Drinker Biddle & Reath LLP has expanded its Information Governance practice team to include data analytics, becoming one of the first law firms to offer data analytics in its practice.
Bennett Borden, co-chair of Drinker Biddle’s Information Governance and eDiscovery Team, has been named Chief Data Scientist and will lead the firm’s data analytics strategy. Borden, who holds a Master of Science degree in Business Analytics from New York University, is the first partner at an Am Law 100 firm to be named to this position.
“Our expansion into data analytics puts us ahead of the curve in the industry, and enables Drinker Biddle to provide a unique service in an area of growing importance to our clients,” said Andrew C. Kassner, Chairman of Drinker Biddle. “Having Bennett and his team employ data analytics provides our clients with special insights, and gives us an extra edge in advising our clients to achieve desired legal outcomes.”
“Harnessing the power of data is essential for our clients to drive value in their business operations and to tell their side of the story in litigation,” said Borden. “Finding relevant information within a large body of data can provide significant strategic legal advantages well beyond mere discovery for the party employing them. With the expansion of our practice into data analytics, we will be able to provide advice around the implications of this data in a way that no other law firm can.”
Borden and other members of the firm’s Information Governance practice team, including Washington, D.C. attorneys Jay Brudz and Jason R. Baron, have already applied analytics in their practice in litigation. The team will work with clients to apply analytics in other legal and information governance settings, including applying predictive analytics in investigations, due diligence, and compliance situations.
Drinker Biddle’s data analytics practice team will provide a variety of innovative services, including:
- Advising clients on the development and use of analytics models that assist in data storytelling, data remediation, and autoclassification.
- Utilizing the team’s research on the use of machine-based learning and unstructured data to build data-driven early warning systems for detecting and preventing corporate fraud and other misconduct.
- Building machine-based learning models to improve process and legal outcomes in corporate matters including mergers and acquisitions, data remediation and autoclassification, information governance program development and enforcement, litigation and investigations, and business intelligence.