz minimum 2 processors
  • Memory: 8 GB RAM

On each custodian machine we placed a mix of email and non-email data. From these data sets we ran a series of tests from which we collected data.

Although X1 Insight and Collection provides a variety of workflows allowing for a complex collection strategy, for the purposes of this proof-of concept, the collection was limited to a simple Boolean query of common football related terms across Enron data. We made two separate collections of email data: a collection to disc with load files and a collection direct pushed to RelativityOne. The terms used in the POC were: “football OR game OR trade OR QB OR league OR cowboys OR longhorns OR thanksgiving OR player.” Following the collections, the results of the load file export were test loaded to Relativity and the results of the dataset published direct to RelativityOne were evaluated in that workspace.

Test Results

The testing process considered four main areas: documenting search results; documenting upload/download times; metadata validation; and reports and exception handling. To test the search results the loaded data was indexed, and searches run to confirm the results. In both load formats, the search results remained the same as shown below.

It is important to note that in Relativity only the text was searched while in X1 all metadata was also included in the search. This is a common difference between review platforms and collection tools, as collection tools are able to search all components of the file, while review is limited to extracted metadata fields only.

Additional tests were performed to document search and exports speeds. One of the components of X1 Insight and Collection is its collection module which sits on the client server and manages the collection from a central location. In the initial test, we chose to export the files to disc and create a load file, while in the second test we leveraged X1s integration with RelativityOne and upload data to Relativity’s cloud instance via the Relativity API.

In both cases, the results proved that X1 is incredibly powerful. Each time the system executed saved searches on five separate machines, pulled the data to the client server, extracted text and metadata and then either generated a load file or sent the deliverable straight to the cloud and into Relativity – all within minutes. The results, shown below, are amazing. In both cases the system completed all steps in under 13.5 minutes. Additional tests were performed to document search and exports speeds.

One of the components of X1 Insight and Collection is its collection module which sits on the client server and manages the collection from a central location. In the initial test, we chose to export the files to disc and create a load file, while in the second test we leveraged X1s integration with RelativityOne and upload data to Relativity’s cloud instance via the Relativity API. In both cases, the results proved that X1 is incredibly powerful. Each time the system executed saved searches on five separate machines, pulled the data to the client server, extracted text and metadata and then either generated a load file or sent the deliverable straight to the cloud and into Relativity – all within minutes. The results, shown below, are amazing. In both cases the system completed all steps in under 13.5 minutes.

Further testing showed that while X1 gets the essential metadata components extracted from the data, there are some features we are used to seeing in established eDiscovery processing tools that are lacking in this product. We also found the exception reporting to be lacking. In our RelativityOne tests, we found 40 files were excluded from upload, yet when reviewing the available exception reporting we had trouble seeing what caused those file failures. These issues notwithstanding, the POC proved successful. X1 Insight and Collection proved to be a powerful search engine and collection tool, capable of collecting over 6,000 documents from five separate machines and uploading those files to RelativityOne in less than fifteen minutes!

Conclusion

X1 Insight and Collection offers multiple benefits to the enterprise user looking to take control of the eDiscovery life cycle. By simplifying the course of an eDiscovery project, X1 limits the number of touch points in the traditional vendor-driven process. Internal users can search and vet terms in real-time before collection. This not only mitigates the opportunity for error, but it greatly reduces the time to review, which is what this solution really seems to be all about. X1 seems to have been designed with the internal investigation in mind. Offering a light tagging feature, X1 gives users a light ECA option that with a couple mouse clicks becomes a collection and processing tool that connects directly to all the features of RelativityOne. When combined with Relativity ECA, Analytics and Active Learning, this might be all the solution the typical enterprise would need.

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Why I Joined X1

X1 Logo 559w 288t

Two weeks ago I joined X1 as CEO, a company I am convinced is in the process of disrupting not just the eDiscovery industry, but the regulatory compliance and corporate governance markets as well.  As I discussed at length with the X1 team and board of directors during the interview process, I see in X1 a ton of similarities to Recommind circa 2007 (shortly after I joined), alongside several additional advantages we didn’t have at Recommind back then.  Does this guarantee greatness for years to come for X1?  Absolutely not.  But it gives us the opportunity to control our own destiny which is all a software startup can ask.  Here’s why.

  • X1’s team and culture are strong. I have learned the hard way how important culture is, how it can be instrumental in raising a collective effort to new heights or hold an otherwise successful company back from reaching its potential.  X1 is filled with people who have been here for 5, 7, 10 and even 14 years (here’s looking at you Alan!).  People here just want to win, to help make clients successful.  Our balance sheet and cap table are clean.  Revenue is growing nicely and we are cashflow positive.  Our investors, shareholders and board of directors have reasonable expectations about our plans and timelines (so far, anyway J).  X1ers are actually nice, which is a refreshing throwback coming from what has become a frequently cutthroat, arrogant culture amongst many of Silicon Valley’s largest tech companies and VC community.  We are building something special at X1, and if we execute well with a customer-centric focus at all times, everything else – accolades, continued revenue growth and profitability, financial gain – will take care of itself.

 

  • Making information actionable is really hard. When I worked at AccessData, a few VC friends of mine gave me grief for being at a company named after a problem that had already been solved.  “Accessing” information is indeed easy in most cases; however, making the right information “actionable” is an entirely different endeavor that is extremely difficult without X1 software.  What has changed over the last 10-15 years is the sheer volume and variety of information being created and therefore subject to litigation, regulatory scrutiny and corporate governance mandates.  Our industry-leading X1 Social Discovery product is proof of this, but the variety of today’s information doesn’t stop at social media: think of collaboration tools like Slack, Skype or Teams.  Simply put, people communicate in a far more varied way today than they used to, and making these varied data types available and actionable is hard.  I want to be at a company that is already addressing these challenges for our corporate, government, law enforcement and law firm clients, with ample runway to extend these capabilities, and X1 is exactly that.

 

  • The pressure on companies to find and act upon data is enormous. In the last 2 weeks we have done webinars on finding information on the Dark Web and California’s Consumer Privacy Act (CaCPA).  These topics weren’t on corporate radars – and in the latter case didn’t even exist – as recently as last year.  Add in GDPR, the growing impact of cybersecurity/breaches, migration of information to SaaS platforms and the cloud and the ever-present scrutiny of regulatory authorities globally and companies are struggling to make their information actionable as never before.  And this situation is unlikely to get any simpler or easier in the coming years, as the way we all communicate continues to evolve more quickly every year.

 

I have learned over my career (and life for that matter) that timing is a key part of life.  It’s rarely something we can control, but it has a huge impact on all of us.  X1 has a terrific opportunity to fill key customer needs at the exact time they need it, and has a team committed to customer success that genuinely cares.  I am extremely fortunate to be here at this time and can’t wait to see where we can take the company over the next 5 years and beyond.

– Craig Carpenter

Craig Carpenter 250 sq

 

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eDiscovery Veteran Craig Carpenter Joins X1 as CEO

Today we have some very exciting news: Craig Carpenter has joined X1 as our new CEO. Craig is a seasoned and experienced executive in the eDiscovery and information governance arena, holding several senior executive positions throughout his impressive career, including CEO, EVP of Sales, CMO, COO, and General Counsel.  Craig was an early executive team member of one of the pioneers of eDiscovery software, Recommind, and a key part of that story from startup to creation of an industry-leading brand.  Craig Carpenter 250 sq

Most recently Craig was CEO of Fronteo, which he joined after running sales at Kroll Ontrack. He now brings his proven, customer-centric approach to X1. What I especially like about Craig is his track record as a thought leader and innovator in the legal technology arena. Here is what another thought leader, UK lawyer and renowned eDiscovery expert Chris Dale said last year on his blog, The eDisclosure Project, about Craig:

“Craig Carpenter was personally responsible for much of Recommind’s success in promoting its expertise at predictive coding. This was a tough sell in those days when it was new, and Craig Carpenter tackled the promotional task with vigour. He was also the first person I heard (at a conference in Hong Kong) predict that analytics had a big future role to play in information governance, something which reached fruition when, a long time later, OpenText added Recommind to its stable with precisely that intention in mind.”

I couldn’t agree more. In fact, Craig will be weighing in on very soon with a blog post of his own on why he came to X1, which will include further insight into what Chris Dale says above, so stay tuned. Craig is a central part of X1’s recent growth and expansion. Our X1 eDiscovery and information governance platform has caught fire, fueled in part by our game-changing alliance with Relativity announced earlier this summer. And LegalTech News weighed in today about Craig, noting that “there are few people more equipped to predict the future of e-discovery.” In his interview with Legaltech News, Carpenter said X1’s position aligns well of with his vision of the e-discovery marketplace, where the goal is to “move the strategy formation phase up to the beginning, as opposed to later in the process.”

Besides being a well-known thought leader in eDiscovery, Craig also has forensic technology experience as he served as Chief Marketing Officer and COO of AccessData. Craig began his career as a practicing attorney. He earned both his Juris Doctor and MBA from Santa Clara University (my law school alma mater) and completed his undergraduate studies at UCLA, where he played quarterback on the Bruins’ Pac-10 championship football team. A team, which I might add, could really use his help this season, but I digress.

And speaking of interesting blog posts, look for even more quality content on this site as Craig will be a featured co-blogger going forward. For now, I am very excited to welcome Craig onboard to take the X1 helm!

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