Schieneman and Opsitnick are the founders of the popular website ESI Bytes, which offers free podcasts on timely e-discovery topics. The goal of ESI Bytes is to enable leaders in e-discovery to share their electronic discovery theories for free as Bytes of information so litigants can avoid the Bite of electronic discovery and learn about bytes of information about electronic discovery.
Here is an excerpt of the conversation:
E-Discovery Beat: 2012 has been a huge year for predictive coding and predictive technologies. Do you think that this year we will see more widespread adoption of predictive technologies or do you think there are still several barriers before you see it really catch on?
Schieneman: Well, having been through and worked in the Global Aerospace case and working, trying to get people use predictive coding, I see a lot of significant barriers still out there but I see continued growth. The biggest barriers are not having people knowing how to use the tools, the customers. Even if they have some idea it’s usually not very deep but they know that the person on the other end or the judge doesn’t have a good understanding. So how do you work through an entire process when you’re in the middle of litigation and you need to do something now.
On the flip side, I think it’s perfectly fine because I don’t believe that the technology providers have the bandwidth to handle everyone. If everyone wanted to do predictive coding right now then we would probably be exporting all our litigation over to Canada. We need to evolve and we are going to. I have no doubt that these tools are going to take over but it’s just going to take a little bit of time, but the future is definitely very bright.
Opsitnick: As Karl indicated, there remain obstacles but I think that over the next three years we will see widespread adoption. I think to say that this year we’ll see wide spread adoption is probably a little overstated. But I think three years from now it will be extremely commonplace and everybody is moving in that direction. People will understand and appreciate cost savings associated with it and it will become completely mainstream just like the evolution of so many others tools and applications in this space.
E-Discovery Beat: One of the other big themes at the conference is big data and there seems to be a billion definitions of what big data is. What does big data mean to you and where do you see it impacting the e-discovery processes the most?
Opsitnick: I think over the years, defining big data has changed, no exception over what is going to happen to in the next five years. The fact of the matter is people have larger collections of data and every year it increases. So what is big data this year will be commonplace five to seven years from now. I remember ten years ago, 5-10 GB was a lot. Today, that’s not a lot. So what does big data mean? It means when we are getting into the terabyte range. When I talk terabyte range, I’m talking about ingesting, reviewing and things of that nature. Now virtually every corporation starts with a terabyte or more.
Every case we get involved in these days has a terabyte or more. I have meetings all the time with companies that tell me they have 50 terabytes or 80 terabytes. We hear about companies with petabytes, but in terms of big data I think technology, smart people and processes solve that just like they solve smaller cases, it’s just about better organization and efficiency.
Schieneman: I think what big data will ultimately put pressure on is for organizations to not just focus on if we can ingest all the big data and that we can build process around trying to shrink that big data. But there is going to be even more emphasis to be able to initially find what is really important. You’ve got your production obligations that’s a little bit separate from your “do I have a case here”, which is more analytics. I think analytics are going to be more important over time.
Opsitnick: I would add that one of the biggest problems I see in these cases that people refer to as big data as a failure on the part of attorneys on both sides of the equation to appreciate the proportionality arguments and really make the narrow cuts and the decisions that need to be made to move forward the data which is most important in the litigation. So typically when I hear people talking about massive amounts of data, they just really haven’t honed the pencil the way they need to, either with the other side or themselves. I think predictive coding is ideal for big data, I think that’s exactly what it’s suited, that’s the problem that it’s designed to fix. So I disagree with the fact that people think that big data cases aren’t ready for predictive coding.
For more information on big data’s role in e-discovery, watch Exterro’s recent webcast “Big Data Converging with Legal, Information Governance and Regulatory Requirements“ here.