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Beyond Document Review: The Discoverability of Nontraditional Sources

By Katie Askey and Colleen M. Yushchak – May 19, 2015

Litigators are already intimately familiar with the identification, collection, review, and production of unstructured data such as emails, documents, and paper. More recently, they are faced with the task of collecting and producing data from formats such as financial or accounting systems, transactional databases, chat, voicemail, or even social media. While there are many tools on the market designed to efficiently and defensibly review and produce documents, few address this new frontier.


What Is Nontraditional Data?
To understand what “nontraditional” data means, it is important to first understand how traditional data is defined. In the context of e-discovery, traditional data refers to unstructured data that is not organized in a predefined manner or free-form text documents that are typically user-created. Examples include email, Microsoft Office files, or paper documents scanned and converted to images.


Nontraditional data is essentially anything that does not fit into this mold. This includes both structured and semi-structured data sources. Structured data is information that is stored in relational database structures. For example, a human resources database might include tables with personal information like names, addresses, job titles, and salaries. While this data resides in different physical tables, all of the information can be easily linked back to one unique record (in this case, the employee), thus making it relational and structured.

Semi-structured data consists of everything that lies between unstructured and structured data. The distinction between the three data types is not always clear, but generally speaking, semi-structured data does not contain the same links between tables that relational databases have. However, it is typically organized into rows and columns. Examples of semi-structured data include social media, mobile device data, voicemails, and interoffice chat files.


Semi-structured and structured data are likely more prevalent in your daily life than you realize. Your TV is recording every show you watch and your GPS-enabled smartphone literally records your every move. The volume and variety of these sources will only continue to grow as new technologies emerge.


Why Is It Important to Capture This Data in a Meaningful Way?
Traditional data sources only illuminate part of the big picture. Through a standard email review, you may be able to ascertain what someone knew and when they knew it. However, this is only the start of the story. Capturing nontraditional data is important because it has the potential to complete the picture with proven facts, pinpoint the smoking gun, and reduce the risk of being surprised by previously unknown data.


Coupled with unstructured data, transactional data found in operational, financial, accounting, and human resources systems can strengthen a legal argument through supporting facts. Data gathered from these sources can prove that an event occurred, explain how it happened, quantify the impact, and point toward a mitigation plan if needed.

As technological advancements are made, people are getting smarter. What does someone do when he or she wants to communicate without leaving a paper trail? Pick up the phone or send a text message. By ignoring these nontraditional sources, vital information can easily be missed.


In the litigation process, the last thing an attorney wants is to be caught in a position of not having all the facts. Disregarding the nontraditional sources discussed in this article leaves your client vulnerable in the event that a competitor collects and uses this data.


Best Practices
The importance of using unstructured data sources in the litigation process is evident, but what is the best way to handle these sources? Transactional databases, audio, chats, and social media are all quite different from emails and documents. Each source requires additional considerations to ensure an efficient and effective collection and review.

In the past, the electronic discovery reference model (EDRM) has been used to provide guidance on the various stages of the e-discovery process. The EDRM provides a conceptual view of the e-discovery process from information governance and identification all the way through production and presentation. While very helpful for outlining the process, the EDRM does not give an indication on how to approach nontraditional data sources. While a great starting point for addressing these sources, the EDRM must change with the times in order to remain effective.


As electronically stored information (ESI) and clients’ needs have evolved, so too must the way we approach the EDRM. Given the competitive landscape of the e-discovery industry, clients often demand customized solutions that support new use cases, performance improvements, and efficiencies. In the future, successful e-discovery solutions will need to provide a combination of both traditional and customized solutions to address these new data sources. In some cases, such as with mortgage-backed securities litigation and anti-money laundering events, using traditional e-discovery platforms that have been tweaked through customization may be effective. In other cases, such as with audio and social media data, an innovative customized solution will be required.


Keywords: litigation, minorities, e-discovery, nontraditional data, unstructured data, semi-structured data, structured data, electronic discovery reference model


Katie Askey is an associate director in Indianapolis, Indiana, and Colleen M. Yushchak is a director in Washington, D.C., with Navigant Consulting, Inc.


Navigant Consulting is a corporate sponsor of the Section of Litigation. Neither the ABA nor ABA entities endorse non-ABA products or services. This article should not be construed as an endorsement.

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