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4 TAR Conversations and How to Prepare for Them

Every day, we create massive amounts of data—around 2.5 quintillion bytes by some estimates. Not surprisingly, this influx of data has reshaped the litigation discovery landscape. During e-discovery, it has become virtually impossible to sift through all of this data without using analytics tools such as technology-assisted review (TAR). Yet, some clients are still hesitant to adopt new workflows. How can you best prepare for a TAR conversation with such a client?

It is important to start with some fact-finding. First, you must discern what is really most important to them. For example, is saving time more important than how much the production will cost? Clarifying priorities up front will help you formulate the best answers to overcome possible objections.

The Cost-conscious Client

What you hear: “I’ve heard the hype, but I’m just not confident I can justify the spend to give TAR a try.”

What you can say: “The budget structure may be different than you’re used to with a linear review, but it will be a smaller spend by the time this project is complete.”

TAR can cut down the cost of review in the long run. However, clients’ concerns may stem from the fact that the budget may look different—rather than hiring dozens or even hundreds of contract reviewers at a relatively low hourly rate to get things started, these projects require expert attorneys on the case from the start. After years of budgeting for e-discovery a certain way, it can be difficult to break the mold.

The bottom line is that extensive manual attorney review is expensive. Though TAR doesn’t completely eliminate the need for expert attorneys, it considerably cuts down on the number of documents they’ll need to review by using algorithms to identify the most relevant ones. Your client can take a set of millions of documents and knock it down to thousands, all for a tiny fraction of the price of manual attorney review. You can help their experts get to the good stuff sooner, and spend less money on their efforts once all is said and done.

The Time-strapped Client

What you hear: “We’re on a tight deadline. There’s just not time to experiment with this.”

What you can say: “In many cases, TAR is actually best suited for projects that need to turn around quickly. It will accelerate review by cutting through the noise right away.”

High-stress timelines are no surprise in the world of litigation, and that hasn’t changed despite growing data volumes. Faced with this pressure from the calendar, clients may be hesitant to give a new methodology a try—even if it’s proven to move more quickly than linear review.

TAR dramatically reduces the document volume that needs to be reviewed, or brings forward the most relevant information at the very beginning of a project. When your client is working under a tight deadline but hesitant to start a TAR project, suggest a prioritized relevance review. Documents with the highest relevancy ranking can be reviewed first to meet their deadline, and they can QC the non-relevant materials at the back end of the project.

The Multi-tasking Client

What you hear: “If I’m taking a chance to learn TAR and get our data indexed for it, how else can it benefit me? I need more ROI out of this.”

What you can say: “TAR isn’t just for standard review. Getting familiar with it and making it a habit can mean better QC workflows, better ECA, and more.”

TAR is not the be-all of analytics, and review is not the end-all of TAR. Putting in the time to index data and learn a proper TAR workflow can help teams accomplish more than just document prioritization or elimination. Even after a case is complete, that knowledge will make future projects seem much simpler.

For example, a client using TAR for review may also be able to use it to quickly sort and assess opposing counsel’s production using the same work product from their own TAR project. Even before that, deposition preparation and quality control of productions are streamlined by the organization that comes with TAR.

The Cautious Client

What you hear: “Honestly, I’m just not sure about this technology. How do I know we can trust it with our data?”

What you can say: “I hope it helps to know that the science behind TAR isn’t new; it’s been proven effective for e-discovery, and courts all over the world have approved it again and again.”

Though TAR has become more popular in recent years, sometimes we still need to dispel the myths around its efficacy. Here are two examples:

TAR is not just a computer reviewing documents. This technology is a tool that classifies and ranks documents based on their likelihood of responsiveness as it learns from reviewers’ initial coding decisions. TAR eliminates the need for expert attorneys to review large numbers of documents that are likely irrelevant. In fact, research has shown that TAR has a lower error rate than manual review alone.

TAR is defensible. Recent court opinions have approved of the use of TAR, recognizing that it’s a legitimate way to conduct e-discovery. In 2012, US Magistrate Judge Andrew Peck approved of the use of TAR in the landmark case, Da Silva Moore (Da Silva Moore v. Publicis Groupe & MSL Grp., 287 F.R.D. 182 (S.D.N.Y. 2012)). In 2014, U.S. Tax Court Judge Ronald L. Buch approved the use of TAR in Dynamo Holdings Ltd. Partnership v. CIR. In dismissing the IRS’s argument that TAR is an unproven technology, Judge Buch noted that while regular e-discovery would have cost over $500,000 in this case, the estimated cost with TAR amounted to around $80,000. Since then, courts in the United States, the UK, and Ireland have approved the technology for use in litigation.

Ultimately, your clients have the same goal you do: to protect their organization and data during a high-stress matter that can make or break their business. With the ability to speak their language and calm their fears around TAR, you can help ensure that goal is attained as effectively and quickly as possible.

Eric Kveton is a project manager. He oversees all aspects of the EDRM for customer projects, including the collection, processing, and analysis of data and management of large cases.