Electronic discovery sampling is a natural next step

ediscovery

When confronted with an almost impossible data analysis problem, a tried and true technique to solve it has been the use of sampling. The mathematical analysis behind sampling is something that has been studied for quite a number of years. Also, sampling has also been put into practice for well over seventy years, in many fields from predicting results of elections and assessing quality of electric bulbs. Why not do the same for certifying your ESI productions, while also addressing defensibility and reasonableness?

Sampling as a way to assess quality is something the Electronic Discovery Reference Model (EDRM) Search Group authors covered in detail, with a strategy in a comprehensive EDRM Search Guide (see Section 9.5 and Appendix 2).

And, while much of that work is still to hit the mainstream litigation scene as a general practice, I was pleasantly surprised to see it receive attention from a fellow blogger and litigator, Nick Brestoff, who highlighted this in a very thoughtfully crafted article in Law.com, titled A Strategy to Sample All the ESI You Need. I commend his article for helping the community understand the practical difficulties in getting a certifiable result that attorneys can stand behind. And, it is highly likely that the current practice is to certify your electronic discovery without a real measure of validity behind it.

That leads us to back to the mechanics of sampling, the math behind it, and its defensibility. As the EDRM Search Guide notes, meaningful sampling can only be done by the one who has the data, i.e., the producing party.

While the Federal Rules of Civil Procedures (FRCP) Rule 26(a) lists required disclosures as well as signing and certification guidelines per Rule 26 (g), there is no agreed upon way to specify sampling parameters as well as the results of sampling.It is in this context, Nick Brestoff’s article is significant – it explores practical ways in which the producing party can shift the sampling mechanics to the requesting party. I do think, however,that there is a logistical problem with this–most litigators will balk at producing the largely irrelevant and non-responsive items to the other side.

Perhaps the real need is for the requesting party to specify in their Rule 26 (b) meet and confer, that the production be certified for completeness by also including a statement on sampling and its results.

A simple request such as, “Sample the data for 98% confidence level and 2% error rate, and report the number of responsive documents” could be sufficient. The producing side can perform random sampling, per the sampling goals for the above request, selecting 13526 documents (based on the sampling table of EDRM Search Guide). This allows the attorneys representing the producing party to certify and sign off on an agreed-upon target.

In addition to the EDRM Search Guide, The Sedona Conference, Working Group Commentary, Achieving Quality in the E-Discovery Process is an indispensable resource for understanding the role of sampling. This paper discusses at length, several sampling methods, their applicability for various purposes, including certifying that the results meet a certain quality criteria.

In addition, a number of electronic discovery cases have mentioned sampling as a way of overcoming the explosion of data volumes.A primary application of sampling is for evaluating proportionality claims, something that has moved from a simple assertion into an informed argument, with specificity on proving cost burden. Let’s examine a few.

Referring to the well-known Zubulake v. UBS Warburg, F.R.D. 280, the courts ordered the producing party in Makrakis v. Demelis, No. 09-706-C, 2010 WL 3004337 (July 13, 2010) to essentially sample just a small number of backup tapes, at the expense of the requesting party. This is also remarkable in the cost-shifting of processing and reviewing of the sample, however small, to the requesting party. Such measures, while reducing the costs of overall e-discovery, places a greater burden on sample selection to the requesting party, forcing them to apply the reasonableness evaluation.

In Barrera v. Boughton, 2010 WL 3926070 (D. Conn. Sept. 30, 2010), the court ruled that a phased approach to ESI discovery is appropriate and quotes an earlier case, S.E.C v. Collins & Aikman Corp, 256 F.R.D. 403, 418 (S.D.N.Y. 2009), that “[t]he concept of sampling to test both the cost and the yield is now part of the mainstream approach to electronic discovery.” The sampling recommendation in this instance was both a reduction of number of custodians from forty to three, as well as a significant reduction in the date range for the search. What was initially a $60,000 ESI search and discovery effort was reduced drastically to under $13,000.

Similarly, sampling is suggested in both M. Adams & Assoc., L.L.C. v. Fujitsu Ltd., No. 1:05-CV-64, 2010 WL 1901776, and Mt. Hawley Ins. Co. v. Felman Prod., Inc. as a way to perform a small set of search terms on a smaller number of custodians so as to get a sense for the larger electronic discovery costs.Clearone Communications v. Chiang offers another example of sampling by the use of Boolean logic to combine more common search terms thereby avoiding over-inclusiveness.

Per the Sedona commentary definitions, this type of sampling is referred to as “judgmental sampling” wherein the practitioner has a general sense of which of the several custodians and date range is most likely to offer the greatest yield. As judgmental sampling becomes more widely adopted as a way of controlling costs, electronic discovery sampling can embrace the benefits of statistical sampling as well.

It is a natural next step, as even with narrow sampling criteria of judgmental sampling, the cost of review can be high. One area where statistical sampling has an advantage is that quantifiable measures of error and confidence intervals are possible, while judgmental sampling has no such formal measurement. Again, if the requesting party wishes to ensure a level of completeness and quality and if the producing party needs a basis for certifying their productions, statistical sampling can be a powerful aid.

Venkat Rangan is Co-Founder & Chief Technology Officer at Clearwell Systems. Venkat has more than 27 years of enterprise software development experience. He has lead technology innovation in the areas of search and electronic discovery and is currently a member of several Sedona Conference working groups as well as holds leadership roles in the EDRM Project. He has also led innovation in information retrieval technology through his participation in TREC Legal Track, a research community responsible for advances in information retrieval as it pertains to legal community. Venkat obtained his Master's degree in Computer Science from the University of Miami, Coral Gables, Florida, and an undergraduate degree in Engineering from the Indian Institute of Technology, Madras, India.

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