Data That Moves the Needle

Video

City Colleges of Chicago has been on a multi‑year journey to become more intentionally data‑informed—using data not simply for reporting, but as a leadership tool to advance equity and student success. In this keynote, Chancellor Juan Salgado will reflect on what he needed from data teams to lead more effectively and the progress City Colleges has made to date. Drawing on CCC’s experience, Chancellor Salgado will describe how data helped align leaders around shared priorities and reinforce the urgency of the district’s “55 for All” student outcome goal.

The keynote will be followed by an audience Q&A with Chancellor Salgado and Vice Chancellor for Strategy and Planning Brittany Jackson Kairis, who oversees Decision Support at CCC. The discussion will focus on practical lessons for leveraging data to improve outcomes.

Workforce Outcomes in a New Policy Moment: Opportunity, Risk, and Institutional Strategy

Video

Community colleges have long been leaders in workforce education, closely aligned with regional labor markets and committed to economic mobility. As federal attention increasingly turns toward post-completion and earnings-based outcomes, institutions face both opportunity and risk: greater visibility into workforce value alongside new pressures around how that value is defined and measured. This keynote will explore how community colleges can use benchmarking and outcomes data strategically—not simply to respond to accountability demands—by examining where earnings metrics are useful, where they fall short, and how data professionals can apply benchmarking with local context to support decision-making and responsibly advance the workforce mission.

Data is the New Oil: The Future of AI, Ethics, and Institutional Research

Video

Artificial Intelligence (AI) is advancing at unprecedented speed, reshaping higher education through predictive models, generative tools, and data-driven decision-making. Yet the mantra that “data is the new oil” underscores both AI’s promise and its risks: flawed or siloed data, misinformation, bias, and limited interpretability. This session addresses the urgent need to balance technical innovation with ethical responsibility. It will trace AI’s evolution, examine challenges of data quality and governance, and present frameworks that promote fairness, transparency, and explainability in institutional research practice.