Since 1994, Barb’s research has explored how organizations generate business value from data assets. Her methods include large-scale surveys, meta-analyses, lab experiments and in-depth case studies; five of her cases have placed in the Society for Information Management Paper Awards competition. Barb is a leading academic scholar, publishing in such journals as Information Systems Research; MIT Sloan Management Review; MIS Quarterly; and MIS Quarterly Executive. She regularly presents her work to academic and business audiences around the world.
Barb joined MIT Sloan in June 2013 to serve as a Principal Research Scientist at the Center for Information Systems Research (CISR). She leads the MIT CISR Data Research Advisory Board, comprised of one hundred data and analytics executives from CISR organizations. The board prioritizes, informs, and participates in data research activities in ways that influence findings and insights at MIT CISR and help advance the field of data analytics.
Prior to MIT CISR, Barb was a tenured faculty member at the University of Virginia (UVA) where she twice earned the UVA All-University Teaching Award (2002, 2010), which recognizes teaching excellence in professors. In 2017, she was awarded the Teradata University Network Hugh J. Watson Award for contributions to the data and analytics academic community. Most recently, she won the 2021 Association for Information Systems AIS Outreach Practice Publication Award for her data monetization research.
Barb is an International Expert Panel Member for The Centre for Information Resilience (CIRES), an Australian Research Council (ARC) Industrial Transformation Training Centre. She is a coauthor of published research with Australian collaborators at the University of Queensland and the University of Melbourne, and an external examiner for Australian doctoral work.
Barb authored her new book Data is Everybody’s Business (MIT Press, September 2023) to inspire workers across organizations to engage in data monetization. She actively works to encourage women, young people, and underrepresented populations to learn about data and pursue data-related careers.