Every law firm needs a legal data strategy. Data strategy is the future of law and having one can open a whole realm of advantages. A data strategy can help to measure, monitor, and manage resources and service providers; model and improve processes; allocate tasks across internal and external resources; allocate tasks across risk management strategies (such as ADR); assess cost and quality of services; and provide a clear picture of a firm’s performance to its board and clients.

But what is a data strategy? Today, we’re taking a slight detour from our usual blog posts to answer that question.

Defining Data Strategy 

When we talk about data strategy, we’re talking about a comprehensive, top-down approach that delves into an organisation’s data to affect all kinds of improvement. We want data on our firm’s cases and clients, data on personnel and communication, data about resources, and data about risk management and uncertainty. Any record of fact that we have, we can use to elevate and improve a firm. Once we collect data, we can observe the kinds of patterns that develop, make an interpretation of that information, and make inferences and plan for the future with as much skilful preparation as possible.  

When is Data Valuable? 

Sometimes it’s hard to know what data is valuable and what isn’t. We typically place a high value on events that happen frequently with relatively low impact and events that occur infrequently but have a high impact – something that is a small factor present every day, like a routine meeting or report, versus a big one-time event, like the completion of a big case that’s taken most of a year to execute. Too often, we view these different aspects of an operation as discrete entities not inherently related. Without a data strategy, we may miss tiny patterns in operations that could be having a more significant impact than one might at first suspect.

The goal of a law department or law firm is to facilitate business activities and transactions and to help manage and value risk. The focus of a legal data strategy, then, is to develop data collection and capabilities to improve the quality and efficiency of a law organisation’s tasks. Our data strategy needs to find anything and everything that may impact how these tasks are completed.

Types of Data 

There are two types of data: structured and unstructured. Examples of structured legal data include billing records, docket records, or litigation outcomes. This data already possesses a semblance of organisation. Unstructured data, on the other hand, typically doesn’t. Examples of unstructured data include court filings, contracts, and deposition transcripts. These documents are considered mainly for their internal content but not often collected and considered separate data points in a set. We have to ask questions about these individual documents and processes. In an ADR case, for example, do we know our chances of winning? Do we know what our liability might be? How successful is ADR in the first place? We can’t find this answer in a pile of documents. The answer may lie in the unstructured data floating around, but it’s ineffective as a predictive tool in its current state. This sort of situation is where we need a model and where having a data strategy can fill in the answer to those questions and many more.

Starting a Data Strategy 

How do we start a data strategy, though? The best way to start is to use small projects that are easily repeatable to design and build predictive models. We need a data set large enough to train a predictive model using machine learning.

In future posts, we will delve deeper into machine learning and the various processes that it entails. Legal teams need to take advantage of these technological tools. Law is a client-centred business, and in a competitive field, clients are going to demand the very best cutting-edge technologies.