As we embark on a new year, I am in constant thought about the areas in which we can help our clients and prospective clients improve efficiency and cost savings in ediscovery. With all the ideas I have, there is one area in particular that seems to be at the top of the list – analytics.
While the use of analytics has been more widely adopted and many have realized the value, there is still a large population out there who do not yet feel comfortable using analytics. For the purpose of this blog, I will refer to them as the non-believers. What is it that keeps the non-believers from using analytics? Is it the concern of confidence in technology, the fear of not achieving desired results, or the cost of using analytics? Let us review these concerns in order to dispel these doubts.
Non-believers lack confidence in technology
Analytics are a part of our daily lives. For example, when shopping for books on Amazon.com, I search for the title of a book that I have previously enjoyed and it returns details of the book as well as a listing of other books that customers bought based on this search. When I scroll further to the bottom, a listing of recommend items based on my browsing history is provided. I rely on these features to help me identify books that I would enjoy based on my past search results. In the end, I can review the suggested books and make the final decision to purchase.
eDiscovery analytics apply the very same method. You make decisions on a subset of documents as to whether they are examples of responsive or non-responsive documents. The decisions you make on the initial set of documents are then analyzed and applied to the remainder of the documents in the corpus and the system recommends a breakdown of responsive and non-responsive documents based on what you previously identified. You then sample these two groups to confirm the decisions are in alignment.
Confidence in using suggestions based on known values previously identified, allows you to quickly assemble results for final review.
Non-believers fear not achieving successful results
Who wouldn’t want to know that they are going to have the desired results they are seeking before buying into analytics? No one, right? However, the underlying issue is that you are focusing on savings that cannot be quantified given the fact that you have not defined what successful results means to your matter. The results you desire should not necessarily be focused on how many responsive documents are found, but rather the ability to group responsive and non-responsive documents for final sampling. Analytics help you divert from the review and coding of each and every document by taking decisions you make and applying them forward. Successful results are the achievement of grouping the responsive and non-responsive documents together quickly, efficiently, and cost effectively.
Non-believers do not want to incur the cost of analytics
The cost of analytics is something that everyone has concerns with. No one wants to pay more than necessary on their ediscovery matters. Leveraging analytics has been proven to save cost and time, both of which are a focus for not only clients, but providers as well. Cost savings also translates into making sure you Maximize the return on each dollar spent, not only in technology, but man power as well. Many do not think of the amount of money spent on document review alone. The cost discussion at the front end is over document collection, filtering, ingestion and production. While review is a necessity, it is the most expensive portion of an ediscovery project. Review costs can be a managed by making sure reviewers are spending time on a quality sample review of those documents that have previously been grouped as responsive and non-responsive by use of analytics.
As new matters arrive on your desk this coming week, ask yourself if you are applying lessons learned from previous matters? Did you use the technology available to you or did you find yourself browsing through the library aisle by aisle, shelf by shelf, hoping to find that one important item? Next time you want to think like Amazon, think analytics.
Please feel free to reach out to me at firstname.lastname@example.org to continue this discussion.