The legal professional’s competitive survival guide

For consumers, data is already all-encompassing. Ordinary people are accustomed to and welcoming of the convenience that insights derived from data bring to their lives.

• When driving, real-time data about traffic congestion is piped to mobile applications such as Google Maps or Waze. This enables drivers to make better decisions about turning right, turning left, or continuing straight ahead. This is also central to ride-share platforms like Uber and Lyft.

• When consumers use their computers or mobile devices, algorithms crunch information about them and about people like them, and present to users choices that match what the algorithms think they want to see. Spotify uses this strategy to help users discover new music they’re likely to enjoy.

• When people walk around the cities they live in, it is common for data-driven predictions about crime to inform the level of police protection available in that area.

• When individuals seek medical care, health care providers can access data about outcomes across millions of patients, and use the data to support decisions about treatments and diagnoses.  

• When consumers use a credit card, they’re facilitating the expansion of a big database of their personal habits. That data can be used for a variety of purposes, from marketing to fraud prevention.  

The prevalence of data-based applications in consumer technology is an important part of this story. Lawyers who have, for one reason or another, not implemented data-driven changes into their professional lives are still witnessing it make their personal lives more convenient and fulfilling. That speaks volumes. Consumer technology applications are becoming those that busy professionals rely on to get work done.

Moreover, a recent Harvard Business Review study, “The Evolution of Decision Making: How Leading Organizations Are Adopting a Data-Driven Culture,” concluded that companies that rely on data expect a better financial performance.

The study highlighted leaders in all industries that are integrating analytics into their organizational culture and identified some leading practices:

• Mandating the use of analytics for certain decisions
• Promoting decision-making transparency, e.g., “showing your work” 
• Emphasizing training in order to get the most out of analytics
• Spreading analytics throughout an organization, not just in central leadership

Data has always been a foundational part of practicing law. Before the convenience, accessibility, and speed of digital mediums were common, lawyers relied on legal reference books kept in vast libraries for information pertinent to the matters they worked on. Some still do.

The law relies on facts and facts are catalogued as data.

Today, however, data is not only a set of static reference points on which a human can make decisions. It’s a dynamic asset they can use to root out previously unseen relationships and conclusions. And software that digests that data can learn, as though it’s a conscious organism.

In other words, the stock in trade for lawyers has always been the complementary forces of expertise and experience. But now another one -- data-driven decision making -- is part of the repertoire.

In order to advise clients, lawyers engage in predictions about the future. In an influential article about legal prediction, Professor Daniel Martin Katz of Chicago Kent College of Law laid it out simply:

These are core questions asked by sophisticated clients such as general counsels, as well as consumers at the retail level. Whether generated by a mental model or a sophisticated algorithm, prediction is a core component of the guidance that many lawyers offer. Indeed, it is by generating informed answers to these types of questions that many lawyers earn their respective wages.”

-- Quantitative Legal Prediction, or How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry

How do lawyers make those kind of predictions? The primary model has been through expertise and experience. As lawyers practiced and gained knowledge over multiple years, they relied on their personal experience and judgment for how these questions should be answered.

Today, facts and statistics can validate those judgments: The data a lawyer has might now be applied to the predictions they are asked to make about the future, particularly if the data exists in a consistent structure.

Consider these examples of how data can fundamentally improve some common predictions lawyers are asked to make:

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 normalized, lawyers can rely on statistical evidence of certain trends and outcomes, instead of anecdotes, to base their case predictions and legal strategy. This is one way big data can improve legal research processes.

Document review: The digitalization of documents has turned document review in litigation into an electronic process. At the core of that process is 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.

Some renowned investors are known for searching for a company’s unfair advantage: something that will insulate it from competitors if and when the company achieves prominence and scale.

Across many professional services industries, data is -- or can be -- that unfair advantage. For lawyers, practicing with a data-driven mindset does not require lawyers to somehow become data scientists or software engineers. Nor does it mean data science or software engineering are equally central to the practice of law as experience and expertise with legal matters.

It’s mostly about being open to injecting data into processes and decisions where it can be the most useful, and treating data as an asset with value which must be maximized.

Think about the various types of legal data that lawyers deal with often, and the data points embedded in them:

Some of this data comes from public or published sources, some is internal to legal organizations. It is the combination and analysis of correlations between data points that provide insights that can support legal and business decisions.

Statistical analysis of some of the data sources above can support important legal decisions and business decisions for law firms, such as:

• The decision to either litigate a matter or settle it. (How: By looking at outcomes from previous cases, the dollar amounts of judgements or settlements, the cost of litigating.)
• An informed prediction on whether a motion will prevail. (How: By looking at the success rates of similar motions before the same judge, or in the same type of matter.) 
• The valuation of an entity the lawyer works for, is representing, or is involved with in a legal matter. (How: By looking at the contract value of the company’s various contractual obligations, and at risks identified in the contracts.) 
• How much in fees a matter should cost a client. (How: By looking at matter management and billing data to understand the cost of similar cases in order to provide a fixed-fee proposal.)
• If a type of litigation can be pursued more efficiently. (How: By looking at matter or billing data to identify process bottlenecks or tasks that could be performs better or more quickly by other types of professionals.)

Think of how much time the typical lawyer spends time contemplating these questions when they spring up. Some of them keep lawyers up at night. Now think of how much time and mental energy they could save with instant information and facts on which to ground their decisions. Whatever the exact numbers are, it’s safe to say that there is significant value in answering these questions quicker and in increasing the confidence a lawyer has in the answers.

The key to thinking with a data mindset is to start with these questions and problems, and to be open to the idea that there may well be data sets that can contribute to the answers.   The specific technologies and techniques for getting to those answers aren’t as important as simply creating the conditions in which those techniques can thrive -- by encouraging data-driven thinking and action.  

Building a data-driven legal practice isn’t going to happen overnight. But starting down that road isn’t the Herculean task that it may seem.

Here are five good starting points. 

1. Walk before you run, and start with the low-hanging fruit

There’s a lot of hype around data analysis and it’s easy to romanticize. But robot lawyers will not be taking over the job of legal reasoning from human attorneys, and magic algorithms will not be predicting the future all on their own.

The place to start with using data to enhance your practice is probably 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. 

Across the board, most of the examples of successful application of AI or analytics had to do with the business side of the house — not robot lawyers giving legal advice, but robots that help firms budget and price matters. At last year’s 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 business operations.”

Starting with straightforward business issues such as staffing work, pricing it, and managing capacity. Then look at the data that might answer those questions, and find or build tools that can access it. And then look for the people on the development side who can take it from there. It’s up to lawyers to get in the right mindset to start thinking about data this way. 

2. Identify and organize your data

It’s about the data before it’s about the software. When the people who actually 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.

One source who consults with lawyers and firms on analytics projects says that the early stages of analytics initiatives are mostly focused on the process of getting an organization’s data into shape and building processes to capture data -- “counting legal things,” as he put it. “Everybody wants to play in predictive analytics, but few have the good data to support it,” he said. 

This step involves getting data structured in the right way: getting it out of, for example, disparate and unwieldy spreadsheets that law firms maintain for data-collection processes and into an organized 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, from legal research databases, and from other third-party sources, that can be tapped and correlated with internal data. 

3. Clean up your data

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

Docket data from state and federal courts, while enormously valuable for analytics applications, is also notoriously messy, coming as it does from a multitude of courts using various systems and terminology, and different data standards. Simply normalizing and cleaning up that data, a process that Thomson Reuters invests heavily in, is difficult enough.

Billing data and matter management data in legal organizations can also need some tidying up. The systems that support those functions generate incredibly valuable data – but they often weren’t designed with the large-scale extraction and analysis of that data in mind.  Imposing that kind of data hygiene is hard work that involves relying on people with good data skills working side-by-side with the legal professionals who understand the significance of the data. 

4. Collaborate with those who know data well

Which leads to the next imperative. There is no getting around the fact that leveraging analytics in a legal organization requires lawyers to work side-by-side with people who understand data and data structures.  This is not a natural partnership that most lawyers are comfortable with.  For most legal professionals, little nothing in their training or experience has required them to cross over from the “practice of law” side to this “business of law” issue. 

This is, again, part of the mindset shift that legal professionals need to make.  But equally, it’s a stretch for data professionals as well. They might not see the legal significance of a certain terminology or classification in data sets – but those distinctions can have huge legal ramifications.  So data professionals working in the legal space need to acquire some level of legal subject matter expertise in order to fulfill their roles properly. 

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

5. Build a data-driven culture in your organization

Building a data-driven legal practice is not something you assign to a task force, department, or an individual.  It’s one of these change-management challenges that requires buy-in from everyone: from leadership that is prepared to invest in it; to the professionals tasked with building applications; to the workflow owners who have the tough task of altering the way work gets done; to the practitioners who have to trust the data and build data-driven predictions into the advice they give their clients.

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.” 

The Harvard Business Review study cited above put it well.  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 will have to be trusted as key team players.   
• Developing best practices. An “analytics ecosystem” across an organization can allow the sharing of successes and best practices.  

Whether they work in law firms, as part of an in-house legal department, or the in the government, lawyers today are part of a business, and few businesses today have the luxury to operate outside the technology trends that are driving growth in other industries.  

Fortunately, there are reasons to believe lawyers are ready to truly embrace data-driven lawyering.

Law schools are increasingly developing curriculum around the nexus of law and modern data management practices, meaning the next generation of lawyers will have data as a foundational piece of their legal education. Popular consumer technology is, of course, always looking for new ways to leverage data to improve the user experience, meaning even if today’s lawyers aren’t rushing to implement data into their own practices, they’re becoming familiar with the capabilities of data from the standpoint of the consumer.

Finally, Thomson Reuters and other solutions providers are making significant investments to bring new solutions to market and educate the legal industry about their benefits. These solutions work as designed and deliver significant value to users at large law firms and small to mid-sized firms, as well as to users on in-house legal teams and in government, law enforcement, and the courts.

Creating a data-driven legal practice is a matter of competitive survival. And it’s much more than simply the adoption of a new tool or product. It requires a shift in mindset for individuals, and a significant cultural change for their organizations.

The future, as they say, is now. The successful organizations will be those that are best able to navigate that transition to a new, data-centric legal services industry.