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Peter Geovanes

Peter Geovanes is a results-driven data, analytics & AI/ML executive (JD/MBA) who provides a unique background that combines data science, artificial intelligence and machine learning capabilities along with business strategy, innovation, R&D, project management and management consulting skills.

Peter leads Winston & Strawn’s Data Strategy, AI & Analytic teams with notable accomplishments across early case assessment, pricing & budgeting, marketing, business development, practice of law, and forecasting. He specializes in applying state-of-the-art analytics, artificial intelligence, data visualization and modeling techniques to generate legal insights and improve overall firm performance.

He was named a finalist for both 2021 “LEGAL INNOVATOR” and "INNOVATIVE LEADER OF THE YEAR”. In 2022 he was named runner-up for "MOST INNOVATIVE LAW FIRM" by American Legal Technology awards and winner of the 2022 “DATA VALUE AWARD” presented to the leader who has worked to extract large amounts of value from their data and analytics projects.

Prior to joining Winston & Strawn, Peter was a Senior Director with Alvarez & Marsal, a Director and Analytics Market Leader with PwC, a Principal for a regional consulting firm, a senior project manager for SPSS and proudly served as a commissioned Officer in the United States Navy.

Can you describe your journey into the world of legal tech and, more specifically, the world of data and analytics?

After college, I was commissioned as an Officer in the United States Navy and proudly served on active duty for 8 years. During my last 2 years in the Navy, I went back to school and earned my MBA.

After leaving the Navy in 1997, I landed a job at one of the Big-5 consulting firms, and by chance, my first couple of client engagements were data, SQL and reporting intensive.

In 2000, I accepted a position with SPSS, a company that played a significant role in the development of the field of data science. In fact, it released one of the first software packages that provided a comprehensive suite of statistical analysis and predictive modeling techniques, making it easier for organizations and individuals to access and use data analytics tools. This helped to drive the growth of the field of data science and lay the foundation for many of the data analytics and AI tools that are widely used today.

I resumed my consulting career in 2005, armed with new skills and expertise in data warehousing, data visualization, predictive analytics, artificial intelligence and machine learning. This collection of technology and consulting skills provided me with opportunities to idealize and architect a variety of innovative solutions for Fortune 1000 clients.

Jump ahead another 5 years, it’s now 2010. I survived the financial crisis but find myself in “mid-life crisis” mode… I need a new challenge! That's when I hit upon the idea of attending law school while continuing to work and raise my family.

It’s now 2016, I have earned my JD and Winston & Strawn provides me with an opportunity to become one of the first JD/MBA/Data Scientists to work at an AmLaw 100 firm.

A position that will allow me to pursue my passions around data and analytics to solve complex business & legal problems in new and innovative ways.

What do people get right and wrong about machine learning and analytics and why?

1. Lack of understanding of the process: People may not fully understand the steps involved in building an ML or analytics model, from collecting and cleaning data to training and evaluating the model. The best advice I can give here is to follow a methodology! I typically, structure my AI and Analytics projects using CRISP-DM methodology. While this methodology was originally designed for data mining and business intelligence projects, it can also be applied to AI/ML projects, as it provides a structured approach to developing and deploying AI/ML models. By following the steps of the methodology, you can ensure that all necessary steps are taken to properly understand the problem, develop effective models, and monitor their performance over time.

2. Have a well-defined use case with a solid business purpose in mind before embarking on an AI/ML project. Having a clear understanding of the problem you are trying to solve, the desired outcome, and the business value will help guide your project and ensure its success.

3. It’s getting much easier - There has been significant advancements in software tools and approaches that simplify AI/ML in recent years. These tools and approaches have made it easier for data scientists, engineers, and business analysts to build, deploy, and monitor AI/ML models. This includes:

Automated Machine Learning (AutoML): AutoML is a new approach that automates many of the steps involved in building and deploying AI/ML models, making it easier for people with less technical expertise to work with AI/ML.

Low-code platforms: Low-code platforms are tools that allow users to build AI/ML models without writing code, making it easier for business analysts and non-technical users to build models.

Pre-trained models: Pre-trained models are AI/ML models that have been trained on large datasets and are available for use in other projects, eliminating the need for developers to train models from scratch.

Integrated Development Environments (IDEs): IDEs are integrated software development tools that provide a complete environment for building and deploying AI/ML models, making it easier for developers to work with AI/ML.

Cloud-based platforms: Cloud-based platforms such as AWS, Azure, and GCP offer a wide range of AI/ML services that can be easily integrated into projects, making it easier for developers to build and deploy models.

These new tools and approaches have simplified AI/ML and made it more accessible for a wider range of people. They have also reduced the time and effort required to build and deploy AI/ML models, making it easier for organizations to adopt AI/ML and drive business value from these technologies.

4. In some cases, you don’t have to wait until the data is perfect - By starting with the data you have, you can begin your analysis and identify potential data gaps more quickly. This may also allow you to gain insights into your problem and determine what additional data you may need.

5. Expecting quick results: People may expect immediate results from ML and analytics projects, but the process can be time-consuming and require multiple iterations before producing meaningful results.

Please describe a particularly successful use of analytics and explain why it was successful.

I have told this story before at a legal tech conference and it ended up being profiled in a Law360 article entitled “3 Legal Go-Getters On How They’re Altering The Industry”

The story begins with a simple lunch meeting with Amy Wisinski, the firm’s Senior Manager of Business Development. I had asked her … “How does the BD team support the firm’s partners?”

Amy went on to describe that one way that her team does this is by subscribing to a variety of different news sources that monitor every time a lawsuit is filed in state or federal court. Typically, the next morning, a number of analysts will search for the proverbial “needle in the haystack” trying to find that new filing that might make a good lead to pass along to a partner.

Immediately, I put on data scientist / analytics translator “hat”, and asked Amy if she happens to have a record of which ones her team “accepted” and “rejected.”

Luckily, Amy had six months of data on exactly that! I then thought to myself … “OMG, I can automate this process!” What previously took four people six hours each day turned into seconds. Not only did it save time, but this now meant better leads would be provided to partners.

Taking that thought a step further … this meant that Winston & Strawn would be first in line when making calls to win new business, since competing law firms would take longer to reach out.

As part of architecting the solution, we ended up putting together a near-real time database of every lawsuit filed in the US. I then thought, perhaps by leveraging an “augmented analytics” platforms, I could use AI/ML much like a team of analysts to identify emerging trends, correlations or outliers in the data.

These alerts could potently be used to detect patterns and trends in certain types of litigation. These insights, could then be passed along to a partner, to share with the general counsel of prospective clients, ultimately telling them things they don't even know about their own business.

Amy presented this solution at the Legal Marketing Competitive Intelligence Conference and the one of the attendees wrote “This solution is 5 years ahead of every firm!”

What strategies do you use to encourage a culture of innovation within your team, and how do you measure the success of these efforts?

Encouraging a culture of innovation within a law firm can be challenging, but it is essential for staying competitive in a rapidly changing legal landscape. For my role, as innovator, change-agent and evangelist, its imperative that I have the support of the firm’s leadership team.

A few years back the firm established a Partner lead Technology Committee and we began a impactful dialogue discussing the Firm’s strategic objectives and how to best align technology and innovation. The Tech committee helps to ensure that the technology initiatives align with the firm's overall business goals and objectives.

The Tech Committee also identifies and encourages attorneys from across the firm to be involved during our Proof-of-concepts studies. With attorney involvement we have noticed that they are better aligned to understand the benefits and potential impact of the technology. Ultimately, this leads to better buy-in and adoption of the technology within the firm.

Measuring the success of a legal technology innovation can be challenging, but it's important to understand its impact on the business and determine whether it is meeting the desired goals and objectives. Here are some metrics that can be used to measure the success of a legal technology innovation, including its positive impact on clients:

Time savings: Track the amount of time saved by using the technology, compared to the traditional manual processes it replaces. This can help to understand the efficiency gains achieved by the innovation, which can positively impact clients by reducing the time it takes to complete work.

Cost savings: Track the costs associated with using the technology, compared to the traditional manual processes it replaces. This can help to understand the cost savings achieved by the innovation, which can positively impact clients by reducing the cost of legal services.

Accuracy: Track the accuracy of the technology, compared to manual processes. This can help to understand the impact of the technology on the quality of the work performed, which can positively impact clients by providing more accurate and reliable results.

User satisfaction: Collect feedback from users of the technology to understand their satisfaction with the technology and its impact on their work. This can help to understand the impact of the technology on client satisfaction.

Adoption rate: Track the adoption rate of the technology among users, including the number of users, the frequency of use, and the duration of use. This can help to understand the impact of the technology on the firm's operations and its ability to meet client needs.

Compliance: Track the technology's compliance with legal and regulatory requirements, including data privacy and security. This can positively impact clients by ensuring that their sensitive information is protected and secure.

Business outcomes: Track the impact of the technology on key business outcomes, such as increased productivity, improved client satisfaction, and reduced risk. This can positively impact clients by improving the firm's ability to meet their needs and providing better, more efficient legal services.

By tracking these metrics, a law firm can measure the success of a legal technology innovation and understand its impact on the business and clients. This information can then be used to make informed decisions about future technology initiatives and investments

The legal tech space is dynamic. How do you stay up to date on all its developments?

Staying current with the rapidly changing legal technology landscape can be a challenge, here are some simple strategies that I follow:

1. Attend Industry tradeshows and conferences - Conferences such as Legal Tech Week, CLOC, and ILTA bring together experts, thought leaders, and industry professionals from across the legal sector, providing valuable insights into the latest trends and innovations in the field.

2. Network with peers: Network with other legal professionals who are interested in technology and innovation. This can provide valuable insights into new trends and best practices, and provide opportunities for collaboration and knowledge-sharing.

3. Participate in pilot projects: Participate in pilot projects or proof of concepts to test and evaluate new technologies. This can provide valuable hands-on experience with new innovations and help to understand their potential benefits and limitations.

4. Collaborate with vendors and partners: Collaborate with technology vendors to understand their offerings and to stay informed about new developments in the legal technology landscape.

5. Looking at technology outside of the legal domain - The hope here is to bring a fresh perspective and new ideas to the legal technology landscape.

For example, looking at technologies in the financial, healthcare, or retail sectors could provide insights into how to improve the management and analysis of large amounts of data, how to enhance the user experience, or how to automate repetitive tasks. By taking inspiration from these other industries, one can bring new ideas and approaches to the legal domain, which can ultimately lead to better services and outcomes for clients.



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