Extract from Sahil Garg’s article “No Crystal Ball Needed: Predicting Litigation Outcomes Through Data Analytics”
Advancements in technology and AI have led to data analytics becoming an indispensable part of most services and industries. The legal industry has not been untouched by this, and law firms, governments and alternative legal service providers have also started utilizing data analytics to predict outcomes with remarkable results. In litigation, analytics is able to provide deep insights not previously possible during initial phases. Machine learning and AI are able to mitigate the challenges of information volume and unstructured data as they process and learn from masses of data in fractions of seconds and equip attorneys with the desired details needed to proceed.
There are many e-discovery providers that have adopted AI and infused it into their offerings. Their products process and consume terabytes of data on a day-to-day basis, which gives them a head start in data analytics and allows them to provide precise insights from the data with predictive categorization, sentiment analysis, personal information analysis and more. Data consultants and ALSPs are adept at leveraging these tools to assess data and provide lawyers with actionable insights at all stages of the litigation lifecycle.
Litigation data analytics covers a variety of techniques designed to analyze information to unlock deep insights from unstructured data using a variety of techniques
