Data Core Workstream
Backed by Data
The Data Core Workstream will transform the way we track and report data to support informed decision-making and student success.
Representatives to the Executive Steering Committee
- Thu Nguyen, Senior Administrator, School of Arts and Sciences (Spring 2023)
- Stuart Shapiro, Senior Administrator, Edward J. Bloustein School of Planning and Public Policy
- Jean Patrick Antoine, Staff, School of Engineering (Living-Learning Communities Workstream Liaison)
- Christine Bifulco, Staff, Office of the Executive Vice President for Academic Affairs
- Madhavi Chakrabarty, Faculty, Rutgers Business School
- Vanessa Coleman, Staff, School of Arts and Sciences
- Enrique Curchitser, Faculty, School of Environmental and Biological Sciences/New Jersey Agricultural Experiment Station
- Mary Elgayar, Student, Rutgers Business School
- Kevin Ewell, Staff, School of Communication and Information
- Martha Haviland, Faculty, School of Arts and Sciences (Curriculum Workstream Liaison)
- Ellen Law, Staff, Office of Information Technology
- Thomas Leustek, Senior Administrator, School of Environmental and Biological Sciences/New Jersey Agricultural Experiment Station (Enrollment and Marketing Workstream Liaison)
- Tugrul Ozel, Faculty, School of Engineering
- David Pickens, Senior Administrator, School of Graduate Studies
- Lisa Sanon-Jules, Staff, Mason Gross School of the Arts (Advising and Academic Support Workstream Liaison)
- Elaine Stroud, Staff, School of Management and Labor Relations (Administrative and Financial Structure Workstream Liaison) (Spring 2023)
- Sangya Varma, Faculty, School of Arts and Sciences
- Amy Wollock, Staff, Graduate School of Education
- An enterprise-wide framework that develops, supports, and promotes processes, practices, and data governance structures to transform both external institutional reporting and internal data-informed decision-making efforts designed to advance our institutional mission and our students’ success.
Conduct a thorough audit of our systems of record and primary data sources, including identifying any gaps in those systems and data repositories that are inhibiting robust data collection, data sharing, and data analysis.
Outline a detailed data governance model that will include both the individual roles for key positions at all levels and the standing committees that will ensure that our data efforts remain relevant and up-to-date.
Provide specific recommendations to improve data quality, including how institutional data is collected, secured, shared, and used for reporting and analysis.