Traditionally, teams of lawyers went through and digested large volumes of legal files, documentary evidence, and other pieces of ancillary information in order to make strong cases. Today, the explosion in the digital footprint of clients has resulted in the need to analyze mountains of both structured and unstructured digital data for relevant case details. While traditional reviewing methods may still work, the cost and time involved makes this exercise increasingly impractical. The harnessing of big data methodologies not only simplifies and automates knowledge discovery within legal data sets but also makes available novel ways of seamlessly joining seemingly disparate details into a cogent story. Here are some use cases
Even mid sized companies routinely send and receive thousands of e-mails daily. As such, in a corporate litigation scenario the number of e-mails that are to be reviewed could potentially number in the millions. Add to this the possibility of attachments such as word documents and spreadsheets, which would make any review task insurmountable. The use of algorithm aided big data search and classification techniques greatly simplifies the extraction of pertinent details and meaning from even these large data sets. For example, you could discover key players and important threads of interaction between them to make your position stronger, or reconstruct the exact chain of events that lead to a particular development of immense consequence to your client.
Being able to quantify the strengths and weaknesses of your case and even assigning a probability of winning is a huge potential source of triumph for big data. While technologies enabling this are still in their infancy, in principle this could be accomplished by algorithmically comparing, via different metrics, your case to similar cases in various other data bases.