Organizations need to anticipate future needs and challenges. Through the lens of your organization’s goals and challenges, ICF addresses critical questions and issues by examining existing internal data, harnessing relevant public data, and engaging in appropriate data collection, analysis, and visualization.
Our Achieving Workforce Excellence (AWE) Analytics framework is based on your unique context. Our workforce analytics specialists excel at translating the insights gleaned from your data into actionable recommendations that will have a real impact on your organization. Customer-focused visualizations can help your leadership team make better decisions and drive key improvements toward increased human capital success. We have helped clients extract meaning from their data for:
Brian Cronin has more than 10 years of experience in leading strategic workforce development, organizational assessment, and transportation studies. Dr. Cronin has conducted a variety of projects related to these areas of expertise for public sector agencies, including the Alabama Department of Transportation, Transportation Research Board (TRB), the Texas Workforce Commission (TWC), the U.S. Social Security Administration (SSA), the U.S. Federal Highway Administration (FHWA), and the U.S. Federal Aviation Administration (FAA). He has also conducted personnel research projects for several branches of the U.S. military and the states of Alabama, Virginia, Missouri, Pennsylvania, Florida, and Texas. For each of these studies, he has provided comprehensive workforce solutions.
Michael Smith has more than 13 years of experience in analytics, strategic planning, risk assessment, innovation, and project management. Mr. Smith currently advises several Department of Defense clients on how to adapt emerging data science, big data, and analytics practices to improve their performance. Mr. Smith currently leads a research project for Army Research Laboratory, Human Research and Engineering Directorate and manages a team of computational scientists and learning researchers developing novel methods for automated course effectiveness analytics using simulation, pattern recognition, and factor analysis.