LDI Resources

From the very beginning of his career, Adam Rouse was deeply interested in technology and its interactions with the legal world. A classic early adopter, Rouse has been at the forefront of driving the legal industry's AI transformation.

The Legal Data Intelligence model was formally launched in May this year, and in the last seven months alone, a large and passionate community of LDI practitioners and supporters has organically grown within the legal firmament. To continue to build on our work as LDI founding members, we announced in September that we were accepting applications for a new program called LDI Architects.

On a recent webinar moderated by David Cowen, founding members of the Legal Data Intelligence (LDI) project shared vital insights and observations on the complexity of data challenges today, and the exciting opportunities available to legal professionals working at the intersection of data, technology, and the law.

At Relativity Fest 2024, four Legal Data Intelligence (LDI) founding members discussed how they've applied LDI to solve real-world legal data challenges within their organizations and the direct implications they see the model's adoption having on the emergence of new jobs and career pathways.

From spearheading AI projects at a major law firm to leading legal analytics at Edward Jones, Jeremiah Weasenforth's unique career path is a case study in how Legal Data Intelligence leaders can navigate complex challenges and create ever more value.

For the very first time, we are accepting applications for legal professionals to become Legal Data Intelligence Architects!

In this episode, Briordy Meyers talks about his early beginnings as a lawyer in public service, his pioneering work building privacy programs in the early days of GDPR, and the novel ways in which he has adapted the use of AI to solve complex legal data challenges.

In this episode, Omar Haroun talks about the genesis of the Legal Data Intelligence project. He dwells on the intricate data challenges confounding legal professionals and the exciting opportunities presented by generative AI to transform the day-to-day of legal work.

Legal Data Intelligence practitioners are wired to take on a growing array of data challenges affecting a broad range of use cases. Their ability to find creative solutions is rooted in their deep experience in finding the data that matters across massive data pools.

In this episode, John Koss takes us through his non-linear career journey which included forays into journalism, business management and the law. He gives us an insightful look into the changing nature of legal business models, the shift in legal professionals' attitudes towards technology and processes in the age of big data, and how the emergence of Legal Data Intelligence could impact electronic discovery and legal workflows.

In this episode, Laura Kibbe traces her career journey from an in-house position at Texaco during the very beginning of her career to her current role at Unisys. Her rich and varied career journey is a perfect reflection of how the legal profession has evolved over the years, and presents a window into the trends, attitudes and leadership that inspired the Legal Data Intelligence project.

What were the systemic factors and trends that led to the emergence of Legal Data Intelligence (LDI)? In this episode, Chris Acosta tracks the evolution of data and the necessity of having a new and strategic framework to tackle legal data challenges. He also talks about the core concepts at the heart of LDI: ROT Data and SUN Data.

Whether they started out as contract attorneys, ediscovery professionals, or due diligence attorneys, Legal Data Intelligence practitioners have now found ways to parlay their skills and interests into a broad range of practice areas and find new career avenues off the beaten path.

What is the origin story for Legal Data Intelligence? How can ambitious legal professionals carve out a career--even an executive-level role--in this emerging space? Farrah Pepper has great advice to share.

What stories and insights are hidden in big data--and how can modern legal professionals extract value from them? David Cowen and Josh Kreamer discuss just that and more in this podcast episode.

In the second episode of David Cowen's Legal Data Intelligence podcast, Scott Milner provides some helpful insights into how the legal profession has gotten to this point, what the LDI project hopes to deliver to practitioners, and how legal professionals can get involved. Legal Data IntelligenceLegal Data Intelligence

What is Legal Data Intelligence? What is its origin story? What is the value it unlocks and what does it mean to be a Legal Data Intelligence practitioner? David unpacks these concepts and more in the first episode with his guest, Ashley Christakis.

At CLOC Global Institute 2024, a group of industry experts unveiled Legal Data Intelligence (LDI), a new initiative and approach to solving the complex data problems that often get in the way of legal professionals today.

Learn how this novel framework can empower you to complete legal tasks, overcome data challenges, and unlock new value.

Take charge of data challenges at your organization. See the responsibilities, scope of work, and skills needed to become a Legal Data Intelligence leader.

Legal Data Intelligence is a team effort, and analysts are crucial members of that team. Learn how this emerging role supports data stakeholders and drives data decisions.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges in litigation. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with regulatory requests. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with third-party subpoenas. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with internal investigations. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges in contract reviews. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges in contract lifecycle management. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with Freedom of Information Act (FOIA) requests. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with data subject access requests (DSARs). This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges in data breach response. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges associated with personal data identification and anonymization. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps tackle data challenges in invoice review. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.

Take a detailed look at how the Legal Data Intelligence model helps optimize operational efficiency. This static version of the model has been bucketed into three steps: Initiate, Investigate, and Implement.