eDiscovery Daily Blog
Preparing for Litigation Before it Happens: eDiscovery Best Practices, Part Six
Editor’s Note: Tom O’Connor is a nationally known consultant, speaker, and writer in the field of computerized litigation support systems. He has also been a great addition to our webinar program, participating with me on several recent webinars. Tom has also written several terrific informational overview series for CloudNine, including eDiscovery and the GDPR: Ready or Not, Here it Comes (which we covered as a webcast), Understanding eDiscovery in Criminal Cases (which we also covered as a webcast), ALSP – Not Just Your Daddy’s LPO, Why Is TAR Like a Bag of M&M’s?, eDiscovery for the Rest of Us (which we also covered as a webcast) and Litigate or Settle? Info You Need to Make Case Decisions (which is our next scheduled webcast on August 29th). Now, Tom has written another terrific overview regarding pre-litigation considerations titled Preparing for Litigation Before it Happens that we’re happy to share on the eDiscovery Daily blog. Enjoy! – Doug
Tom’s overview is split into seven(!) parts, so we’ll cover each part separately. Parts one, two and three were published last week, part four was published Monday and part five was published Tuesday. Here’s the sixth part.
BTW, in addition to exhibiting at ILTACON in National Harbor, MD next week in booth 936, CloudNine will also host a happy hour on Tuesday, August 21 from 4:30 to 6:30pm ET at the National Harbor’s Public House (click here to register). Come and get to know CloudNine, your provider for LAW PreDiscovery®, Concordance® and the CloudNine™ SaaS platform! We want to see you!
One Reason Why IG is Not More Popular
I have developed one theory for why formal IG policies and software have not been used more widely. It is that the increased improvements in and use of technology to analyze data and find patterns in Big Data has preempted more widespread use of IG applications.
This is not a new phenomenon. Knowledge Management pioneers were doing this type of development years. People like Ron Friedmann, Partner at Fireman & Co. and Peter Krakaur, Vice President of Legal Business Solutions at United Lex, were building home-grown systems at their firms (Ron at Wilmer Cutler Perkins in the early 90’s and Peter at Brobeck Phleger & Harrison came in the early 2000’s) to share, use and manage internal information. These KM systems were the first multidisciplinary approach to achieving organizational objectives by making the best use of enterprise wide knowledge
Search engines were not unique but later came blazingly fast search engines like X1. Using indexing across more than 500 filetypes, X1 allowed unified searching through their local data indices across multiple data types with a user-friendly interface.
Then came Google with it’s equally fast web-based searching. Google wanted to index all the information they were collecting and then present meaningful results to users. There was nothing on the market that would do that, so they built their own platform which eventually came to be the open source project Nuch. Hadoop was spun-off from that and Yahoo then helped develop Hadoop for enterprise applications.
Both Google and then Hadoop were designed to search large amounts of data that didn’t fit into tables and could benefit from analytical searching. Further, Hadoop was designed to run on a large number of machines that don’t share either memory or disks, so users could buy their own servers, link them together and run Hadoop on each one. The result is you can have organizational data on multiple separate servers and Hadoop is good at dealing with data spread across multiple servers.
So, as the data environment became one where early systems in limited domains were struggling to find distributed data, the need arose for this new generation of knowledge management solutions using semantic and linguistic capabilities that could provide system wide information access in a non-structured way.
Ralph Losey made the point best when he observed that “AI-Enhanced Big Data Search Will Greatly Simplify Information Governance” (in this blog post here). Why? Because as he put it,
In order to meet the basic goal of finding information, Information Governance focuses its efforts on the proper classification of information. Again, the idea was to make it simpler to find information by preserving some of it, the information you might need to access, and destroying the rest. That is where records classification comes in.
This creates a basic problem for Information Governance because the whole system is based on a notion that the best way to find valuable information is to destroy worthless information. Much of Information Governance is devoted to trying to determine what information is a valuable needle, and what is worthless chaff. This is because everyone knows that the more information you have, the harder it is for you to find the information you need. The idea is that too much information will cut you off. These maxims were true in the pre-AI-Enhanced Search days, but are, IMO, no longer true today, or, at least, will not be true in the next five to ten years, maybe sooner.
The interesting point is that Ralph said this in 2014. That’s right. Four years ago. So maybe the issue with lack of IG deployment is that were undergoing the same realization that Ralph articulated and were drifting away from IG programs into more analytics-based programs that they could build themselves.
As I pointed out above, business data can be regulated by hundreds if not thousands of federal, state and local laws which require different types of information to be preserved for different lengths of time. Information governance thus became a very complicated legal analysis problem and building an IG policy around this “records life-cycle” paradigm to reflect those requirements might have made sense in a paper world.
But Big Data in the ESI world is cheap to store and easy to search, especially with the new analytic algorithms and the new paradigm is what Ralph calls “Save and Search v. Classify and Delete.” Ralph also likes to call all this new analytic power “AI.” I myself think that’s an underdefined and over used label but is the name really important? If I can use a newer analytical search product such as Brainspace or Heureka to effectively comb through massive amounts of corporate data and then see trends and links among users and data types I’m not sure that it matters what we call it.
To sum up, Ralph sees three big advances in the field of search analytics that are dictating the new alternatives to IG: Big Data, cheap parallel computing and better algorithms. All three of those combine to make IG systems less important as clients learn to adopt aggressive search strategies with new technology that allow them to find data for both corporate strategy and litigation avoidance.
We’ll publish Part 7 – Concluding Remarks – tomorrow.
So, what do you think? Does your organization have a plan for preparing for litigation before it happens? As always, please share any comments you might have or if you’d like to know more about a particular topic.
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Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine. eDiscovery Daily is made available by CloudNine solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscovery Daily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.
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