The Securities Litigation Expert Blog

How Big Data is Changing the Legal Services Landscape

Posted by Jack Duval

May 13, 2013 4:07:41 AM

Big law firms and their corporate clients are engaging in a kind of data warfare, where each is analyzing massive databases of legal billing data and using them in negotiations.  (ABA)  Some highlights include:

Lexis Advance MedMal Navigator:  This product allows you... to determine in 20 minutes - versus 20 days - if a case is worth taking on... and it gives them analysis on available expert witnesses, including insight into the kinds of cases those witnesses have participated in and the type of testimony they offered.

TyMetrix LegalView:  The service aggregates the invoices of tens of billions of dollars of legal spending on an ongoing basis ... Many have used our LegalView data warehouse to compare their rates and understand the best way to position themselves with clients - low-cost provider or high-end value player.

Sky Analytics:  (advises) users on whether to approve, reject or reduce increases in hourly rates requested by outside law firms.  The primary variables the tool analyzes are an attorney's years of experience, his or her position in the firm, the law firm size and the cost of living where the attorney is based.

In five years, I think the entire legal services ecosystem will look different than it does today.  Those law firms that can run themselves like a business will succeed and those that hold on to the billable hour will suffer.
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Topics: big data, Statistics, Data Analysis, litigation, legal services, analytics, Law Firm Analytics

Big Data for Big Cities

Posted by Jack Duval

Feb 25, 2013 4:37:43 AM

The internet of things is coming to a city near you.  The New York Times had an interesting article on how real-time data of a cities traffic, water, and energy use can be used to reduce its resource consumption by up to 50 percent. (NYT)

NYU has started a program called the Center for Urban Science and Progress to study the "science of cities".  This is just one more example of how New York is becoming of of the dominate technology hubs on the planet.

The initiative at N.Y.U. is part of a broader trend: the global drive to apply modern sensor, computing and data-sifting technologies to urban environments, in what has become known as “smart city” technology. The goals are big gains in efficiency and quality of life by using digital technology to better manage traffic and curb the consumption of water and electricity, for example. By some estimates, water and electricity use can be cut by 30 to 50 percent over the course of a decade.

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Topics: big data, Statistics, NYU, Complexity, Predictive Analytics, NYT, education

SEC Using Algos to Detect Fraud

Posted by Jack Duval

Feb 19, 2013 2:51:31 AM

The SEC is using algorithmic methods to analyze financial statements from public companies to detect fraud.  (WaPo)

 (the) software package... will stream real-time trade data from the exchanges into the agency’s headquarters. Rather than build the technology from scratch at great expense, the agency purchased it from a New Jersey firm called Tradeworx. The project, called Market Information Data Analytics, or MIDAS, is in the final testing phases.

The SEC has used similar programs to analyze hedge fund returns.  See our previous coverage of SEC uses of big data techniques here.
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Topics: big data, Statistics, Data Analysis, fraud, litigation, analytics, SEC, Compliance, regulation., Predictive Analytics, Analytic Talent

Real-Time Big Data Could Be Next

Posted by Jack Duval

Nov 29, 2012 3:40:00 PM

This blog post continues our expert analysis of complex investments and their regulation.

The New York Times Bits Section has a nice article on Jeff Hawkins and his ideas on real-time big data analysis.  (NYT)  Here's the crux:

“It only makes sense to look at old data if you think the world doesn’t change,” said Mr. Hawkins. “You don’t remember the specific muscles you just used to pick up a coffee cup, or all the words you heard this morning; you might remember some of the ideas.”

If no data needs to be saved over a long term and real-time data can stream in all the information that is needed, a big part of the tech industry has a problem. Data storage companies like EMC and Hewlett-Packard thrive on storing massive amounts of data cheaply. Data analysis companies including Microsoft, I.B.M., and SAS fetch that data and crunch the history to find patterns. They and others rely on both the traditional relational databases from Oracle, and newer “unstructured” databases like Hadoop.

Much of this will be a relic within a few years, according to Mr. Hawkins. “Hadoop won’t go away, but it will manage a lot less stuff,” he said in an interview at Numenta’s headquarters in Redwood City, Calif. “Querying databases won’t matter as much, as people worry instead about millions of streams of real-time data.” In a sensor-rich world of data feeds, he is saying, we will model ourselves more closely on the constant change that is the real world.

If true, this would be a paradigm shift.
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Topics: big data, analysis, Statistics, Data Analysis, Jeff Hawkins, real-time, Complexity, Predictive Analytics, NYT, Analytic Talent

Big Data and Discovery Post-Rambus

Posted by Jack Duval

Nov 26, 2012 2:25:58 AM

Mary Mack, writing in The Metropolitan Corporate Counsel, has a nice piece on big data discovery after the Rambus decision.  (TMCC)  Below are some key take-aways.

Here's the problem in a nutshell:

After a very expensive e-discovery exercise, one legal department requested a report on files that were a year past their retention date and that were categorized as nonresponsive to litigation. They found that more than $5 million was spent reviewing those documents – documents that could have been disposed of well in advance of litigation. Other companies, large and small, are intimidated by the file shares and SharePoint sites that have no accountable steward that grow unmonitored.  Almost all companies have at least one legal hold, and with no one accountable to ask, the file shares and SharePoint sites might be assumed to hold potentially relevant documents.

Regarding the Rambus decision:
The recent Rambus case (Hynix Semiconductor, Inc. v. Rambus, Inc., No. C-00-20905 RMW (N.D. Cal. Sept. 21, 2012)) provides guidance to avoid spoliation of evidence charges. The disposition should not take place as part of a litigation plan. Records and data should not be destroyed wholesale when litigation is reasonably foreseeable. The officer in charge of destruction should understand and be accountable for the enterprise’s litigation hold and preservation responsibilities. The disposition of records should be content neutral and apply to a wide variety of documents, not only documents that are frequently responsive to a certain type of litigation. The motivation should not be to dispose of potentially harmful documents. A record should be kept of what was destroyed, by whom and when, with an accounting of the retention policy, the actual practice and the monitoring of results.

Hat tip Teddy Angelus.  (P|A)
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Topics: e-discovery, big data, litigation, discovery, Rambus, Mary Mack, The Metropolitan Corporate Counsel, Compliance, regulation.

Nate Silver Called All 50 States...

Posted by Jack Duval

Nov 10, 2012 11:47:01 AM

and was only off by 400,000 on the popular vote.  How ya like big data now?  VentureBeat has the story. (VB)

See out previous coverage of Nate Silver here.

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Topics: presidential election, big data, Statistics, Data Analysis, Nate Silver, Predictive Analytics

Data Scientists and the New Cool

Posted by Jack Duval

Sep 30, 2012 4:40:45 AM

Tom Davenport has an excellent mid-lenth piece out in the Harvard Business Review about how data science is the new sexy job.  Tom has been writing about this for quite some time.  (HBR)  Of particular note was his description of the Insight Data Science Program, which is a post-doc Silicon Valley feeder five week training program. (IDSP)

What is clear is that this relatively new discipline is still nascent and does not have a formal academic domain yet.  In truth, that makes it more interesting, as it brings in talent from the whole spectrum of quantitative disciplines.

Here's an excerpt:

What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.

Data scientists’ most basic, universal skill is the ability to write code. This may be less true in five years’ time, when many more people will have the title “data scientist” on their business cards. More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.

But we would say the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested. This often entails the associative thinking that characterizes the most creative scientists in any field. For example, we know of a data scientist studying a fraud problem who realized that it was analogous to a type of DNA sequencing problem. By bringing together those disparate worlds, he and his team were able to craft a solution that dramatically reduced fraud losses.

Perhaps it’s becoming clear why the word “scientist” fits this emerging role. Experimental physicists, for example, also have to design equipment, gather data, conduct multiple experiments, and communicate their results. Thus, companies looking for people who can work with complex data have had good luck recruiting among those with educational and work backgrounds in the physical or social sciences. Some of the best and brightest data scientists are PhDs in esoteric fields like ecology and systems biology. George Roumeliotis, the head of a data science team at Intuit in Silicon Valley, holds a doctorate in astrophysics. A little less surprisingly, many of the data scientists working in business today were formally trained in computer science, math, or economics. They can emerge from any field that has a strong data and computational focus.


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Topics: data science, big data, Silicon Valley, Statistics, Data Analysis, Insight Data Science Program, Tom Davenport, Harvard Business Review, Predictive Analytics, Analytic Talent, education

Top Five Cities for Big Data Talent (It's not who you think)

Posted by Jack Duval

Aug 30, 2012 4:29:46 PM

CIOInsight is out with this list.  I'm not sure of the methodology, but it's interesting.  See the piece here.

1. San Francisco

2. McLean, Virginia

3. Boston

4. St. Louis

5. Toronto


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Topics: big data, Statistics, Data Analysis, CIOInsight, Cities, Predictive Analytics, Analytic Talent

Climate Corp. Combines Big Data, Predictive Analytics, and Insurance and Raises $50M

Posted by Jack Duval

Jun 15, 2012 6:28:22 AM

Climate Corp. is selling weather insurance to farmers based on data that the farmers provide to the company.  Climate Corp. then crunches the data (a cool trillion fields) and offers the farmers bespoke crop insurance.

Read the article here.

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Topics: big data, Data Analysis, insurance, Climate Corp., farmers, crop, Predictive Analytics

Big Data is Worth Nothing without Big Science

Posted by Jack Duval

May 16, 2012 2:02:10 AM

Here's a nice piece from CNET on how big data alone is not enough, you really need the skills to know how to pull the meaning from it.

That being said, it is getting easier to analyze big data.

Money Quote:

Last year, the McKinsey Global Institute projected that the United States alone needs 140,000 to 190,000 more workers with "deep analytical expertise".



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Topics: big data, Statistics, Data Analysis, big science, McKinsey, CNET, Predictive Analytics

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