Day One

• Exploring how valuable analytics storytelling changes depending on end user, aligning visuals and granularity to what each role values
• Understanding how this differs not just based on role but also on individuals’ backgrounds, and how to identify how each internal customer consumes data based on these factors
• Discussing how to adapt reports to be visually clear, contextually relevant, and usable for different end users

• Outlining strategies to deliver data directly to end users through automated reports, notifications, and dashboards so they can access insights without navigating complex systems
• Developing methods to filter large datasets, highlight meaningful and relevant trends, and avoid overloading users with unnecessary information to keep the analytics clear
• Collaborating with end users during dashboard and report design to ensure understanding, trust, and adoption

• Discussing why limiting AI pilots to well-defined, high-value workflows reduces risk and creates measurable results before expanding into complex operational processes
• Implementing frameworks that manage nondeterministic outcomes and prepare teams for solutions that may not deliver predictable results every time
• Developing success metrics to benchmark AI pilots against traditional analytics

CASE STUDY

• Creating formalized, role-based governance policies around meeting foundational CDAO criteria for control and compliance
• Sharing our DART (Data, AI, and Reporting Technologies) Principles that explicitly define responsible AI use, bias detection, and explainability expectations
• Discussing our Technology Evaluation & Innovation Policy that institutionalizes a tiered PoC–Prototype–Pilot process, which balances innovation with risk management
• Outlining how DART embeds change management, data literacy, and role-based access. This ensures we forge a data-driven culture based on trust, responsibility, and adaptability