Big Data is here and it is here to stay. Retailers collect and analyze billions of customer purchases every hour, looking for buying trends to better target their marketing efforts. Credit card companies constantly analyze incoming transactions searching for indicators of fraud. Social media platforms curate, store, analyze, and sell data that is invaluable to advertisers. As WikiLeaks has demonstrated, governments scour, record, and analyze massive amounts of information daily, including data mined from their closest allies.
Big Data simply refers to the use of predictive analytics or certain other advanced methods to extract value from data. Accuracy in Big Data may lead to more confident decision-making, and better decisions can result in greater operational efficiency, strategic planning, cost reduction, and reduced risk.
Should in-house counsel care? Yes, because Big Data a) allows in-house counsel to be more effective, b) has the potential to introduce new compliance and legal risks; and c) can be an additional source of revenue for the client.
Recruiting firms have amassed reams of information correlating legal skills to legal support needs, leadership skills, and teamwork, and other criteria such as risk aversion, which are helpful to executives when building their in-house legal teams. Hiring to criteria other than technical excellence may lead to a better, more responsive, and engaged business and legal adviser who can speak both the language of business and the language of law.
Law firms have jumped firmly on the Big Data bandwagon. A firm’s use of predictive analysis should be part of the criteria used by in-house counsel in choosing an external partner. Law firms that partner with their clients to understand their data, to collect their own data, and bring that data to their clients in partnership will have an important competitive edge over those who don’t.
In-house legal departments often explore alternative billing arrangements, but they often fail to follow through, falling back instead on the traditional hourly billing model. Legal management firms have collected empirical evidence of the effectiveness of various contending alternative billing models. The reports, which can be purchased for a fee, provide valuable insights as to the relative effectiveness of a large spectrum of arrangements, thereby eliminating much of the guesswork. Some organizations provide very detailed report cards on law firms that enable in-house legal departments to play hardball when negotiating invoices and provide details as to which external legal firms offer rate reductions.
One of the most difficult challenges for in-house counsel is to decide how best to triage legal support between internal and external counsel. Overall, there appears to be a trend toward in-house keeping strategic, high-value-add legal work. Skyrocketing outside counsel fees, budget constraints, and technology-/data-driven solutions are prompting companies to handle a greater amount of legal work. Big Data is capable of mining clients for their legal needs, examining and interpreting legal invoice data, and analyzing performance reviews and other human resources data and can provide a “genetic road map” for much of the law department’s operations.
When coupled with human judgment, Big Data can provide a powerful tool to inform in-sourcing decisions. Insourcing low-complexity work can have a damaging effect on legal department morale and may be best reallocated to legal process outsourcing companies. Conversely, in-sourcing highly esoteric work is rarely cost effective (especially if an in-house “specialist” must be hired), limits law department flexibility, and increases a law department’s risk profile. By identifying and insourcing moderately complex legal work, a law department can maintain an intellectually engaged, flexible team that reclaims or retains legal work, driving savings and efficiency.
Venue shopping is a tactic that has long been employed by litigants to remove a matter to a jurisdiction deemed friendlier to one’s side, based on past performance. Jury selection has evolved from an art to a high science, incorporating psychological profiles and mock juries to identify the optimal approach before even entering the courtroom. Now firms are providing tools such as decision trees featuring probable timelines, decision points, and projected costs.
In the past, in-house legal departments were largely reactive. There was limited predictability in terms of the types of product liability cases that may be filed, their frequency, and their quantum. Big Data can now provide insights as to the number of likely product defects and expected damage claims based on customer service call histories, model numbers, and shipment dates and the like, as well as the jurisdictions in which they are likely to be launched. These insights allow counsel to develop proactive strategies, from product recall to settlement analysis to hiring defence counsel in advance of a first suit.
Certain data platforms forage through case law to offer lawyers advice on whether a motion will be approved or denied, or how a patent case, for example, is likely to be adjudicated or settled. Others provide analysis on expert witnesses, including insight into the kinds of cases those witnesses have participated in and the type of testimony they have offered. A large number of commercial organizations have curated and analyzed extremely detailed information regarding the proclivities of a given judge, bench, or judicial district, all of which are available for a modest fee.
Big Data allows companies to better understand their customers, optimize their supply chains, and make better decisions affecting their business. These new tools, however, come with new risks. In-house counsel should be aware of how their organizations are using Big Data, and ideally should be involved in their setup and execution. The challenge is that in the rush to deploy new Big Data systems, compliance and legal risks will not be identified or may be ignored.
The staggering growth of Big Data over the last decade has created important privacy concerns as personally identifying information is collected, stored, used, and sold. The degree of penetration into one’s personal information is often not well understood by the individuals from whom the information is collected. Information is often stored well past the expiry of the purpose for which the information was first collected. When purchasing and using predictive analysis, in-house counsel needs to not condone any practices that may be illegal from a privacy perspective.
Many laws relating to the collection, storage, and use of personal information are antiquated. The rules applied to paper documents and single platforms such as e-mail. The company-to-client data stream is now much wider, including Facebook, Twitter, e-mail, and many other systems. The old rules don’t apply very well. In-house counsel needs to be aware of impending legislative changes, case law that may extend the reach of existing laws, as well as “soft law” that may deem certain practices illegal through the use of interpretation bulletins, policy manuals, and the like.
Big Data uses vast data sets and complex, computer-driven analysis beyond the comprehension of a single individual. These Big Data-based decisions place significant trust into automated systems. An overreliance on predictive analysis and disregard to good, old common sense and practicality may turn out to be disastrous. Recent financial crises demonstrated that certain financial models used by leading firms turned out to be very wrong. The early failure of Big Data in the financial industry caused many organizations to pull back, refine their methodologies, better balance their reliance on predictive analytics, and create checks and balances involving human intervention.
The legal department budget is often seen as a major cost centre for many corporations. Legal departments are often in search of working with other budget centres to identify new revenue opportunities that will benefit the client overall. The licensing or sale of data analytics can provide an interesting source of new revenue. Depending on the company’s industry, products, and customers, the distribution of data generated by a client can command a high price from third parties such as customers, suppliers, academic institutions, and the like.