Matthew E. Pohl
Class Action Consultant
Our Principal, Matthew E. Pohl, has built a national reputation as a leading data analytics expert with a unique breadth of experience that spans litigation analytics, complex big data analytics, class action administration, and enterprise data warehousing. His unique blend of knowledge and experience has allowed him to guide clients through the data and computer system challenges faced in complex civil litigation for more than two decades as a Data Expert, Damages Expert, Class Action Expert, and Special Master.
Mr. Pohl received his M.S. in Information Systems from University of Colorado-Denver, summa cum laude. He graduated as the top student in his undergraduate program at the University of Denver, where his Decision Sciences major explored quantitative methods for problem solving.
Class Action Administration Experience
Mr. Pohl has been deposed in cases where his fact-based opinions and reports have been found to be sound and were used as the basis for settlement discussions and class member damages and benefits. During his career, he has provided sworn testimony as a fact expert in both state and federal courts.
For 15 years, Mr. Pohl owned and led Class Action Administration, Inc., a nationally-awarded class settlement administration firm. Mr. Pohl was responsible for the design, development, and continuous improvement of the firm’s systems and processes used to manage the unique characteristics of over 300 class actions. Mr. Pohl worked directly with plaintiff, defense, and in-house counsel to craft effective class administration plans, to opine on suitability of class notice programs, and ensure successful execution of approved settlement terms. He and his firm were appointed as class administrator in numerous state and federal jurisdictions.
Analytics Experience
Mr. Pohl’s litigation analytics experience spans over twenty years working with plaintiff and defense attorneys during the litigation phase of complex commercial and class action lawsuits as a damages expert and consultant. This experience includes years with Arthur Andersen’s Economic and Financial Consulting practice and with his own firm, DataVision Consulting. The wide range of cases in which his data analytics expertise was retained includes matters regarding oil and gas royalties, life and vehicle insurance premiums, employee compensation for overtime and piece-meal work, banking fees, property value diminution, chemical leak evacuations, retailer point-of-sale systems logic, debt collection practices, recalled pharmaceuticals, automobile defects, telecommunication billing practices, improper retail refunds, and others. These cases required him to build custom systems that result in analytical solutions to determine financial damages or other fact-based reports. He also helped clients by developing initial exposure assessment models to estimate the value / exposure of cases early in the litigation process.
Damages & Data Experience
Mr. Pohl has served as Damages Expert and Special Master in a number of class actions including claims related to environmental contamination, oil and gas royalties, general relief distributions, land use enrichment, consumer protection, and employment discrimination. In some instances, Mr. Pohl oversaw medical monitoring programs and devised allocation methodologies. His plans have been presented to the parties and the court, and he has given court testimony prior to the Court’s approval.
Mr. Pohl’s big data analytics skills matured during his time as Director of Business Intelligence at a $4 billion office supply company. His team was responsible for the development and maintenance of a nationwide data warehouse and for deploying reporting and business analytics capabilities throughout the organization’s national and regional offices. Mr. Pohl also led a team in developing a global data warehouse for a healthcare equipment manufacturer with divisions in eight countries. His experience in data warehousing afforded him a comprehensive understanding of corporate systems architecture and design, data modeling, and how to extract and transform data from a wide range of enterprise-level computer systems.