Defensible Disposal and Predictive Coding Reduces (?) eDiscovery by 65%


Following Judge Peck’s decision on predictive coding in February of 2012, yet another Judge has gone in the same direction. In Global Aerospace Inc., et al, v. Landow Aviation, L.P. dba Dulles Jet Center, et al (April 23, 2012), Judge Chamblin, a state judge in the 20th Judicial Circuit of Virginia’s Loudoun Circuit Court, wrote:

“Having heard argument with regard to the Motion of Landow Aviation Limited Partnership, Landow Aviation I, Inc., and Landow & Company Builders, Inc., pursuant to Virginia Rules of Supreme Court 4:1 (b) and (c) and 4:15, it is hereby ordered Defendants shall be allowed to proceed with the use of predictive coding for the purposes of the processing and production of electronically stored information.”

This decision was despite plaintiff’s objections the technology is not as effective as purely human review.

This decision comes on top of a new RAND Institute for Civil Justice report which highlights a couple of important points. First, the report estimated that $0.73 of every dollar spent on eDiscovery can be attributed to the “Review” task.RAND also called out a study showing an 80% time savings in Attorney review hours when predictive coding was utilized.

This suggests that the use of predictive coding could, optimistically, reduce an organization’s eDiscovery costs by 58.4%.

The barriers to the adoption of predictive coding technology are (still):

  • Outside counsel may be slow to adopt this due to the possibility of loosing a large revenue stream
  • Outside and Internal counsel will be hesitant to rely on new technology without a track record of success
  • Additional guidance from Judges

These barriers will be overcome relatively quickly.

Let’s take this cost saving projection further. In my last blog I talked about “Defensible Disposal” or in other words, getting rid of old data not needed by the business. It is estimated the cost of review can be reduced by 50% by simply utilizing an effective Information Governance program. Utilizing the Defensible Disposal strategy brings the $0.73 of every eDiscovery review dollar down to $0.365.

Now, if predictive coding can reduce the remaining 50% of the cost of eDiscovery review by 80% as was suggested in the RAND report, between the two strategies, a total eDiscovery savings of approximately 65.7% could be achieved. To review, lets look at the math.

Starting with $0.73 of every eDiscovery dollar is attributed to the review process

Calculating a 50% saving due to Defensible Disposal brings the cost of review down to $0.365.

Calculating the additional 80% review savings using predictive coding we get:

$0.365 * 0.2 (1-.8) = $0.073 (total cost of review after savings from both strategies)

To finish the calculations we need to add back in the cost not related to review (processing and collection) which is $0.27

Total cost of eDiscovery = $0.073 + $0.27 = $0.343 or a savings of: $1.0 – $0.343 = 0.657 or 65.7%.

 As with any estimates…your mileage may vary, but this exercise points out the potential cost savings utilizing just two strategies, Defensible Disposal and Predictive Coding.

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Information Management Cost Reduction Strategies for Litigation


In these still questionable economic times, most legal departments are still looking for ways to reduce, or at least stop the growth, of their legal budgets. One of the most obvious targets for cost reduction in any legal department is the cost of responding to eDiscovery including the cost of finding all potentially responsive ESI, culling it down and then having in-house or external attorneys review it for relevance and privilege. Per a CGOC survey, the average GC spends approximately $3 million per discovery to gather and prepare information for opposing counsel in litigation.

Most organizations are looking for ways to reduce these growing costs of eDiscovery. The top four cost reduction strategies legal departments are considering are:

  • Bring more evidence analysis and do more ESI processing internally
  • Keep more of the review of ESI in house rather that utilize outside law firms
  • Look at off-shore review
  • Pressure external law firms for lower rates

I don’t believe these strategies address the real problem, the huge and growing amount of ESI.

Several eDiscovery experts have told me that the average eDiscovery matter can include between 2 and 3 GB of potentially responsive ESI per employee. Now, to put that in context, 1 GB of data can contain between 10,000 and 75,000 pages of content. Multiply that by 3 and you are potentially looking at between 30,000 and 225,000 pages of content that should be reviewed for relevancy and privilege per employee. Now consider that litigation and eDiscovery usually includes more than one employee…ranging from two to hundreds.

It seems to me the most straight forward and common sense way to reduce eDiscovery costs is to better manage the information that could be pulled into an eDiscovery matter, proactively.

To illustrate this proactive information management strategy for eDiscovery, we can look at the overused but still appropriate DuPont case study from several years ago.

DuPont re-looked at nine cases. They determined that they had reviewed a total of 75,450,000 pages of content in those nine cases. A total of 11,040,000 turned out to be responsive to the cases. DuPont also looked at the status of these 75 million pages of content to determine their status in their records management process. They found that approximately 50% of those 75 million pages of content were beyond their documented retention period and should have been destroyed and never reviewed for any of the 9 cases. They also calculated they spent $11, 961,000 reviewing this content. In other words, they spent &11.9 million reviewing documents that should not have existed if their records retention schedule and policy had been followed.

An information management program, besides capturing and making ESI available for use, includes the defensible deletion of ESI that has reached the end of its retention period and therefore is valueless to the organization.

Corporate counsel should be the biggest proponents of information governance in their organizations simply due to the fact that it affects their budgets directly.

Information Governance and Predictive Coding


Predictive coding, also known as computer assisted coding and technology assisted review, all refer to the act of using computers and software applications which use machine learning algorithms to enable a computer to learn from records presented it (usually from human attorneys) as to what types of content are potentially relevant to a given legal matter. After a sufficient number of examples are provided by the attorneys, the technology is given access to the entire potential corpus (records/data) to sort through and find records that, based on its “learning”, are potentially relevant to the case.

This automation can dramatically reduce costs due to the fact that computers, instead of attorneys conduct the first pass culling of potentially millions of records.

Predictive coding has several very predictable dependencies that need to be addressed to be accepted as a useful and dependable tool in the eDiscovery process. First, which documents/records are used and who chooses them to “train the system”? This training selection will almost always be conducted by attorneys involved with the case.

The second dependency revolves around the number of documents used for the training. How many training documents are needed to provide the needed sample size to enable a dependable process?

And most importantly, do the parties have access to all potentially relevant documents in the case to draw the training documents from? Remember, potentially relevant documents can be stored anywhere. For predictive coding, or any other eDiscovery process to be legally defensible, all existing case related documents need to be available. This requirement highlights the need for effective information management by all in a given organization.

As the courts adopt, or at least experiment with predictive coding, as Judge Peck did in Monique Da Silva Moore, et al., v. Publicis Groupe & MSL Group, Civ. No. 11-1279 (ALC)(AJP) (S.D.N.Y. February 24, 2012, an effective information management program will become key to he courts adopting this new technology.