Legal Analytics & How Attorneys Use It

Legal analytics is the application of data science to the business and practice of law. It is an umbrella term that covers numerous different techniques, legal analytics software, and goals. Legal analytics can be applied directly to the business of law, and it can also be applied to legal work. The benefits of legal analytics include saving time, reducing risk, improving decision-making, and increasing goodwill and trust from clients. Legal practitioners may work directly with a legal analytics firm, or they may rely on tools that incorporate legal analytics.

To perform legal analytics, developers and practitioners draw on concepts and tools from the field of data science. At its core, data science is the process of inspecting, cleansing, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. 

Initially, analytics may focus on summarizing data to provide a new view of historical trends. Beyond presenting basic statistics, data science can inform decisions in the present or about the future. Most of these questions tend to be one of five types:

  • Classification—Is this A or B?
  • Anomaly detection—Is this weird?
  • Regression—How much? How many?
  • Network analytics—How is this organized?
  • Reinforcement learning—What should I do next?

Within the field of data science are a number of concepts, many of which have become popularized in the media and with vendors. Understanding the meaning of these terms is very helpful when selecting and using legal analytics.

One of the most ubiquitous terms used today in the field is “artificial intelligence” or simply AI. There isn’t a universal definition for AI. Generally, artificial intelligence is the use of computers to perform human-like tasks. The term is broad and includes the technology behind self-driving cars and film recommendations. Within AI are a number of other concepts. One form of AI that is particularly relevant to the law is natural language processing, or NLP. NLP is about teaching computers to read and write. Like NLP, computer vision programs allow computers to “see” pictures and videos. Computer vision is used to identify objects (e.g., it is a cat), recognize people and places, and create models of the world (e.g., for self-driving cars).

All artificial intelligence relies on algorithms, or equations. When these algorithms are developed and refined by the computer itself, we are talking about the field of machine learning or ML. In ML, data scientists often feed large quantities of data to their algorithms in order to “train” the model. Models are at the heart of machine learning. When artificial intelligence projects involve extremely large or complex datasets, we are talking about “big data,” which involves specialized tools and techniques to avoid overloading systems.

In order to do quality legal analytics, one typically needs large amounts of consistent and quality data. Most of the time, this data isn’t clean and neatly waiting for the data scientist. Instead, data engineers work like plumbers to build pipelines of data. They often inspect, structure, integrate, clean, and transport the data to a place where it can be used by legal analytics teams. Like the pipes in our homes, most of the data engineers’ work is not seen by the end users, but data pipelines are foundational. Failure to do quality data engineering at the beginning of a project can result in large costs later in the project, as issues with the data and the data pipeline become evident. 

All of these techniques fall within legal analytics and are used by developers and business intelligence teams to develop tools and reports to empower legal professionals to do their work better and faster.

Currently, the market for legal analytics tools and services is fragmented, as numerous startups and existing companies develop and refine new tools or enter the legal market. Many existing tools are being adapted to the legal market. Practitioners, lawyers, and legal operations managers should keep an open mind and seek advisors to evaluate and implement legal analytics tools.

Like any business, law firms and general counsel can use business analytics tools to gain visibility into their operations and finances. Often this visibility can uncover profit and cost centers in the organization, identify bottlenecks in business processes, and support a host of other executive and operational decision-making. These tools aggregate, model, analyze, and present data from systems across the enterprise (e.g., contract management, CRM, and eDiscovery). The market leader in this space is Microsoft with their PowerBI software. However, many vendors provide quality tools. Successfully implementing these kinds of tools often hinges on quality change management rather than tool selection, but here are several to choose from that can meet different needs:

  • PowerBI: Microsoft’s software for modeling data from multiple sources and creating and sharing dashboards. They are the current market frontrunner.
  • Tableau: Tableau is a market-leading data analytics platform that was purchased by Salesforce, the market-leading CRM tool, and is heavily integrated into their ecosystem.
  • Qlik: Another contender in the space, Qlik provides data and analytics solutions and tools.
  • Apache Spark: Apache Spark is an open-source data analytics engine that includes tools for data engineering, analysis, machine learning, and dashboarding.
  • DigDash: In addition to the large companies offering business analytics tools, there are a number of smaller companies offering quality tools. Digdash is one example. It offers an analytics and dashboarding tool that is highly rated, and the company is highly responsive because of its size and agility.
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Customer Relationship Management (CRM)

These tools didn’t start as legal technology, but they can be very useful legal analytics tools. CRM allows businesses to develop and track customer engagements, contacts, sales pipelines, customer contracts, and other aspects of the relationship in one place. These tools can be used to perform conflict checks, bill clients, track communications with clients, offer internal and client-facing knowledge bases, and do a multitude of other things. The data stored within these systems can be easily analyzed to provide insights into relationships, sales, and performance. Through integration, the data in these systems can support other legal data analytics work, including document automation.

The market leader in CRM tools is Salesforce. Salesforce, which is not specifically targeted to the legal market, has transitioned from software as a service (saas) to platform as a service (paas), where customers and third-party developers can create their own solutions within the Salesforce CRM. For example, Litify is a CRM and document-management application built for the legal market that was developed using the Salesforce CRM tools. There are a number of other CRM tools built specifically for the legal market, including Clio.

Document Automation

Legal departments and law firms spend considerable amounts of time creating and managing document templates. Document automation simplifies this process, reducing errors and saving time. Document automation software often include algorithms that detect missing clauses, identify and parse the elements of documents, calculate risk scores, and otherwise improve the functionality of the tools. Most of these algorithms are baked into the document automation software and are nearly invisible to many users. However, they are integral to the functionality of the tool.

To create a new legal document, a practitioner can use questionnaires created and managed by the tools to gather the information needed before the software automatically generates the draft document. Subsequently, the practitioner can customize the documents. Legal document automation tools regularly update their library of terms as laws change, nearly eliminating version control issues.

There are many document automation tools that legal practitioners can implement, and many of them provide both document automation and contract review. An emerging leader in document automation is IronClad. IronClad was founded in 2014 and is backed by venture capital. IronClad combines document automation with contract workflows. Like IronClad, many tools today combine multiple legal analytics components to provide greater benefit to users.

Current tools in this space include:

  • Ontra: This platform is used to create, negotiate, process, and understand routinely used documents.
  • DocAssemble: A feature of the technology world is the growth of open-source tools. These free tools are best for organizations that are open to working within the open-source community and are willing to deploy and manage their own software. 
  • Woodpecker: This Microsoft Word Add-in allows users to create legal documents quickly and then send clients automatically generated web-based questionnaires to speed completion of the documents.

Contract Review

During contract negotiation, documents are often emailed back and forth and revised again and again. Tracked changes are turned on and off, changes are accepted and not tracked, and too often scriven errors make their way into agreements, leading to later confusion and client dissatisfaction. Contract review tools built using natural language processing (NLP) allow lawyers to quickly identify problem areas within contracts, saving time and eliminating costly errors. These tools enhance the contract review process by finding inconsistencies, identifying missing clauses, and highlighting risky areas within the contract. Like most legal analytics tools, they supplement and improve rather than eliminate the work of lawyers on contract review.

Contract review tools can either be integrated in contract management tools or stand alone. Stand-alone tools tend to be less expensive and more flexible, particularly when working with documents from external parties that must be reviewed using standard document software.

Current tools in this space include:

  • Evisoft: An end-to-end agreement management platform that includes AI-driven contract analytics.
  • Loio: Like most tools in this space, this tool combines clause management with contract review. 
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Litigation Prediction

Artificial intelligence tools are used to calculate risk and reward scores for cases. These tools help litigators estimate the likelihood of success for a case, improving decision-making by legal practitioners and clients. These tools can help with settlement negotiations, attorney selection, case selection, and trial preparation. To develop these kinds of tools, developers need access to large datasets that include information about cases, attorneys, judges, settlements, and more.

An emerging area of AI legal analytics technology is litigation prediction. Many insurance companies and firms use this software to identify cases that could be litigated. This is a less mature area within legal analytics. 

Current companies in this space include: 

  • Megaputer, which offers a tool that predicts the threat of litigation within insurance claims; and
  • Infinilytics, which uses AI, ML, and NLP to predict the threat of litigation within property and casualty claims.

Electronic Discovery

Discovery can account for a majority of the cost in large litigation cases. The process of manually reviewing documents is time consuming and prone to error. Legal analytics integrated into electronic discovery tools can extract entities from documents, logically organize and display materials, and help lawyers sift through the mountains of discovery. 

Current companies in this space include:

  • OpenText, which is an end-to-end ediscovery tool to store and search through discovery materials. The tool also supports collaboration and case preparation.
  • Everlaw, which uses AI to organize and search ediscovery material and then helps teams find critical information and collaborate.

Trademark Management and Protection

Protecting intellectual property (IP) requires considerable effort to monitor and enforce trademark rights. IP protection tools use computer vision (a computer that can “see”) and natural language processing (NLP) to monitor numerous platforms for trademarks that may infringe on senior trademarks, and alerts the owner to them. These tools can also alert users to the misuse of trademarks.

There are several trademark screening and protection tools offered by companies, including:

  • Corsearch: Provides a suite of tools to help select available trademarks and to protect marks from infringement in the marketplace.
  • Anaqua: Manage trademark renewals, title updates, data verification, infringements, and threats; and collaborate with others. 

A number of direct-to-client tools are being offered that allow clients to receive services that previously would have been the exclusive domain of lawyers. Many of these tools integrate legal analytics algorithms to improve the user experience. While many lawyers may resist these tools, savvy lawyers can find ways to integrate them into their practices. 

These tools include:

  • Trust&Will: Online estate planning, giving clients the ability to create their own estate plan without visiting a lawyer. 
  • HelloDivorce: A set of tools to help people file for divorce in uncontested cases.

I Don’t See the Tool I Need/I Don’t Know Which Tool Is Right for Me

In addition to specific tools, there are a number of high-end data analytics companies that can develop bespoke legal analytics tools or guide companies as they adopt legal data analytics. Two examples are Big Deal Analytics and Lex Machina. These companies can build legal analytics programs, develop new algorithms and tools, advise on tool selection, implement tools, engineer data pipelines, and implement enterprise master data management programs. Rather than selling a particular tool or piece of software, companies like Big Deal Analytics provide full-service data analytics support.

Conclusion

There are many new and exciting legal analytics tools available right now, and more are being launched every week. These tools have the potential to change the practice of law, increasing productivity, reducing errors, and improving client satisfaction. Successfully implementing new technology requires particular attention when selecting the technology. When selecting and implementing these tools, it is important that the new tools enhance, rather than disrupt, business processes. To increase the chances of successful implementation, carefully evaluate the available tools and have the right technical and change-management support to integrate the technology, modify business processes, and train and communicate with staff.

Article by Michael Habash

Michael Habash is a technology program executive and attorney. He has over a decade of experience implementing and managing technology solutions, corporate policy, and risk management for large corporations and organizations. He stays active in the legal community helping law firms evaluate and implement legal technology and as a volunteer attorney.

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