Helping Lawyers use Data and Analytics to Serve Their Clients
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Helping Lawyers use data and analytics to serve their clients.

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Warren E. Agin

Warren Agin is a Senior Consultant, Data Solutions for Elevate ServicesLexPredict group.

A former partner of Swiggart & Agin, LLC, at Elevate Mr. Agin designs machine learning systems, helps firms and law departments use and interpret data, and provides subject matter expertise within the LexPredict data science group.

His background includes certified training in data analytics and machine learning in addition to almost three decades experience in insolvency, business and technology law.

Download Mr. Agin's full CV.

Warren Agin is the founding chair of the American Bar Association's Legal Analytics Committee. He has taught analytic techniques as an adjunct professor at Boston College Law School, and writes and lectures on data and machine learning systems.

Recent Publications

Certifications and Training

  • Machine Learning - Andrew Ing, Stanford

  • Machine Learning: Regression - University of Washington

  • Machine Learning Foundations: A Case Study Approach - University of Washington

  • Excel to MySQL: Analytic Techniques for Business Specialization - Duke University

  • Managing Big Data with MySQL - Duke University

  • Data Visualization and Communication with Tableau - Duke University

  • Mastering Data Analysis in Excel - Duke University

  • Business Metrics for Data-Driven Companies - Duke University

Prior to joining LexPredict, LLC (acquired by Elevate Services in December 2018), Mr. Agin was the principal of Analytic Law, and a member of Swiggart & Agin, LLC, a law firm he co-founded in 1999. Internationally known for his work in technology and insolvency law, Mr. Agin has chaired the American Bar Association's E-commerce and Insolvency Subcommittee, chaired the Business Law Section's Technology Committee, and served on the ABA's Standing Committee on Technology and Information Services as well as the Governing Council for the ABA’s Center for Innovation. He also previously co-chaired the Boston Bar Association's Internet and Computer Law Committee, and served for many years on the steering committee for its Intellectual Property Section.


Warren Agin joins hosts Pat Clendenen and Jeffrey Davis on Radio Entrepreneurs to discuss his cutting edge work in legal analytics.



The Future

Over the next two decades, the way in which lawyers work will change radically. Entirely new ways of delivering legal services will emerge...
— Richard Susskind

Data Analytics

Whether or not most attorneys have noticed it, legal data use has gone mainstream. Products built on analyzing "big data" are now available that can predict the outcome of lawsuits or even specific motions. Small data applications use the data available inside your firm to improve billing practices, measure performance, identify how to generate value for you and your clients, and drive improved results-based decision making. Data driven methods will give the firms using them clear competitive advantages. And, understanding how to manage the data your firm creates is the first step toward using more sophisticated tools, like machine learning and economic modeling. 

Artificial Intelligence

The legal industry has lagged behind others in using machine learning or "AI" systems. With recent increases in computing power and improved access to machine learning technology, this is rapidly changing. Machine learning already sees widespread use in TAR discovery systems, with some predicting its use in TAR might eventually be mandatory in some situations to reduce discovery time and costs.  In the UK,  according to consultant Richard Tromans, nearly all top 30 law firms either use or are piloting some form of AI system. A 2017 report by Altman Weil concluded over half of firms with more than 250 attorneys are investigating AI systems. A Bloomberg study early in 2019 found that over 25% of large legal departments were using legal AI systems. Machine learning can allow computer systems to perform complex tasks, but it also performs simple jobs, such as sorting contracts, speeding document review, and finding drafting errors. A growing number of vendors provide machine learning based solutions for many legal tasks, ranging from simple to complex, but new programming tools also make building custom solutions relatively easy. As more large firms incorporate these systems in their product offerings, allowing them to provide improved service more efficiently, the rest of the legal profession must follow.

We are very influenced by completely automatic things that we have no control over, and we don’t know we’re doing it.
— Daniel Kahneman

Behavioral Economics

What is behavioral economics, and how does it relate to the work we do as lawyers? In short, behavioral economics is the science of how people make decisions. By understanding the techniques people use to make their decisions, including those that cause us to occasionally make bad decisions, we can accomplish two things. We can help other people make better decisions (or perhaps, instead, make the decisions we want them to make). We can also better understand our own decision making processes and, with a more concrete understanding, improve them. Understanding decision making can improve our performance in negotiating deals, structuring contracts, or building compliance systems.

Applying behavioral economics concepts, especially when coupled with data and basic game theory techniques, can provide a substantial advantage in making strategic and tactical decisions.

The future is here, it’s just not evenly distributed yet.
— William Gibson

What's Next?

For law firms, the question isn't whether they will adopt data driven practices and techniques - but when and how. For most firms, the next step is building an understanding of these techniques: what they are, how they can help improve delivery of legal services, and how to incorporate them into existing work flows. In many cases, this will mean starting slow. Training will help the firm develop lawyers and other staff comfortable with data driven problem solving. Smaller projects will teach the firm and its professionals how to use data, acquire and build data driven systems, organize and structure work, and integrate new technologies into the firm culture.