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How Law Firms Can Capitalize on Big Data in the Digital Era

Historically, the practice of law has tended towards upholding tradition; it is not typically perceived as a cutting-edge field, certainly in the technological sense of the word, based on the gut instinct and acumen of individual litigators. But it is in the best interest of contemporary practitioners of law to embrace the increasingly digitized world, which ushers in unthinkable amounts of data detailing the communications, relations, locations, and activity of almost anyone connected to the Internet. Computers can store and analyze massive amounts of information concerning judges, Federal District Courts, case types, law firms, and any and all other relevant information to discover patterns or revelatory details, work previously performed by a firm’s employees, wasting hundreds of hours and resources poring over thousands of papers. The term “big data” refers to these datasets that are too large to be analyzed with traditional techniques and to the analytic tools used to dissect and divvy up that data. It is a fairly recent development, seeing as 90% of the data in the world was collected in the past five years; it is a momentous one, nonetheless. 

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The digital data revolution is a solution to the bulky and inefficient ways law firms once dealt with data. It promises increased productivity, optimized workflows, money-saving solutions, and the evaporation of anxiety wasted on small issues; it is simply a matter of whether or not we can keep up with and keep track of the amount of data being funneled into our hands. 


Litigation Analytics: A New Era for Data

Officially referred to as litigation analytics, the use of previously amassed data, processed through programs like Westlaw and Lexus, to inform case strategy and tactics has revolutionized the way lawyers look at the work they do. The ability to classify and organize exclusively relevant data saves money, hundreds of hours of manual labor, and expedites future casework exponentially. The kind of data analysis currently in practice is largely descriptive, as opposed to diagnostic, predictive, or prescriptive; it answers the question of what happened as efficiently as possible and allows litigators to make their case from there, instead of attempting the larger questions of why it happened, what will happen, and how it can be made to happen. 

Thanks to a myriad of analytic tools, the excavation of specific information within a dataset has become a lot easier. Machine learning, a form of AI that grew from statistics in which computers “learn” from a limited set of data inputs and algorithms, can help find the right documents for particular cases and subsequently find the right information within those documents. Based on previously recorded examples, firms can pair their pricing up against industry averages to determine costs, prove their efficiency in comparison to competitors, and predict the profitability of a given case; litigators can determine the scope of risk for any given decision, craft better litigation strategies, research more thoroughly, and better predict the time it will take a case to come to resolution.


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Legal officials can know for a fact the tendencies of a presiding judge or the case record and strategy pattern of opposing counsel. Firms can use analytics to track industry trends in marketing, social networking, and business development, and to counsel clients on human resources issues such as hiring practices, liability exposure, and compensation administration. But this method of information management comes with its own set of risks, of course.


Keeping Your Data Safe 

 In our contemporary age, keeping track of data and ensuring it stays out of the wrong hands is not an easy task. The legal industry is sensitive when it comes to data protection. Data that has been outsourced to a server or cloud is particularly vulnerable to theft. Liability for data breaches falls on the shoulders of legal firms themselves and could have disastrous financial and reputational ramifications. Security programs should include measures to prevent data theft and courses of action to identify, respond to, and recover from data breaches and security issues. It is vital that employees’ personal devices are encrypted, and that a firm have systems in place to support data restoration costs, often not included in cybersecurity plans. It is important, too, that companies not lose sight of their employees’ performance, and ensure all personnel are equipped with the proper training required to operate new systems put in place. Client data access should be governed strictly, while still remaining available to applicable employees whenever necessary, and all data should be backed up to third party servers. The American Bar Association’s Center for Personal Responsibility outlines its own rules of  professional conduct, which emphasize understanding the technologies your law firm employs, and more importantly, demonstrating sufficient efforts to properly secure and manage sensitive information. 

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While law firms are unlikely to ever reach a stage of data capacity mirroring that of Fortune 500 companies, they will be expected to offer informed advice on big data issues like risk aversion, ethics, and data security. Companies looking to minimize threats to their security need to maintain a balance between retention and destruction in terms of data storage. If the volume becomes overwhelming, the digitization no longer becomes cost-effective, and the firm loses out on one of the primary reasons for digitization in the first place. In addition, superfluous data that is no longer relevant or useful is simply sitting in your storage, waiting to be swiped.

The introduction of big data into the legal world is not about the replacement of the human element of labor with robots in suits, but instead about making better use of the vast quantities of digital information available in an applicable way to your business operations by optimizing workflows, digitalization, and providing an enhanced customer experience. 


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Categories: Legal, Data

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