Information Governance (InfoGovernance) is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of information to enable an organization to achieve its goals. Information governance should be an element in planning an enterprise's information architecture.

(Gartner Hype Cycle for Legal and Regulatory Information Governance, 2009, December 2009).

An Engagement Area (EA) is an area where the commander of a military force intends to contain and destroy an enemy force with the massed effects of all available weapons systems.

(FM 1-02, Operational Terms and Graphics, September 2004).

Monday, March 23, 2015

Do smart machines require ethical programming?

From Help Net Security
Realizing the potential of smart machines — and ensuring successful outcomes for the businesses that rely on them — will hinge on how trusted smart machines are and how well they maintain that trust. Central to establishing this trust will be ethical values that people recognize and are comfortable with. “Clearly, people must trust smart machines if they are to accept and use them,” said Frank Buytendijk, research vice president and distinguished analyst at Gartner. “The ability to earn trust must be part of any plan to implement artificial intelligence (AI) or smart machines, and will be an important selling point when marketing this technology. CIOs must be able to monitor smart machine technology for unintended consequences of public use and respond immediately, embracing unforeseen positive outcomes and countering undesirable ones.”

Friday, March 20, 2015

Ending the Debate on TAR Seed Sets

By Hal Marcus
In the wake of Judge Peck’s recent Rio Tinto opinion on technology assisted review, the ediscovery blogosphere has been repeatedly quoting its bold pronouncements that judicial acceptance of TAR “is now black letter law” and that “it is inappropriate to hold TAR to a higher standard than keywords or manual review.” And rightly so — these statements appear intended to put outdated predictive coding debates to rest once and for all. Yet a good deal of the focus is going to the question Judge Peck raises but does not fully resolve: whether disclosure of TAR seed sets may be required.

‘Have you Taken Leave of Your Senses?': Top 10 Takeaways from 2015 LegalTech Judges Panel

By Tamara Emory
At this year’s Legal Tech, I once again had the honor of moderating the Judges Panel, on which Judge John Facciola (D.D.C., retired), Judge Andrew Peck (S.D.N.Y), Judge Frank Maas (S.D.N.Y), and Judge Elizabeth Laporte (N.D.Cal.) presented.  This time, we had a provocative topic (or, perhaps –as Judge Peck put it–, a depressing one): “What’s Wrong with Discovery?”  The judges had plenty of insight into why discovery has become risky and expensive, what causes attorney misconduct in discovery, and implications for access to justice.  Below are ten highlights of that discussion.

Wednesday, March 18, 2015

Beyond the Abstract: Considering Investing and Investors in eDiscovery (Cartoon and Clip)

Regularly we read, see and hear more and more about mergers, acquisitions, investments and investors in the business of electronic discovery.  This week our cartoon and clip features an abstract look at investing in eDiscovery (cartoon) and two quick reference links that highlight merger, acquisition and investment activities from both an activity level and an investor level (clip).


The Business of eDiscovery: Two Mentions of Interest

Provided below are links to two recent updates related to mergers, acquisitions, investments and investors in eDiscovery.  
  • Mergers, Acquisitions and Investments in eDiscovery:  Provided as a non-comprehensive overview of over 100 key and publicly announced eDiscovery related mergers, acquisitions and investments since 2001, the following listing highlights key industry activities through the lens of announcement date,acquired company, acquiring or investing company and acquisition amount (if known).
  • A Short List of eDiscovery Investors:  Provided as a short list of 30+ investment organizations that have funded eDiscovery-related companies between 2009 and today.
Click here to follow all ComplexDiscovery cartoons and clips.

Tuesday, March 17, 2015

Boosting PII Detection and Protection in “Unstructured” Content

By John Martin
Because of the significant reputational and financial consequences of failing to protect content containing personally identifiable information (“PII”), corporations and governmental agencies have made it a major goal to identify and protect such content.
Privacy expectations arise from a number of laws in different jurisdictions and are sometimes referred to by various acronyms such as HIPAA or PCI, but we will refer to them collectively in this posting as “PII.”
One of the most challenging aspects to identifying and protecting PII is how to deal with “unstructured” content, i.e., with documents or files on file shares, personal computing devices, and content management systems. These files can be generated within and outside the organization using many applications, can be converted to multiple file formats (most commonly to PDF), and seemingly have unlimited form and content.

2015 Big Data and Analytics Survey

By IDG Enterprise
The 2015 Big Data and Analytics study highlights data-driven initiatives and strategies driving data investments within IT organizations. In order to gain a deeper understanding of organizations’ big data goals and tactics, the research shows data deployment trends, future investment growth and opportunities for vendors.

Wednesday, March 11, 2015

TAR 2.0 and Text Analytics: More Fast Forward to the Past?

By John Martin
Text analytics does some remarkable things with what it’s able to see, but in one critical aspect it is a giant leap backwards to the days of telegraphs and stock ticker tapes when information was delivered on continuous strips of paper with just numbers, letters, and basic punctuation printed on them. In those days, the long strips of paper could be cut into pieces and taped or pasted together to form pages. Nowadays, text analytics takes documents and cuts them into short strips of characters and punctuation, and assembles the short strips into continuous text strings.  A ten-page document becomes the equivalent of a 100 foot-long ticker tape.
For text analytics, a word is a word. Words have a one-dimensional order with each word either in front of or behind other words on this virtual ticker tape. There are no concepts like logos, graphics, form lines, signatures, page orientation, and how things are arranged or placed on a page – the sort of thing that authors spend long hours composing and adjusting to convey the correct meaning.
Now in point of fact, text analytics is often able to do some pretty useful things with the information that it is able to see. But keep in mind that it is inferring meaning from just the text it is able to see. Visual classification is able to use a much richer set of data on which to base it’s analysis – what documents actually look like.