Author Details

Daniel J. Smith, PhD, MBA is an associate professor and director-level analytics leader whose work bridges academia and applied analytics leadership in healthcare. He focuses on turning complex data into trustworthy, decision-ready insight through measurement strategy, standardized KPI definitions and governance practices that improve consistency, transparency and reuse of metrics across teams.

In academic settings, Dr. Smith teaches graduate and doctoral learners in biostatistics, research methods and applied analytics, emphasizing practical interpretation, reproducible workflows and clear communication of results for decision-making. His teaching and mentoring are grounded in practical experience designing reporting ecosystems, building semantic layers that support a “single source of truth,” and aligning analytics to organizational goals.

In professional leadership roles, he has guided cross-functional teams in building performance frameworks, forecasting and predictive modeling, and outcome-oriented analytics that support access, experience, quality and operational planning. His work frequently sits at the intersection of data governance, information architecture and applied A to help organizations responsibly scale analytics from pilots to enterprise capability.

Dr. Smith holds a Certificate in AI-Driven Leadership from Stanford University and an MBA and PhD from Arizona State University. He is actively engaged in responsible AI leadership and education. He is especially interested in analytics governance, ethical AI adoption and practical pathways for leaders to translate data and AI into measurable impact.