How do I prove that TAR makes sense?

By Mark Walker, VP Advisory Services at iControl ESI

Please post a response with your thoughts, especially if you disagree with any of this, or I get anything wrong. This post is intended to prompt discussion on this topic.

Introduction

When does it make sense to use a TAR workflow? Those of us that work with predictive analytics (a/k/a predictive coding) and TAR workflows have been asked this question more times than we can count. The answer is usually the unpopular “it depends” response. At the end of the day, there should be a cost vs. benefit math exercise. However, non-monetary factors can also impact the decision. The time allotted to get the production out the door, resources available, and the budget are also factors. Even case strategy can factor into the equation. There are a lot of variables. Virtually everyone agrees that in most cases we simply cannot review everything. Most just resort to using date, file type and search term filters. We can do better.

Those of us that have been using TAR workflows for years know that a well-planned TAR workflow using machine learning (preferably active learning) will save both time and money. We know that using the right technology is highly accurate when based upon sound sampling methods where humans teach the technology to find what they seek. But how do we prove it to someone who has never traveled that road? Lawyers are all about proof. That’s what they do. We have a tough audience.. 

Defining the Problem

A few weeks ago, I reconnected with a LitSupport manager at a major law firm. He has been in the industry a very long time and closely follows the most cutting-edge technology. As a LitSup manager, he has had success convincing lawyers within his firm to use TAR workflows. Well, some of them. This time, I asked him the dreaded question, but in a slightly difference way – “What kind of cases should your lawyers consider using predictive analytics.” His answer, tongue in cheek: “Every Case!” We both got a good chuckle out of that answer. While we chuckled, he is exactly right. But, like everyone else in the industry, he is also frustrated with the industry as a whole’s inability to make the argument in a way that resonates with lawyers. Some use fear to convince – if you don’t do it, others will. Lawyers like litmus tests. Bright lines. They don’t like grey. Lawyers don’t react well to threats and attempts to invoke fear.

When reviewing documents, lawyers want documents that are relevant. Sure, good lawyers are concerned about cost and one would think would be interested in anything that will make them more efficient. But, they are also concerned about risk and trust.

Here’s the root of the problem: Relevancy rates in collected documents are often as low as 1% in most cases. That means 99 out of every 100 documents collected have no value. Sure, there are exceptions, but it is rare that a document review relevancy rate is above 50% using traditional search and review work flows. No matter how you cut it, when 50% of what you review (best case) is wasted effort, there is an expensive problem that needs to be solved. By the way, a search and review workflow that achieves a 50% reduction and relevancy rate is a phenomenal achievement. We traditionally see closer to 30% without leveraging a TAR workflow. We can do better! We must get as close to the 1% we seek as possible.

Using a document count litmus test to determine whether to use predictive analytics doesn’t work. For example, “use predictive analytics when you have 10,000 documents to review”. The average single custodian (witness) has on average 10,000 documents collected. If 1% of that is what we expect to be relevant, then out of 10,000 documents your seeking 100 that are relevant. There are too many other factors that might make it more cost effective to just review the 10,000 documents. Document count is not the right litmus test. 

Solving the Problem - Do the math

Using our 10,000 document, single custodian example, we arrive at a conservative 50% relevancy rate litmus test. That is, if you expect that whatever method you use to filter down before review will yield less than a 50% relevancy rate during review, then it makes sense for you to deploy TRUSTED predictive analytics technology to your review, often in conjunction with validating search terms to exchange with the opposition. See Combining Search and Predictive Coding in 5 Easy Steps. While you can’t really know for certain what the actual relevancy rate will be up front, obviously, we can usually have a pretty good idea if it’s going to be above 50%.

In our 10,000-document example using a traditional filter, search and review methods, one might cut the review in half and only review 5,000 documents. At a billing rate of $250 per hour, and a typical review rate of 55 docs per hour, the cost to review 5,000 documents is $22,727.27. $250 an hour is low compared to the market rate for associates. Make your estimates conservative.

If predictive analytics rate is $0.06 per document, the cost to classify with predictive analytics the 10,000 documents available for review is $600. All other technology costs such as processing and hosting will be incurred no matter what review method you chose.

Leveraging predictive analytics, you should typically see an 80% or above relevancy rate during review. If you only achieve 50% using traditional search and review, then spending $600 on analytics achieves at least 30% improvement, which is very conservative. Therefore, in this very conservative example you reduce the review by 1,500 documents and avoid 27.7 hours of review time. At $250 per hour, that’s $6,818.18 of review cost avoided. Since the analytics cost just $600, the net savings is $6,218.18. How can anyone ignore that advantage?

Ah, naysayers might say, we are going to use contract reviewers at $55 per hour! Even with the dramatically reduced billing rate, there is still a net savings of $900, and don’t discount speed either. 

Predictive Analytics is not just for Big cases anymore.

In the example above, we’ve used a very small case - a 10,000 document case hosted in a review platform is, well, rare these days. Many of the cases we deal with are multi-million document cases. 100,000 hosted is common. Using the same modeling as outlined above, the savings achieved on a 100,000-document population is persuasive and undeniable.

At a $250 per hour review rate

At a $55 per hour review rate

Conclusion

With very few exceptions, leveraging a TAR workflow that includes predictive analytics (a/k/a predictive coding) will save considerable time and money. The courts have been encouraging lawyers to leverage technology. Clients are demanding their outside counsel reduce costs.  Fixed fee arrangements are becoming common place where lawyers have skin in the game to keep the time they spend on matters low. For contingent fee lawyers, time really is money.

Do the math yourself. Apply whatever assumptions you feel appropriate. Increase document decisions per hour, lower hourly rates, increase the per doc cost of analytics. What you will find is that using even the most extreme and efficient methodology, leveraging predictive analytics simply makes financial sense for everyone involved. Reach out to me and I’ll provide you with a calculator so you can input your own assumptions.

So, what’s keeping you from leveraging predictive analytics? Inquiring minds want to know.