Information Governance: Thoughts on eDiscovery

In the last two weeks, Gartner has published their “Magic Quadrant” for eDiscovery, along with their view of the state of the market.  Our views on the market, along with what we’ve seen recently with our client-base, align with many of their observations.  In coming blogs, I will address some of the topics raised in these reports, along with observations around the market from our clients and other analysts; and where our strategy is focused to continue to serve as a leader in the market.

 

For this blog, I wanted to cover a couple topics.  First, I wanted to comment on the MQ, and second to discuss HP Autonomy’s current strategy around recommendations that were incorporated into these reports.  It is worth pointing out that many of the ideas in these reports are not new, but 2012 and the first half of 2013 has provided some empirical data to support the trends.   

 

HP Autonomy was again firmly established within the Leadership Quadrant for eDiscovery.  I am not sure that any other solution provider has been in the Leadership Quadrant as long as Autonomy (although I am sure someone will tell me if I am wrong).  The eDiscovery market has been extremely dynamic over the years, so to remain a leader is a testament to the fact that Autonomy can both innovate, while also deliver a solution that addresses the day-day requirements for our clients.  It also shows the importance of solid financial execution to support appropriate R&D, and staying power to live through fads and changes.

Beyond our solid results, in this blog I want to discuss two topics that were noted in both Gartner reports, and are also more broadly discussed by other analysts (IDC, Forrester, eDJ, etc) as well as other HP Autonomy clients:

 

  1. Importance of Analytics:  I had to laugh a little around the main themes in the last two Legal Tech’s; Predictive Coding in 2012 and Big Data in eDiscovery in 2013.  In a lot of ways, these two “hot topics” in two successive years show why so many eDiscovery players have come and gone. It also shows that a lot of people don’t understand the metaphysics involved here; the key to both is around information analytics.  To illustrate a point, I want to share a real-live case from one of our law firm clients
  • Nearly 4 years ago they were involved in a case, which required 2-3 months’ worth of traditional doc-doc review for a dozen lawyers/reviewers for the collected corpus
  • Three years later, that same data set required a re-review based on different arguments on appeal.  Using Autonomy’s analytics, including its concept clustering capabilities, the same dataset was re-reviewed in under 2 weeks with 3 reviewers. 

Aside from demonstrating the tremendous ROI that can be achieved with powerful analytics; the irony was the law firm was viewed as not using what others in the industry narrowly defined as TAR or “predictive coding.”  We agree.  Instead, they used a broader form of technology assisted review (TAR), enabled by analytics, allowing the law firm to deliver equal or better results, faster and at lower cost than traditional methods (or I would argue even the narrower view of “predictive coding”).  We believe real world uses cases and results should inform the definition, and from our perspective this was the epitome of TAR. 

 

My point is that sometimes, we in the industry get so hung up with a particular path, you get lost en route to the destination.  Our strategy has been to incorporate a broad set of analytics, including those that support “predictive coding”, but also many others that help gain insight into information.  Big Data is no different; the amount of data generated from structured and unstructured sources just makes analytics all the more important. 

 

Trying to constrain TAR into a couple very specific models is a mistake in our opinion.  The applications creating, dissemination, and storing information are simply evolving faster than the definition will support.    We need to provide a broad, flexible, and scalable framework to respond to use cases none of us can fully predict (pun intended).  Working with some great research out of HP Labs, we are in the process of incorporating even better methods to analyze and visualize information, and will further evolve TAR in our solutions to support this dynamic ecosystem.

 

  1. Information Governance:  For several years the industry has been talking about a trend toward proactively governing information, incorporating a set of capabilities of which eDiscovery is but one strand.  Gartner, IDC, Forrester, and many others have indicated the trend may finally be turning into a reality, and our own observations from clients would support the assertion.
  • Conceptually information governance makes perfect sense.  Most enterprises today must address potential legal/litigation obligations, along with a myriad of statutory and regulatory obligations around the retention, production, or disposition of information. 
  • The same piece of information that is subject to a retention obligation is often relevant to a regulatory request, investigation, or lawsuit. Of course the key will be to understand a piece of content; otherwise it will be very difficult to apply appropriate policies.

 

This is a focus area for HP Autonomy.  The underlying analytics we have allows us to apply policy to address several different obligations, and support applications across a common information access layer.  This is also an area and where we are focusing significant R&D within Autonomy, and HP more broadly. 

 

This is also where many solutions will fail.  First, moving from analyzing and indexing a subset of collected data in eDiscovery is very different than working in a living, breathing enterprise environment, 365x24x7.  Second, few eDiscovery providers have ever worked at enterprise scale.  Consider our compliant archiving and supervision/surveillance environments must appropriately index and archive 8-10 million objects per day, and run scores of supervision policies against that corpus, just for one client!  At the same time, these environments are simultaneously supporting extraction of +10TB monthly of content to produce for regulatory inquiries, investigation, and litigation (for individual clients).

 

Even at the scale noted above, we are often touching a fraction of the enterprise information that will ultimately find its way into a governance framework.  Many solution providers struggle with a few TB of nicely collected content; but governing dynamic information at scale is a whole different world many in eDiscovery have never visited.  We have explored a portion of this world, and are in the process of mapping out and building the solutions necessary to address the remainder.

 

Read Joe Garber’s post.

 

Leave a Comment

We encourage you to share your comments on this post. Comments are moderated and will be reviewed
and posted as promptly as possible during regular business hours

To ensure your comment is published, be sure to follow the Community Guidelines.

Be sure to enter a unique name. You can't reuse a name that's already in use.
Be sure to enter a unique email address. You can't reuse an email address that's already in use.
Type the characters you see in the picture above.Type the words you hear.
Search
About the Author
George Tziahanas leads product management and strategy for HP Autonomy’s Legal and Compliance Portfolio, including its Compliant Archiving, ...


Follow Us
The opinions expressed above are the personal opinions of the authors, not of HP. By using this site, you accept the Terms of Use and Rules of Participation