Data Analytics and Law?

The last couple of decades have seen a dramatic movement from static paper records to the digitisation of data in all spheres of life. Along with the digitisation of traditional information (e.g. correspondence, medical records etc.) has come a vast amount of new data capture, showing the behaviour of internet users, how they engage with website content and - in the case of law firms - how prospective clients browsing legal information pages are drawn in by calls to action and convert to enquiries and new business.

One challenge many organisations face is how to effectively tap into all this data, find and analyse patterns and trends and translate this to business improvements. The term "Big Data" refers to these large sets of data, as well as the concept of computationally analysing them (also known as data analytics) and making sense of the vast reams of digital information. Law firms can use big data tools to improve their client knowledge and spot opportunities for offering additional legal advice (e.g. by linking a database of new legislation with their CRM system). Other big data techniques (explored further below) can help lawyers win cases by allowing them to analyse case law. The ability to process big data on-the-fly can be even more useful - which is where the concept of "fast data" comes in.

Fast Data vs Big Data

As increasing amounts of data are captured, the real advantage for many companies does not necessarily lie in analysing historic data, but rather in understanding the brand new incoming data and forming strategies to react to emerging patterns in real time. This use of instant data analytics (and the fast rate at which new data arrives) is known as "Fast Data". One example of a way in which law firms could use fast data analytics would be to spot emerging trends and to apply this knowledge to form winning legal strategies (see more on this below).

Predictive coding reduces costs – but how?

Big data techniques which take advantage of machine learning can be applied to certain routine legal tasks often undertaken by junior lawyers or paralegals. The electronic discovery process is one such task, where the application of artificial intelligence to big data can lead to significant time and cost savings. This method, known as “predictive coding” involves the analysis, identification and classification of large swathes of digital documents, by computer software, to determine which ones are relevant for purposes of disclosure. Predictive coding, which has recently received backing from the High Court, can save hundreds or thousands of man hours, allowing firms to reduce the overall expenditure of e-disclosure.

Data Analytics and Legal Strategies

Legal strategies are often formed with a thorough understanding of likely outcomes based on knowledge of results in previous similar cases. Although this has traditionally come down to professional experience, data analytics can also help to predict outcomes by analysing digitised case law and spotting patterns. One such big data tool is the Legal Analytics Platform from Lex Machina which uses AI techniques such as natural language processing and machine learning to mine litigation data and predict the outcomes of cases. As lawyers increasingly get involved with technology, using data analytics tools will become second nature.

Data Analytics helps with business development

Aside from its application to legal work itself, data analytics can also assist with a range of business development techniques. CRM databases can be mined for client information, allowing the identification of up-selling opportunities (e.g. a business which previously sought IP advice may now need employment law advice as a result of changing regulations). As mentioned before, new blogs and social media content can be generated on the basis of popular web pages – and, conversely, analysis of social media data can be used to gain an understanding of trends. One of the challenges of mining client data lies in tackling “unstructured data” where disparate data is spread across different databases – but, as witnessed by the insurance industry, it can be extremely useful to implement big and fast data techniques to unify and make sense of all this data.

This article was written by Rajdeep Dutta, Head of Redwood Knowledge Centre.