"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.