What is a data-driven legal department, anyway?

How lawyers can use legal data to influence decisions

Legal data is having a moment. Most corporate legal departments (CLDs) haven’t historically been hotbeds of data-based strategies, but that’s starting to change. Data-driven lawyers have an opportunity to make their team more informed, more efficient, and more valuable to their organization than ever. The first step is learning what’s possible.

Legal data’s untapped potential

Many companies have spent incredible resources managing both the opportunities and the risks associated with data management. Embracing data-driven management is part of most forward-thinking companies’ digital strategies. Companies that have harnessed the power of data have opened the door to make more transparent, objective, and confident decisions. This can provide an organization a competitive edge and make it more resilient.

Corporate legal departments (CLDs) have sometimes lagged in adopting
data-driven legal approaches, in part due to comparative complexity and subjectivity in executing legal processes (they aren’t manufacturing widgets!) as well as the pace and intricacies of the practice of law itself.

Legal judgment and experience can be viewed as cornerstones of practicing law, but relying solely on lawyers’ individual experiences, biases, and gut instincts to decide key issues—both operational (such as selection of counsel) and substantive (such as litigation strategy)—does not allow the CLD to also take advantage of the incremental benefits of legal data. We aren’t talking about completely replacing legal professionals’ experience and judgment with algorithms. The data-driven legal department leverages internal and external data as primary or supplementary factors in executing legal processes. It adopts decision frameworks that rely on data in conjunction with other expert decision-support resources.

What is a data-driven legal department, anyway?

Should you be a data-driven legal department?

Data-driven decisioning is properly framed as part of the CLD’s natural evolution. But is it necessary? CLDs often functioned well prior to the digital age, but there was less legal data available to calibrate, validate, and enhance decision-making. Today, it’s the language of business. Companies today expect more than just advice purely based on individual experience or acumen; another level of persuasion and confidence comes from advice that is supported or enhanced with objective metrics.

Consider this example: Your lawyer has had five appearances in front of a particular judge, has argued in that judge’s courtroom, and has seen the judge’s facial expressions as different arguments were made. These are valuable insights, and the lawyer feels confident predicting how the judge may rule. That same judge, however, may have heard 1,000 cases, and ruled directionally on your issue multiple times. The lawyer may have the right experience, but not enough data points to make a more informed decision. More data would likely lead to a better outcome.

Data-driven decisioning doesn’t happen in a vacuum, and there are often factors that can offset pure data observations that make sense circumstantially or strategically for the CLD. Even in these cases, such analysis provides a calibration point for the CLD. A data-driven legal department seeks to understand where data supports—or challenges—strategies and operations, sometimes providing validation and sometimes disproving assumptions, leading to new insights.

A data-driven approach can help you to take a retrospective view of your results, examine your current operations, or begin to make assessments about outcomes within your litigation portfolio.

What is good data? What is bad data?

Most CLDs already have a decent amount of legal data that they are at least somewhat successful in using for hindsight. Many find it challenging to operationalize, however, because that data is in different repositories in different places. In order to leverage information to provide insight and foresight, CLDs should organize and rationalize data from their legal spend systems; contracts and other corporate documents; litigation and discovery documents and more.

Before using legal data to drive decision-making, the right data must be identified that is relevant to decisioning from a contextual and timing perspective. This is called “good data.”

Bad data, or more specifically, data that is not clean nor fit for purpose, can cause frustration or even poor decision-making. Negative outcomes may occur when the data is wrong; the data is poorly understood for context or what it measures; when utilizing the data itself is time-consuming or onerous; or when the data arrives too late to make a difference.

The ability to leverage legal data requires a cohesive strategy and execution around key data processes such as aggregation and data hygiene. Conversely, weak process often generates weak data. Data cleansing and quality control are well-understood processes in analytics, and there are professionals available who can assist with these fundamental requirements.

Where should your CLD start?

The first step in embarking on a data-driven legal strategy is to understand what processes require, or should better leverage, available legal data. Benchmarking with other law departments can help with this. An inventory of data sources supplemented with relevant data characteristics is a tool with which to get started.

Once you have the list of processes, enabling systems, and data sources, you will want to select where to focus. Bases for prioritization often include areas for potential high impact, achievability (low-hanging fruit), visibility, or results that are adaptable to other processes.

Deploying a pilot or proof of concept can be a valuable tool to help answer questions such as: What are data points that are meaningful to enhance the decisioning process? Are those data points available internally? Externally? How will we surface and consume the data, and how will decisions be operationalized? Carefully measure and document the process and the results to surface what worked and what didn’t—and to create a blueprint for other subsequent processes.

Embrace your data opportunity

While CLDs may just be starting to focus more on legal data to influence decision-making, they do have one major advantage over many other departments: There is a lot of data. Documents related to substantive legal matters (which could arguably include almost all corporate records and documents) alongside those related to law department operations provide CLDs with an enormous data set. Properly leveraged, all this data can help the CLD leapfrog the corporation’s other business units and even take a leadership role in gaining value from big data.

Asking team members to become data-driven lawyers is less about data science and more about establishing straightforward indicators of success. It’s important to set reasonable expectations for how data will be used and that it will be used to facilitate stakeholders’ decisions, not to make decisions for them.

The data that is created, transferred, leveraged, used, misused, or even dormant in CLDs provides a prominent source of opportunity in an ever-evolving digital world. Some have called such data the digital exhaust generated from an organization’s activities and outcomes, but there could be potential opportunity in capturing that exhaust and using it to drive success. Launching a pilot to help answer some of the key questions is a great way to find out.

Learn more about Deloltte's Legal Business services

An outdated and inefficient legal entity management process may create needless costs and risks, including potential instances of noncompliance. Deloitte's Legal Business Services* (LEM) services can provide increased transparency into legal entity governance and reduce cost, all in an effort to make your legal ecosystem more efficient and effective. We are powered by an integrated workflow, data, and document management technology platform providing high visibility into engagement management workflows and real-time analytics.

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