Trying to understand big data recalls the story of the blind men touching an elephant. Although we don't have a clear picture of the totality of the big data elephant, we can still learn something from each part that we touch. Understanding the role of big data in e-discovery is one such example.
(Note that the following discussion focuses on the use of predictive coding, which is an analytical technology for processing e-discovery "big data." Since it's a data-driven intelligence software application, "predictive coding" is also the name for a class of products that all try to accomplish the same goal. For purposes of this discussion, predictive coding will be used in a general sense.)
The purpose of civil litigation is to determine who wins a lawsuit and who loses money. However, the monetary impact can be found in more than the potential monetary awards--there's also the cost of the litigation process itself. That can be quite expensive, especially when hundreds of thousands or even millions of documents (sometimes even tens of millions of documents) are involved in large cases. So keeping e-discovery costs down is a key goal of an enterprise's internal legal team.
As part of the e-discovery process, the team must determine which documents are responsive (relevant to the litigation). All documents must be examined and winnowed down to (hopefully) this much smaller subset. The process was previously done manually, which has been a reasonable approach, although human beings are prone to error (especially when scanning a lot of documents) and reasonable people can disagree. It was costly, however--after all, this isn't a minimum-wage situation, and even outsourcing is expensive.
A second approach is to use keyword search. While this sounds useful and can be of some help, FTI Technology, a leading e-discovery vendor, reports that generally only a fraction of responsive documents can be found via keyword methods.
Predictive Coding: Predictive Analytics for E-discovery
An advanced analytical approach called predictive coding can now be used to successfully winnow a set of documents. A small subset of all the documents--enough to provide a statistically reliable sample size--is examined manually, and the documents are classified as responsive or nonresponsive. Different predictive coding schemes exist, but the algorithms and heuristics apply their artificial intelligence, machine learning, data mining or whatever you want to call it to classify documents that are considered responsive in a civil litigation case.
That sounds great, but when potentially very large sums of money are involved and traditionally accepted, andhuman-based processes are being dramatically altered, questions legitimately arise, such as the following:
- Can lawyers effectively defend the use of predictive coding?
- Will the courts accept the use of the technology?
- For what type of legal matters is the technology well suited, and for what types is it not?
- Are the economic cost savings real enough to justify the use of the technology?
- How is the adoption process proceeding?