How to Choose an E-Discovery Vendor

How to Choose an E-Discovery Vendor

By David Ferry. He writes from San Francisco (United States) about the law, social issues, and technology.

A payday loan dispute before Delaware’s Court of Chancery went awry when the lender failed to produce critical electronically stored information: the interest rates it charged.

Pressed by the court after numerous twists and turns—including a ruling that the defendant had based a motion for dismissal on a false assertion—the lead defense counsel made a confession. “I am not computer literate,” he said. “I have not found presence in the cybernetic revolution. I need a secretary to help me turn on the computer. This was out of my bailiwick.”

In a ruling late last year, the court criticized both the defense attorney and the defendant’s Delaware counsel for failing to participate actively in the e-discovery process. “Professed technological incompetence is not an excuse for discovery misconduct,” Vice Chancellor J. Travis Laster wrote. (James v. Nat’l Fin. LLC, 2014 WL 6845560, at *12 (Del. Ch.).)

At this point, it goes without saying that every modern law firm needs to have someone on staff with a working knowledge of e-discovery. Exploding volumes of information are driving demand for these high-tech tools, says Robert Hilson, marketing director at the cloud-based discovery platform Logikcull. And, as the Delaware attorneys learned, a hands-off approach may not work. But figuring out which provider is right for your firm has never been harder. Many existing tools are “antiquated and rely on outdated, incredibly complex work flows,” Hilson says.

How to Choose

William Kellermann, e-discovery and information governance counsel at Hanson Bridgett in San Francisco, has been following the industry since its infancy—“e-discovery has been my life; it’s put my kids through school,” he laughs—and still he says selecting a vendor can be difficult. Using a broad survey of the industry, such as Gartner’s e-discovery report, Kellermann suggests narrowing your list based on vendors’ skill sets and reputations. But he notes there is no one-stop shop for all a firm’s needs. For example, a provider with a strong regional presence might not be the best for international data, he says, and some firms specialize in areas like forensics involving Apple computers.

In addition, many vendors offer similar services, sometimes with only subtle differences. “It’s a bit like going to the farmers market with my family,” says Todd Stefan, executive vice president of Los Angeles–based forensics and e-discovery firm SETEC Investigations. “Do I want oranges from this guy or oranges from that guy?”

The biggest recent additions to the repertoire of e-discovery providers have been among the most jargon-plagued (See glossary below). There are “advanced analytics” in early case assessment, optical character recognition (OCR), predictive coding, and more. And many of these services fall under the rubric of technology-assisted review (TAR)—except for the bits that don’t. TAR is used both as an umbrella term for many of the tools that help automate review and—in a mix-up we can likely thank marketers for—interchangeably with the process of humans training a computer algorithm to recognize important documents.

Jargon Demystified

Called “predictive coding,” that process involves real, live reviewers going through a small portion of potentially responsive documents—say a few thousand—and essentially giving the thumbs-up or thumbs-down on each one. Eventually, like the music service Pandora, the e-discovery program “learns” to recognize important documents on its own, drastically cutting the amount of time that humans must spend reviewing documents.

“The technology’s not doing the work, but it’s assisting and making the human mind more efficient,” says Christopher Gallagher, national director of the e-discovery provider eQ.

Another current e-discovery buzzword is analytic tools, says Gallagher. Most big providers now offer several:

• Dynamic clustering sorts documents by content, allowing reviewers to skip to the most relevant topics.
• Email threading groups and sorts email chains, which can have hundreds of elements.
• Near-duplicate identification matches original documents with similar versions.
• Conceptual search can find relevant documents based on ideas and not exact keywords.

“Last year, we only had a dozen cases that utilized advanced analytics—out of over 200 cases. Just in the first six months of this year, we had more than double that,” he says.

Dollars and Sense

“Too often, discovery is not just about uncovering the truth, but also about how much of the truth the parties can afford to disinter,” Magistrate Judge James C. Francis wrote 13 years ago. (Rowe Ent. Inc., v. William Morris Agency, Inc., 205 F.R.D. 421, 423 (S.D.N.Y 2002).) The cost of reviewing a gigabyte of data has dropped since then, but the amount of electronically stored information has skyrocketed. So the problem remains.

“You’ve got the cell phone, the tablets, the cloud-based work, the personal cloud,” says Stefan at SETEC Investigations, who remembers delivering paper evidence by the truckload in the days before e-discovery. “Throw in social media, and next thing you know a lot of these attorneys are struggling [to review all the documents they need to]. It’s so much more intense than it was historically.”

Pricing models vary by vendor. A vendor may charge per gigabyte, per month, per hour, per page, or in other ways—making comparison-shopping difficult. The best solution is the old-fashioned one: Ask each vendor for a proposal. And, if you have the time to test-drive a system, consider taking advantage of the free trial periods many providers offer to analyze a few hundred documents before charges kick in.

E-discovery on the Go

For firms handling discovery without a litigation-support vendor, experts say the best platform is whichever one the attorneys have the skills to use. Fortunately, many platforms have become simpler and much more convenient.

Also, many platforms—such as Logikcull and Everlaw—are cloud-based. This allows attorneys to review documents from any computer, anywhere. Some platforms upload only metadata to the cloud; others upload all potentially relevant client documents before reviewing them, which increases angst among the security-worried, but the data is typically encrypted when stored on the servers of e-discovery providers. And the American Bar Association has said that data is often more secure with vendors than with law firms. Either way, ease of use has increased demand for cloud-based document review.

Consider Your Situation

The choice of e-discovery platform depends on functionality, cost, the case, and the firm. Stefan of SETEC Investigations recommends stepping back and thinking about your needs, options, and staffing before investigating vendors: Do you have an unmanageable cache of documents and decent funds available? If so, a company touting basic technology-assisted review may be for you. Do you need help with OCR, but not necessarily the most advanced technology? Then talk with a firm that offers managed review. Are you a small firm with a limited budget? In that case, consider newer vendors that let you do it all yourself for a set fee.

“There are more challenges now because there is more data, more choice—and smarter opposing counsel. And these opposing counsel,” Stefan says, “a couple times a week, they’re hiring us to attack what the opposite side did.”

E-DISCOVERY GLOSSARY

  • algorithm: Automated procedures computers follow to solve problems.
  • big data: A buzzword for the vast data sets produced by users of modern technology (including emails, PDFs, cell phone metadata, search engine returns, text messages, and so on) and the analytics used to evaluate this data.
  • metadata: Data about data. For example, email metadata refers to data about an email, including how large it is, its sender and receiver, and its send date.
  • OCR: Short for optical character recognition, a technology that allows scanned documents to become searchable on a computer.
  • predictive coding: Advanced programming that uses algorithms to flag relevant documents during the review process. But humans are still necessary because the program must be “taught” which documents are important by having a reviewer sift through an initial “seed set” of documents. The program can then analyze a much bigger set of documents without human intervention.
  • technology-assisted review (or TAR): A catchall term for the software used to expedite document review.
  • unstructured data: This refers to “electronically stored information” (or ESI, or simply “data”) that is difficult for computers to read and organize.

Resources

See Also

Not My Brother’s Keeper: Varley v. Regional School District No. 4 and A School District’s Responsibility For “Free Speech” Claims Involving a Vendor (19.4)
Smartphone Forensics: 10 Tips From The E-Discovery Trenches (18.5)
How Will the Rise of Google Glass Impact e-Discovery? (17.6)
Litigation Update – Rules of Civil Procedure Continue to Adapt to the Realities of Electronic Discovery (17.6)
In ESI Discovery: Are keywords, concepts and other searches simply antiques? (17.1)
Electronic Discovery: Glossary of 123 Commonly Used Terms (17.1)
ESI Discovery Best Practices, Part 3: You’ve identified ESI, now how do you go about collecting it? (16.6)
Ninth Circuit Shifts “Significant Expense” of Compliance with Third Party Subpoenas to Party Seeking Discovery (16.2)
Taking Stock Of The Legal Cloud: Opportunities And Pitfalls (15.7)
E-discovery Is Hard (15.6)
7 Tips To Cut EDiscovery Dollars (15.6)
Harsh & Costly Lesson from Unilateral Use of E-Discovery Tools (15.5)
Litigation Alert: Not All E-Discovery News is Bad (15)
Managing Your Online Presence after Death (14.7)
Discovery of Social Media Data (14.4)
Tax Court Sanctions Use of Predictive Coding During ESI Discovery (14.3)
Lessons From A Cautionary Tale of Electronic Discovery Pitfalls in Health Care Litigation (14)
Plus-Sized e-Discovery for Medium-Sized Firms (14)
[Video] Why Choose the Dual Track Process? (13.9)
Matching the Solution to the Needs, not the Needs to the Solution (13.7)
Privacy in the Cloud: A Legal Framework for Moving Personal Data to the Cloud (13.5)
Achieve Organizational Goals by Taking a Managed Services Approach to E-Discovery (13.2)
E-Discovery Neutrals – Four Questions (13.2)
Argentina Ruling Doesn’t Address Non-FSIA Discovery Limits (13.1)
You Can Take It With You: Bringing An E-Discovery Case To Your New Firm (12.9)
401(k) Plans and the Free Market: Is Your Vendor Ever a Fiduciary? (12.4)
The Narrow Scope of Supplemental Discovery in an Inter Partes Review (12)
Russia moves to ban exports of personal data (12)
SaaS, PaaS and the Cloud? Part 3: Think Before You Float (11.7)