Big Data Dip

John Edwards

Law Technology News

02-01-2013

Information is the fuel that drives law offices. Yet extracting intelligence out of the massive amounts of raw information — aka Big Data — now pouring into law offices from court records, emails, text messages, transcripts, Facebook posts, Twitter, and other sources requires both careful planning and the help of sophisticated predictive analytics software.

Predictive analytics draws on a variety of techniques — including statistics, modeling, machine learning, and data mining — to study data and make predictions about future events and trends.

"Big data in general, and predictive data analytics in particular, are the potential holy grail in the practice of law," declares Donald Wochna, chief legal officer at Vestige Digital Investigations, a computer forensics firm located in Medina, Ohio.

"Fast, high-performing data analytics can help enterprises and law firms harness expanding data collections to guide them on everything from finding profitable efficiencies to making important decisions in case strategy," adds Matt Gillis, vice president and managing director for litigation tools and professional services at New York-based LexisNexis. "It's not uncommon for attorneys to sort through and make sense of upwards of 300 terabytes of data when preparing for a case [and] the massive volume of data simply outpaces the capabilities of traditional technology tools to process that much information in a timely fashion."

Analytics in Action. Predictive analytics tools help lawyers make insightful connections that might otherwise have gone unnoticed. This ability can be particularly useful when performing electronic data discovery. "Predictive data analytics seeks to identify correlations in data that occur during the planning phase of unlawful conduct so that conduct can be interdicted before any damage is done," Wochna says.

Analytics products can also slash case preparation time and costs. "Part of the challenge in e-discovery is the prevalence of large amounts of unstructured data," Gillis says. Structured data is information that's organized inside a database or spreadsheet, which makes it easily identifiable and relatable to even the simplest analysis tools, he explains.

Yet unstructured data, such as emails, word processing documents, instant messages, tweets, blog posts and other digital communications, now constitutes most of the data flowing into law offices.

"Both types of data are subject to e-discovery during litigation, yet few organizations have the technical tools and expertise to apply the same degree of sophistication to the analysis of unstructured data as to structured data," Gillis says.

An emerging generation of big data management tools help attorneys gain control over virtually all types of data, allowing them to find information relevant to their case and determine exactly which data should be processed and reviewed. "In particular, open source analytics platforms have proven extremely fast at processing data," Gillis says. Open source technologies such as Apache Hadoop are highly efficient because they help make sense of information chaos. "They pull massive amounts of structured and unstructured data into a refinery system and break it down quickly so attorneys have quick and easy access to relevant information," he says.

Analytics tools specifically designed for legal applications are also available to help law offices efficiently track, manage, and review big data, says Sheila Mackay, senior director of e-discovery consulting at Xerox Litigation Services, based in Albany, N.Y. She notes that technology-assisted review products, for instance, use machine-learning techniques to automate the prioritization of documents for review based on how likely they are to be responsive to a particular matter.

"TAR may be appropriate for large volumes of data subject to discovery that would otherwise be cost- and time-prohibitive to review manually based on deadlines," Mackay says. Such approaches enable review managers to be more effective in allocating workflow to associate and contract review resources, achieve more consistency and optimize senior attorneys' time.

Analytics software can also help law offices optimize a variety of time-consuming business and management tasks, such as caseload distribution, revenue projection, fee forecasting and ?client data organization.

Dean Gonsowski is senior e-discovery counsel at Mountain View, Calif.-based analytics software publisher Symantec. He notes that when representing a client in a patent infringement suit, a law office could use analytics to develop a reasonable fee estimate by processing and analyzing data gleaned from its involvement in previous, related suits.

"In like manner, such an estimate could help the law firm project its revenue streams on that suit and assist with overall budget forecasts," he adds.

Getting Started. A law office considering a move into big data analytics should begin by taking a close look at the data it's currently storing and how that information is being used.

"A value-focused analysis will help determine what information should ultimately be kept and for how long," Gonsowski says.

Effective big data management and use begins with four basic steps, says Gillis. "Develop a strategy for information governance; establish rules for defensible deletion; prioritize data sets; and select best technology tools."

Mackay suggests creating a project management and oversight team. "It should be comprised of senior-level management, with representatives from IT," she notes. "Outside specialists, including consultants and e-discovery providers, can complement these teams by offering specific expertise."

She also recommends creating a ?culture of information governance. "The law office should establish a comprehensive structure that supports all of its data along with processes and roles that outline how data will be handled."

"Within such a structure, data can thrive as an asset rather than a liability." The structure, she says, should include a strategy for easily retrieving useful data, as well as data that has current business value, while avoiding a "keep everything" policy, which can actually make data a liability.

"Retaining information that has no big data potential threatens to turn big data into bad data, which merely increases risk," Mackay says.

For example, firms that stubbornly retain client electronically stored information gathered in e-discovery — even after a lawsuit has been resolved — are occasionally forced by subpoena to hand over that data. "Not only does this needlessly divert firm resources into e-discovery sideshows," Mackay says, "it negatively impacts client information retention policies implemented to defensibly delete that data."

While many analytics tools are designed for use by lawyers with little or no technical experience, only the very largest law organizations should attempt to find, install, and configure the necessary software without outside help.

"Lawyers are experts at law but not at technology," says John Tredennick, CEO of Catalyst Repository Systems, an analytics tools publisher headquartered in Denver. He notes that multiple federal court rulings have cautioned that certain aspects of e-discovery require specialized expertise in computer technology, statistics, linguistics, and other technical matters.

"Law firms are well advised to focus on their core capabilities and bring in outside vendors and consultants to assist with the technical aspects of handling big data," says Tredennick.

Mackay stresses the need to thoroughly document all steps. "A robust audit trail is imperative when a law office is called upon to defend and explain ... processes and decisions to a court or government agency, and can show good faith to comply with legal and compliance obligations," she says. Yet the biggest risk facing law offices, Mackay says, "is not having a well-thought-out and designed plan."