While data analytics is often viewed as a highly technical field, one of its most challenging aspects lies in identifying the right questions to ask. Beyond the expected skills of summarizing data, building visualizations, and generating insights, analysts must also bridge the gap between complex data and non-technical stakeholders.

This presentation features a case study led by two women from the Research and Data Analytics team at the Minnesota Department of Labor and Industry. It illustrates the end-to-end process of transforming raw data to create a fully developed dashboard that delivers actionable insights for the department’s Apprenticeship unit.

We will share key challenges encountered along the way, from handling issues of data quality and accessibility to adapting the tool for the differing needs and expectations of new stakeholders. Attendees will leave with actionable strategies for transforming messy datasets into clear, impactful dashboards that drive smarter decision making.

As healthcare organizations accelerate their adoption of AI and data-driven systems, the challenge lies not only in innovation but in responsibly scaling these technologies within clinical and operational workflows. This session examines the technical and governance frameworks required to translate AI research into reliable and compliant real-world applications. We will explore best practices in model lifecycle management, data quality assurance, bias detection, regulatory alignment, and human-in-the-loop validation, grounded in lessons from implementing AI solutions across complex healthcare environments. Emphasizing cross-functional collaboration among clinicians, data scientists, and business leaders, the session highlights how to balance technical rigor with clinical relevance and ethical accountability. Attendees will gain actionable insights into building trustworthy AI pipelines, integrating MLOps principles in regulated settings, and delivering measurable improvements in patient care, efficiency, and organizational learning.