Dr. Sena Okuboyejo
Product-Minded Analytics Leader | MIS Faculty | Business Intelligence, GenAI/LLM-Enabled Insights, UX Research & Systems Analysis | Health IT Contexts
Background
I am a product-minded analytics and systems leader with 17+ years of experience at the intersection of data, technology, and decision-making. My work translates complex data and workflows into clear insights, usable systems, and AI-enabled solutions that drive real organizational outcomes.
My expertise spans business intelligence, systems analysis, UX-informed research, and GenAI/LLM-enabled decision support, with applied experience across health IT and data-intensive environments. I specialize in complex, high-stakes systems where data quality, user needs, ethical considerations, and operational constraints must be carefully balanced.
I bring a product and systems mindset to analytics—starting with problem framing and stakeholder needs, then designing dashboards, workflows, and AI-supported insights that people can actually use. My GenAI work emphasizes responsible, human-centered applications, including LLM-assisted analysis, insight generation, learning design, and decision support.
Currently serving as Assistant Professor of MIS at Metropolitan State University, I lead initiatives in analytics education, supervise doctoral students, and pioneer institutional adoption of generative AI technologies.
Expertise & Research
My work spans technical implementation and scholarly inquiry, combining hands-on analytics expertise with research-driven insights to advance the field.
Technical Expertise
Dashboards, KPIs, and data storytelling
Analysis, insight synthesis, and workflow support
Requirements, workflows, and stakeholder alignment
User-centered evaluation and design
Research Focus
Digital Health Transformation
Examining outcomes from digital interventions, mobile health, AI-supported care, and data-driven public health approaches with focus on equitable, preventative, and personalized healthcare.
Intervention Design & Evaluation
Developing and assessing digital interventions for behavior change, health improvement, and learning advancement through participatory, stakeholder-driven approaches.
AI Ethics & Adoption
Creating frameworks for responsible AI integration in higher education and training faculty on ethical AI implementation and use.