X1 Distributed Discovery & RelativityOne Integration: Testing and Proof of Concept

By John Patzakis
October 10, 2018

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 Distributed Discovery.  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 Distributed Discovery, 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 Distributed Discovery, 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 Level hosting 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 Distributed Discovery 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 Distributed Discovery: 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 Distributed Discovery 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 Distributed Discovery 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 Distributed Discovery 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 Distributed Discovery 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 Distributed Discovery 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.