ABOUT - Data Science Symposium 2026

Today’s data-science environment is filled with both opportunity and uncertainty. As the pace of change accelerates, organizations are challenged not only to build technical capabilities, but to translate them into meaningful, real-world impact.

This symposium explores what it truly takes to stay relevant in the next era of data science — not by retrofitting new technologies into existing processes, but by rethinking how systems, skills, and organizations work together. Across five complementary tracks, we will examine how to design, build, deploy, and scale data science and AI solutions that deliver measurable impact.

Why Attend?

Hear from industry leaders shaping the future of AI and data science
Explore five focused tracks covering technology, strategy, and business impact
Learn from real-world case studies and implementation success stories
Connect with professionals across analytics, AI, and business leadership
Leave with practical ideas you can apply immediately

Customize your symposium experience. During each breakout session, attendees can choose from presentations across the five tracks listed below, allowing them to focus on the topics most relevant to their goals and challenges.

Track 1: Activating Agentic Ecosystems

As AI systems move beyond isolated use cases, organizations must rethink how work gets done—not retrofit agents into existing processes. This track explores how to design ecosystems where humans, agents, and systems collaborate dynamically, requiring new architectures and technical fluency.

Track 2: Data Foundations & Access at Scale

Impactful AI starts with intentional data design, not incremental fixes to legacy systems. This track focuses on building modern data foundations that support scale, accessibility, and speed—while requiring evolving technical skill sets in data engineering and governance.

Track 3: From Hype to Value: Driving ROI

As pressure to demonstrate value increases, organizations must rethink how they prioritize, measure, and deliver impact from AI investments. This track highlights approaches that connect technical work to business outcomes.

Track 4: Building & Deploying AI Systems

Delivering impact requires rethinking how systems are built for real-world use. This track focuses on the technical depth needed to develop, deploy, and scale AI systems, emphasizing engineering discipline, integration, and maintainability.

Track 5: Trustworthy AI

Trust is not a one-time design decision—it is discovered and strengthened throughout the lifecycle of an AI system. This track explores how organizations build transparency, reliability, and governance into their work and adapt as new risks and expectations emerge.

AI does not create impact on its own. Organizations do. As an academic health system, Cincinnati Children's Hospital Medical Center must improve patient care, accelerate research, and educate the next generation of healthcare professionals while navigating the rapid evolution of AI. This keynote shares our journey to becoming an AI-ready organization by building the capabilities needed to scale data and AI responsibly across clinical, research, educational, and operational domains. Attendees will learn how governance, workforce development, and practical use cases transformed isolated initiatives into enterprise capabilities, creating the trust and alignment needed to turn AI investments into meaningful and lasting impact.

Predictions are not decisions. A decision is a choice among alternatives, and managers are ultimately accountable for choosing the path that best balances risk and return. As AI makes predictions increasingly accessible, the value of data science shifts from generating answers to helping organizations make better decisions. Decision Intelligence does exactly that. Modeling decisions, not just data, provides leaders with smarter alternatives and clearer trade-offs. Attendees will learn why Decision Intelligence is becoming a critical capability in the next era of data science and how it can help professionals and organizations stay relevant by turning analytical capability into measurable impact.