This week I had a number of conversations with lawyers so that I could at least try to understand what it was like to be in their shoes. We talked about interesting cases, amazing escapes and ultimately about life in the electronic world. But a resounding theme was “I wish I could go back to practicing law like I did 15 years ago”. Don’t get me wrong, these folks still loved their jobs, but they felt that somehow they had become subservient to a process, a workflow, that is ESI. The sheer volume of electronic data has changed their world.
I tried to visualize this impact and drew this picture – death by 1000 smiley faces. The point is the following: in ‘the old days’ when building a case a lawyer would conduct interviews and come up with a list of people (or custodians) who he felt pretty sure we close to the matter in hand. Call it intuition or gut-feel, it was a bit like police work, they just knew who the bad guys were. This gut feel still holds true today, except that lawyers cannot trust their instincts given the morass of electronic data that abounds – email, SMS, sharepoint, hard drives, facebook. smart phones, etc. So, they are forced to throw a much wider safety net around the cast of actors (the custodian list). Now they are faced with possibly hundreds of custodians rather than the 5 or 10 that their instincts tell them are the real actors. Well, we all understand the problem with this picture – there is too much data… so we use keywords to negotiate down the scope and cost of discovery.
Over-preservation and over-collection is a big problem. But we are trying to fix it at the back end (with keywords) rather than at the front end (identify the custodians really involved). But if we could do this it would be a sense of deja vu or “Back to the Future”.
What if I told you Catelas can take you Back to the Future. Comprehensively and defensibly we can help you limit the scope of discovery so that you really are preserving and collecting only those custodians that are truly close to the matter. Don’t settle for the 1000 smiley faces approach.