Building a Data-Driven Legal Practice

The practice of law, like many other fields, is characterised by an overwhelming volume of data and by the speed at which that data is accumulating.

The data is fragmented and complex – coming from traditional public sources such as courts and regulators as well as legal organisations’ own processes: timekeeping, billing, matter management and the like.

For law firms and in-house legal departments, however, data-driven decision making is relatively new and uncharted.

In Legal, Better Data Means Better Predictions

One of the strongest assets in a lawyer’s portfolio of skills has always been expertise and experience. In using that expertise to advise clients, lawyers engage in predictions about the future: “How much will this cost? How long will it take? What are our chances of prevailing in court?”

But now another tool – data-driven decision making – is part of the repertoire. Here are a few examples areas where legal practice is being transformed by leveraging data:

Litigation Planning and Strategy: Understanding the likelihood of success on a motion or a judge’s tendency to rule on a specific type of matter can be key to litigation. Certain judges take longer to rule, and tend to rule in a certain way. Lawyers may draw on their own experiences, or colleagues’ anecdotes, when predicting outcomes, but these sources of information don’t paint a full picture.

Today’s legal analytics tools offer the capability to mine the entire body of a judge’s rulings, or to create comparisons using data sets that can be quite robust. Once docket data is cleaned up and normalised, lawyers can rely on statistical evidence of certain trends and outcomes, instead of anecdotes, on which to base their case predictions and legal strategy. This is one way big data can improve legal research processes.

Document Review: The digitalisation of documents has turned document review in litigation into an electronic process. At the core of that process is a question that triggers a prediction: Which of these millions of documents are likely to be responsive to a discovery request?

Where that review process used to require lawyers to manually read every document and code for relevancy, today’s eDiscovery tools can be trained to look for significant patterns in all that electronic data, and to issue predictions on the likelihood of a given document’s relevance to the training set.

Pricing and Budgeting: How much will it cost? That’s certainly one of the questions of utmost importance to clients. Many traditional lawyers answer that question in terms of expected inputs – if they are billing by the hour, they know from experience how long a matter is likely to take, and that experience becomes the basis for a prediction. But that sort of experience-based prediction can lead to all sorts of conflicts and disappointments when a matter deviates from the norm.

Today, however, when law firm practice management and financial systems collect all kinds of data about matters and their pricing, there are significantly better ways to use that data to make more firm predictions about pricing, and even to support fixed fees for certain services, cutting the risk of deviations to both the client and the lawyer.

Building a Data-Driven Practice: 5 steps

The following five steps illustrate some of the practical and cultural shifts lawyers and legal teams will have to navigate to succeed in a data-rich environment.

Walk Before You Run – on the Business Side of the Organisation

The place to start your data-driven initiative is in comparatively mundane applications like your own billing and matter-management systems. They hold a gold mine of data about productivity, value, talent, results and outcomes.

Most successful applications of artificial intelligence or analytics are on the business side of the house – not robot lawyers giving legal advice, but robots that help firms better budget and price matters. At last year’s International Legal Technology Association (ILTA) conference, one law firm consultant put it well: “Before we use AI to replace what lawyers do, let’s start using it for what they won’t or can’t do” – which usually involves analyzing their firms’ business operations.

Start with straightforward business issues such as staffing a legal matter, pricing it and managing capacity. Then look at the data that can inform those decisions and find or build tools that can access it. And then look for the people on the operations side who can create and maintain the necessary protocols and processes to ensure the value of this information is continuously tapped.

Identify and Organise Your Data

It’s about the data before it’s about the software. When the people who implement analytics solutions get together, it’s amazing how much they talk about the data itself before they get to the subject of what the software does.

Karl Haraldsson of HBR Consolting, who consults with lawyers and firms on analytics projects, says the early stages of analytics initiatives are focused on getting an organisation’s data into shape and building processes to capture that data – “counting legal things,” as he puts it.

“Everybody wants to play in predictive analytics, but few have the good data to support it,” he says.

This step also involves getting data structured in the right way: for example, molding it from disparate and unwieldy spreadsheets into an organised and structured format that is both secure and shareable among those with the appropriate access permissions.

This stage also includes identifying external data sets – from dockets, legal research databases and other third-party sources – that can be tapped and correlated with internal data.

Clean Up Your Data

Data hygiene is a critical step. Terms and classifications for medical treatments, for example, need to be fully standardised.

Legal docket data from state and federal courts, while enormously valuable for analytic applications, is also notoriously messy, coming as it does from a multitude of courts using various systems, terminology and data standards. Simply normalising and cleaning up that data is difficult enough.

Billing and matter management is an additional challenge. While the systems that support those functions generate incredibly valuable data, they generally weren’t designed with large-scale data extraction and analysis in mind. This type of data hygiene is hard work that requires people with good data skills working alongside legal professionals who understand the significance of the data.

Collaborate with Those Who Know Data Well

This leads to the next imperative: Leveraging analytics in a legal organisation requires lawyers to work side by side with specialists who understand data and data structures. This is not a natural partnership that most lawyers are comfortable with. For most legal professionals, little or nothing in their training or experience has required them to cross over from the “practice of law” side to this “business of law” issue.

While this is part of the new thinking that legal professionals need to make, it’s also a stretch for data professionals as well. They might not see the legal significance of a certain terminology or classification in data sets – distinctions that can have huge legal ramifications. Data professionals working in the legal arena need to acquire some level of legal subject-matter expertise to fulfill their roles.

For both lawyers and data professionals, building trust and subject-matter expertise across professional boundaries is a big part of the mind-set shift that the legal industry is facing today.

Build a Data-Driven Culture

Building a data-driven legal practice is not something you assign to a task force, department or individual. It’s a change-management challenge that requires buy-in from everyone: leadership that is prepared to invest in it, professionals tasked with building applications, workflow owners who face the tough task of altering the way work gets done, practitioners who have to build data-driven predictions into the advice they give their clients, and everyone involved in using this new source of intelligence to reliably scope, price and manage client work.

None of this comes easy, and it all comes down to building behaviors and practices that support the idea that “this is how we do things now, and it’s better than prior practice.”

A Harvard Business Review study, “The Evolution of Decision Making: How Leading Organisations Are Adopting a Data-Driven Culture,” noted that building a data-driven culture requires a lot from users:

  • Enhancing skills – Users and managers will have to adapt to new tools.
  • Balancing data with instincts – Lawyers in particular will not easily give up their autonomy; there must be leeway to question what the data says.
  • Forging new relationships – Analytics professionals have to be trusted as key team players.
  • Developing best practices – An “analytics ecosystem” across an organisation can allow the sharing of successes and best practices.

Clearly, building a data-driven legal organisation requires more than just a software purchase or the hiring of a data analyst or two. It requires significant cultural and operational shifts. The successful legal organisations will be the ones willing and able to make those changes.