by Hal Marcus, eDiscovery attorney, Recommind
The LIBOR scandal has cast a spotlight on a growing trend in legal data analysis. Intriguingly, much of the notable evidence of banks manipulating interest rates came from communications delivered over an institutionalised chat system. With investigations into enterprise activities on the up – the European Union, among other governing bodies worldwide, has become more aggressive in its enforcement posture – corporations must contend with data types old and new to meet compliance requirements. Faced with an increasingly vigilant regulatory climate, organisations have no choice but to prepare for the legal ramifications of continually growing data volumes and sources.
While regulatory enquiries are never a welcome development to begin with, they are particularly troubling for organisations without adequate internal compliance programmes and tools to handle large volumes of company data for eDisclosure and internal investigations. In the face of such enquiries or lawsuits, identifying and presenting key communications is a critical issue. And as the LIBOR scandal shows, instant messaging (IM) conversations are now a substantial component of that need.
For many workers, daily use of IM platforms such as Skype, Google Chat and Instant Bloomberg is superseding email for casual exchanges. As organisations provide their employees with enterprise chat platforms to facilitate rapid business communications, they unwittingly create a growing headache for most eDisclosure platforms and, subsequently, the compliance officers and company lawyers needing to make sense of large volumes of chat data.
Putting IM in the spotlight
Unlike emails, IMs are replete with irregular formatting and extraneous metadata. Chat platforms like Bloomberg typically log and display every time a user enters or exits the platform, or the particular chat room or group. All of these entrances, exits, participant counts, timestamps, and other metadata can complicate the processing and display of IM data, making review more difficult. Again, unlike emails with their comparably convenient subject lines and breaks, IM conversations continue indefinitely and may wind though multiple topics, making reviewing chat data that bit more challenging.
IM conversations also present a unique problem in the highly unstructured data they contain. Successful spelling is hardly expected, grammar barely an afterthought, and quick jargon commonplace. While convenient for conducting business, such informality can leave keyword search relatively toothless as a means of finding what you need in an investigation.
Effectively managing your data
Legal teams have routinely struggled to find the information that matters among large volumes of chat communications across financial services organisations. In the case of extracting the information from those conversations that matter, sometimes buried in a mess of IM data, accessing it quickly and cleanly is paramount. For banks and companies in other highly regulated industries, a robust analytical eDisclosure tool that offers clear, rapid insight into every kind of enterprise data can be the difference between creating or damaging a perception that the company is working in good faith to comply with regulatory authorities.
Advanced analytics can help legal teams weed out crucial information faster and with greater accuracy, yet the complex metadata and other peculiarities of chat data have previously confounded such tools. New, intelligent processing of IM data, however, can turn this challenge on its head. After all, metadata is a double-edged sword: for whatever complexity it brings, it also offers valuable clues for fact investigations and useful data points for culling document volumes.
Staying ahead of the conversation
Presenting chat communications as clear, easy-to-read documents encompassing entire chat discussions can make for far more effective review and analysis. Pictorial visualisations can also help compliance and legal teams focus on the facts that matter faster, enabling them to see networks of communicators more easily. For tackling the issue of informal prose, phrase analysis can help. Concept analysis can look beyond individual search terms to the broader context in which they appear, grouping documents based on a broader analysis of their contents. Continuous machine learning can build on what you’ve already found, to suggest likely relevant documents for review from chat, along with email and the rest of your data.
If financial services companies are going to be ready in the face of increasing regulation and compliance demands, they need to face the growing popularity of IM as a communication method head on. With intelligent processing, clear document rendering, and advanced analytics, it is possible to efficiently cull and comprehend chat data, and given the prevalence of enterprise IM platforms today, eDisclosure technology must and can deal with chat data. This will give legal and compliance teams at financial services organisations a fighting chance in identifying the communications that matter.