Visualizing Data in a Predictive Coding Project – Part Three

By Ralph Losey

This is part three of my presentation of an idea for visualization of data in a predictive coding project. Please read part one and part two first. This concluding blog in the visualization series also serves as a stand alone lesson on the basics of math, sampling, probability, prevalence, recall and precision. It will summarize some of my current thoughts on quality control and quality assurances in large scale document reviews. Bottom line, there is far more to quality control than doing the math , but still, sampling and metric analysis are helpful. So too is creative […]

Read the complete article at: Visualizing Data in a Predictive Coding Project – Part Three

Location. Location. Location.

By Craig Ball

I’m peripatetic. My stuff lives in Austin; but, I’m in a different city every few days. Lately looking for a new place for my stuff to await my return, I’m reminded of the first three rules of real estate investing: 1. Location; 2. Location and 3. Location. Location has long been crucial in trial, too: “So, you claim you were at home alone on the night of November 25, 2014 when this heinous crime was committed!  Is that what you expect this jury to believe?”   If you can pinpoint people’s locations at particular times, you can solve crimes. […]

Read the original article at: Location. Location. Location.

Your ROI Is Coming Out of My Pocket

By Michael Simon

Our modern lives are filled with black boxes, things that we understand in terms of the inputs they require (click the mouse, turn the wheel, insert slice of bread) and the outputs we receive (your computer beeps, your car turns, you get toast!).  Yet between the input and the output there are a whole bunch of things happening that we can’t see, can’t explain, and – most importantly – don’t actually need to explain to accomplish our desired task.  As long as the inputs are understandable and the outputs are what we expect, what lies in between can be completely opaque.  I don’t need to know how my toaster works, as long as I get my toast.

So why is the fact that machine learning (a/k/a “predictive coding”) is a black box such a problem?  Is it because human review of documents (i.e., an eyes-on-all-docs full review) is somehow more transparent?  Of course not.  We have study after study of the greater accuracy and effectiveness of review assisted by machine learning (when used properly).

Read the complete article at: Your ROI Is Coming Out of my Pocket

2014 eDiscovery Trends: Survey Results

By Michele Lange

With about six weeks remaining in the year, let the “2014 reflections” bombardment begin! You know what I am talking about — the close of the calendar year prompts oodles of nostalgic news stories recalling the biggest events of the year. Okay, I will admit it…about this time last year, I willingly clicked on Google’s Top Ten Trending Stories of 2013 and BuzzFeed’s The 27 Movies We Loved in 2013. There is just something about this time of year that makes us want to ponder the past.

So, wholeheartedly jumping in to the “year in review” spirit, Kroll Ontrack surveyed over 550 law firm and corporate ediscovery professionals to gauge the biggest trends and impacts in ediscovery in 2014. This was a great year for the world of ediscovery, and now is the perfect time to share some of the interesting 2014 trends with all of you. To see the full set of ediscovery trends, please download the “2014 Ediscovery Trends: Industry Survey Results” guide.

Read the complete article at: 2014 eDiscovery Trends: Survey Results

Visualizing Data in a Predictive Coding Project – Part Two

By Ralph Losey

This is part two of my presentation of an idea for visualization of data in a predictive coding project.Please read part one first.

As most of you already know, the ranking of all documents according to their probable relevance, or other criteria, is the purpose of predictive coding. The ranking allows accurate predictions to me made as to how the documents should be coded. In part one I shared the idea by providing a series of images of a typical document ranking process. I only included a few brief verbal descriptions. This week I will spell it out and further develop the idea. Next week I hope to end on a high note with random sampling and math.

Vertical and Horizontal Axis of the Images

The visualizations here presented all represent a collection of documents. It is supposed to be pointillistimage, with one point for each document. At the beginning of a document review project, before any predictive coding training has been applied to the collection, the documents are all unranked. They are relatively unknown. This is shown by the fuzzy round cloud of unknown data.

Read the complete article at: Visualizing Data in a Predictive Coding Project – Part Two

TAR in the Courts: A Compendium of Case Law about Technology Assisted Review

By Bob Ambrogi

Magistrate Judge Andrew Peck It is less than three years since the first court decision approving the use of technology assisted review in e-discovery. “Counsel no longer have to worry about being the ‘first’ or ‘guinea pig’ for judicial acceptance of computer-assisted review,” U.S. Magistrate Judge Andrew J. Peck declared in his groundbreaking opinion in Da Silva Moore v. Publicis Groupe . Judge Peck did not open a floodgate of judicial decisions on TAR. To date, there have been fewer than 20 such decisions and not one from an appellate court. However, what he did do — just as [...]

Read the complete article at: TAR in the Courts: A Compendium of Case Law about Technology Assisted Review

Turkeys, Thanksgiving and Predictive Coding (Cartoon and Clip)

The Cartoon and Clip of the Week for November 14, 2014

Daily we read, see and hear more and more about the opportunities, challenges and concerns associated with predictive coding. This week’s cartoon and clip highlights a visual depiction of two knowledge workers taking a random sampling approach to predictive coding (cartoon) and some considerations for thinking about the challenges associated with textual analytics-based technology-assisted review platforms. (clip).

---

Text and Non-Text Files in Information Governance and eDiscovery

When you are evaluating information governance and electronic discovery solutions do you ask your vendor/service provider the basic questions of:

1) Does your system or process identify both textual and non-textual ESI files?

2) How does your system or process index and classify non-textual ESI files? (Example: Image only PDFs.)

3) How does your system or process identify text within non-textual ESI files? (Example: Graphics with words published to an image only PDF.)

If your vendor/service provider cannot adequately answer these three simple questions, then you may want to consider the potential risk and exposure associated with not fully considering non-textual ESI in your information governance and eDiscovery efforts.  Additionally, if your vendor is relying on a non-automated process for classifying non-textual files, then you may not be using the most efficient approach for your information governance or eDiscovery efforts.

Click here to find a running listing of some of the latest postings on the topic of Technology-Assisted Review.

Understanding the Chief Data Officer Role

By Gartner Research

Organizations may build their businesses on data, but they don’t necessarily manage it well. That’s why Chief Data Officers (CDO) can play a valuable role in helping the organization value its data across the enterprise. CDOs particularly are on the rise in regulated industries and Gartner predicts that 50% of all companies in regulated industries will have a CDO by 2017, according to Debra Logan, vice president and Gartner Fellow, in her session at Gartner Symposium ITxpo. CDOs Help Manage Data as a Corporate Asset “There is no coherent leadership strategy around corporate assets,” Ms. Logan said. Organizations [...]

Read the complete article at: Understanding the Chief Data Officer Role

Archiving in the Enterprise: New Magic Quadrant From Gartner

Published on November 10, 2014, the new Gartner Magic Quadrant for Enterprise Information Archiving(G00262936) provides information technology and business professionals with information and insight into solutions available to meet compliance and eDiscovery challenges while reducing primary storage costs.

This new magic quadrant is authored by leading Gartner experts to include:

• Alan Dayley (Research Director)
• Garth Landers (Research Director)
• Anthony Kros (Research Analyst)
• Jie Zhang (Research Director)

While the complete report is available for purchase from Gartner, general information from the magic quadrant is typically shared by mentioned vendors and media professionals in announcements, commentaries and news stories.

Provided below is the listing of representative enterprise archiving vendors from the report and, where available, vendor public comments on their mention in the magic quadrant.

Enterprise Information Archiving Vendors (Alphabetically Ordered with Links)

• Actiance
• Artec IT Solutions
• Barracuda Networks (Message Archiver)
• Bloomberg (Bloomberg Vault)
• CommVault
• EMC (SourceOne)
• Global Relay
• Google (Vault)
• GWAVA
• HP
• IBM (Content Collector)
• Message Solution
• Metalogix
• Microsoft
• Mimecast
• OpenText
• Proofpoint
• Smarsh
• Symantec
• ZL Technologies

Report Mentions from Representative Vendor Websites

From the Actiance Website

We are pleased to announce that Actiance, the creator of Alcatraz, was included in the Magic Quadrant for Enterprise Information Archiving.

From the GWAVA Press Release

Gartner has stated, “By 2019, 75% of organizations will treat archived data as an active and “nearline” data source, and not simply as a separate repository to be viewed or searched periodically, up from less than 10% today.”

Found within the Magic Quadrant, Gartner goes on to say, “GWAVA is one of the few vendors that offers native Gmail archiving support. Retain has the ability to search and take action on outside data sources and to conduct eDiscovery activities across any connected dataset.”

From the Proofpoint Website

“Broader information governance concerns (regulatory compliance, business-focused retention and deletion of data, and managing aging data based on a clear understanding of its value) are beginning to surpass e-discovery as the primary driver for deploying EIA.”

Gartner, Inc. positions Proofpoint in the Leaders Quadrant in its 2014 Magic Quadrant for Enterprise Information Archiving.

Public Domain Sources:

• Gartner Research
• Selected Vendor Announcements/Releases

Are You Paying Too Much for eDiscovery Processing?

By Lexbe

Lexbe provides eDiscovery software and services for legal professionals at law firms, corporations, and government agencies.  

“Free eDiscovery Processing” sounds too good to be true. Until now, you may have spent many hundreds of dollars per GB to process native documents like Outlook Email and Microsoft Office files into paginated reviewable formats like TIFF or PDF. So how can those charges simply disappear? The revolutionary answer lies with Lexbe’s secure, scalable cloud technology.

Click here for a demonstration of Lexbe eDiscovery Platform features, a 15 day trial, or for any additional information.

To learn more about Lexbe, visit Lexbe.com

Visualizing Data in a Predictive Coding Project

By Ralph Losey

Soon all good predictive coding software will include visualizations like this to help searchers to understand the data. The images can be automatically created by computer to accurately visualize exactly how the data is being analyzed and ranked. Experienced searchers can use this kind of visual information to better understand what they should do next to efficiently meet their search and review goals.

Read the complete article at: Visualizing Data in a Predictive Coding Project

The Pendulum Swings: Practical Measurement in eDiscovery

By Herbert L. Roitblat, Ph.D

Until a few years ago, there was basically no effort expended to measure the efficacy of eDiscovery. As computer-assisted review and other technologies became more widespread, an interest in measurement grew, in large part to convince a skeptical audience that these technologies actually worked. Now, I fear, the pendulum has swung too far in the other direction and it seems that measurement has taken over the agenda. Some of the early reported cases involving disputes over the use of predictive coding and some proselytizing by pundits, including probably me, have convinced people that measurement is important. But we risk losing sight of the really important problem, that is good quality eDiscovery.

Read the complete article at: The Pendulum Swings: Practical Measurement in eDiscovery 

Is Social Media Risky? Organizational Considerations (Cartoon and Clip)

The Cartoon and Clip of the Week for November 7, 2014

Daily we read, see and hear more and more about the organizational risks associated with social media use. This week’s cartoon and clip highlights a unique approach to dealing with social media risk (cartoon) and some considerations for thinking about and evaluating organizational risk and cost related to social media (clip).

---

---

Organizational Risk and Costs

While the benefits of social media use in the workplace can be great, the risks associated with social media usage by organizational employees can also be great.   These risks, many times grouped according to their origin as a data risk, a behavior risk, and/or a technology risk, can have a significant impact on key organizational areas to include but not limited to:

• Revenue: The potential for organizational revenue loss based on reputation damage and confidential information exposure.
• Productivity: The potential for organizational productivity loss based on too much time spent on social networks and use of social networks to undermine management by circumventing established hierarchy and workflow patterns.
• Security: The potential organization information system security compromise based on the introduction of malware into technology systems and uncontrolled exchange of data.

A Simple Framework for Considering Social Media Risk

In order to appropriately evaluate and address potential social media risk within an organization, its important to have a simple and understandable approach from which to begin considering an organization’s social media landscape.   While there are many tactics and techniques for evaluating and addressing potential social media risks, the following four steps may provide a useful and overarching framework for beginning to consider social media risk:

Detection:

• Do you have a risk related to social media? (Example:  Potential or Actual Risk)
• Have you done an impact analysis on how the social media risk might impact the organization?  (Example:  Acceptable or Unacceptable Risk)

Identification:

• Have you identified the social media networks that might contribute to social media risk with the organization?  (Example:  LinkedIn, Facebook, Twitter)
• Have you identified employees that may be using social networks in the workplace?  (Example: Individuals, Workgroups, Departments)

Location:

• Have you determined the location where social networks are being accessed?  (Example:  Inside Corporate Firewall, Outside Corporate Firewall)
• Have you established policy or guidance addressing the access of social networks by employees?  (Example:  Social Media Usage Policy, Corporate Communication Device Usage Policy)

Reporting:

• Do you have a system in place to monitor usage of social media networks?  (Example:  Active Technology Monitoring, Passive Human Sampling)
• Have you established an individual, workgroup, or department as the lead in assessing social media usage reports?  (Example:  Director of Human Resources, Office of Compliance, IT Department)

While there are many additional considerations that could be added to this short listing, the benefit of the framework is that it can help you get started evaluating and addressing social media risk in an intentional and proactive manner.

Selecting an eDiscovery Vendor is Like Buying the Right Car – but Don’t Forget about the Dealership

By Andreas Mueller

In a sea of 600+ e-discovery providers in the US alone, trying to find the right vendor to meet and fulfill your requirements is difficult. Like purchasing a car, you have a choice of vendors that range from local, to regional and national providers. Some that use their own technologies, others that use off-the-shelf products and a few others that provide traditional processing and hosting services spawned from the paper world.

This article will walk you through a decision making process to provide guidance when evaluating e-discovery vendors that best meet your current and anticipated needs.

Read the complete article at: Selecting an e-discovery vendor is like buying the right car – but don’t forget about the dealership

Records Management and Unstructured Data: File Analysis Paralysis? (Cartoon and Clip)

The Cartoon and Clip of the Week for October 31, 2014

Daily we read, see and hear more and more about the challenges of managing unstructured data stores in information governance and eDiscovery. This week’s cartoon and clip highlights a unique records management challenge faced by many corporations today (cartoon) and a research firm’s aggregated listing of 28 file analysis software vendors than can contribute to increased efficiency in information management decisions for unstructured data (clip).

---

---

Reduce File Analysis Paralysis

Click here to learn more about solutions for unstructured data file analysis .