The effective use of "data analytics"1 is quickly changing the legal landscape and the practice of law, for the better. This is a fast-changing area where today's "use cases" will be quickly superseded by new and more powerful uses of these technologies. The list below summarizes ten key areas where in-house counsel may consider the use of data analytics either as a solely in-house measure or in connection with engagements with outside counsel.
1. Meeting E-Discovery Search Requirements
Analytics in the form of predictive coding (also known as "technology-assisted review" or "TAR") means that savvy in-house counsel are now able to be more efficient in searching through the huge and ever-increasing volume of documents in the form of electronically stored information (ESI) in corporate data repositories, in response to discovery demands. In-house legal departments can use TAR methods to deal with several facets of litigation, including with respect to performing an early case assessment at the very outset of a case (or even prior to its filing with early notice); using TAR tools to perform the culling down of documents; and performing document analysis to zero in on the most relevant documents as well as to handle filtering for privilege. The latest analytics software packages also provide for graphical views for the visualization of data in a multitude of ways.
2. Actively Carrying Out State of the Art Records Management
The large volume of data in corporate environments may potentially overwhelm traditional recordkeeping functions, which continue to be necessary both for regulatory and compliance purposes, but also to fulfill ongoing business needs. In-house counsel would benefit from being on the front line in suggested automated ways to manage and preserve corporate records, including through the use of analytical techniques such as auto-categorization of email and other communications into existing records categories. Greater visibility into data sets in turn translates into an enhanced ability to prevent over-preservation of records. In a fully automated environment (where records are automatically kept in e-archives, for example), counsel will best be able to leverage the power of analytics to better search for and categorize information, as well as to perform remediation (disposition) as described below.
3. Engaging in Legacy Data Remediation
The power of data analytics to reliably classify content makes it optimal for data remediation efforts, an increasingly important component to effective information governance. In-house counsel can use analytics techniques to drive record retention policies, for the purpose of better identifying specific data categories that can - and should - be disposed of. In a world of corporate data that is effectively doubling every couple of years, in-house counsel will best serve the greater interests of the corporation by proactively taking the lead in ensuring that retention policies are (i) updated where needed to account for changes in technology platforms, (ii) known to employees in all constituent components, and (iii) fully executed. Increasingly, firms are using analytics software to review the contents of shared repositories (Sharepoint sites, shared directories and social collaborative platforms of all kinds) for the purpose of undertaking the remediation of legacy data.
4. Performing Corporate Due Diligence
Preparing a company for sale involves many people gathering information to present to the acquiring company to satisfy due diligence, and most of this gathering and preparation is done electronically. In-house counsel can use advanced data analytics to trace the compilation of targets' due diligence information, including the email, texts and other communications received surrounding these due diligence efforts. In doing so, counsel can learn more about the source and potential weaknesses of each disclosure, as well as internal disagreements within the target regarding the transaction. Armed with this intelligence, counsel is in a stronger position to renegotiate provisions within an acquisition agreement.
5. Undertaking Post-Merger Recuperation
In-house counsel can leverage data analytics to identify and assess post-merger recuperation of funds relating to a target company's failure to properly state its financial position during the merger process. Analytics may be used to establish a more reasonable valuation based on documents related to such areas as financial performance, customers, market share, receivables, and potential liabilities, especially where disagreements within the target's staff undermine the reasonableness of the previously stated acquisition price.
6. Initiating Whistleblower Investigations
Corporate clients can use advanced analytics for investigations, either in response to a regulatory inquiry or for purely internal purposes. Corporate clients are often faced with determining if, and to what extent, an allegation is true. In these instances, management often face uncertainty in estimating their company's exposure and/or how to remediate the situation -- while traditional investigation techniques crawl along. However, companies can use data analytics to strategically target the data that is most likely to identify important facts relating to the allegations. A skillful application of advanced analytics in the early stages of an investigation allows you to get to an answer quickly and accurately, which arms in-house counsel with the strategic advantage of knowing and understanding the company's position. Using analytics also increases the likelihood of an early resolution of the matter, generally at a fraction of the time and cost budgeted for eventual litigation.
7. Detecting a Kickback Scheme
Kickback schemes, also known as corporate bribery, are one of the most common types of highdollar corporate fraud -- yet are also often one of the most difficult activities for companies to detect. Because corporate kickbacks can take many forms - including money, goods, or services - it can be challenging and time-consuming for in-house counsel to locate and then piece together the particular data trails using traditional investigative methods. In-house counsel can however, employ data analytics techniques to quickly detect and analyze red flags of suspicious activity, such as undisclosed and/or unexpected relationships or irregularities in communication patterns between employees.
8. Finding Early Warnings of Employment Discrimination
Certain kinds of misconduct follow known patterns, and bad actors tend to undertake the same kinds of actions. For example, a person who is harassing or discriminating against others tends to undertake specific actions and use particular language in communications. This type of misconduct and others can be detected using an analytical technique known as "sentiment analysis," which extracts subjective information - such as the emotional state of the author - through analysis of textual source materials. In-house counsel should consider whether, depending on the culture of the organization he or she represents, as well as past history of litigation, it makes sense to devote resources to this type of early detection of potential bad conduct.
9. In-House Monitoring of Potential Data Breaches
We all live now in a post-Snowden, post-Sony world where just about every week brings fresh evidence of the complexity of securing IT environments from attack, both from the outside as well as from inside threats. The use of analytical software to monitor structured and unstructured content is growing, and in-house counsel should be aware of the capabilities of state of the art software to perform types of forensic analysis of the threat environment. In particular, the risk to the organization faced from insider threats - including employees uploading or walking out with intellectual property of the corporation - is still not completely understood or appreciated. In-house counsel involved in data breach incident teams may wish to review the options available on the market for detection software as part of a review of the corporate framework of internal controls.
10. Data Mining of Customer Information
Companies increasingly are using data mining techniques to understand and obtain insight into their customer base, in terms of household spending on goods and services, consumer shopping patterns, and other health and lifestyle information. Privacy issues increasingly will arise as companies consider selling consumer to third-party brokers, who in turn aggregate data in a manner so as to build extensive "dossiers" on the online consumer population. Most individuals, both inside and outside organizations, do not have a complete picture of how third parties may "connect the dots" with respect to particular individuals. In-house counsel should be diligent in inquiring what data mining is being undertaken, and what corporate uses of customer data are being made or envisioned, in order to weigh in on the privacy implications of their institution's practice. Counsel will then be in a better position to help craft defensible guidance to consumers providing informed consent with respect to the practices at issue.
1A useful definition of "data analytics" is "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and add value. . . . " T. Davenport & J. Kim, KEEPING UP WITH THE QUANTS : YOUR GUIDE TO UNDERSTANDING AND USING ANALYTICS ( 2013).
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Given the pace of change in this area, in short order there may be ten new topic areas of interest to in-house counsel involving the use of analytics. For the time being, counsel is well advised to learn as much as possible about the corporate IT environment, so as to better understand what issues may yet arise from existing and a myriad number of future analytics use cases.