Scott Clendaniel

Chief Learning Officer

Nautilus Academy

Organizations that build trusted, well-governed, and accessible data foundations are more likely to scale AI successfully and generate measurable business value. In contrast, fragmented, poorly governed data environments often limit progress, regardless of advances in models or tools.

This session provides a practical framework for evaluating and strengthening the data foundation required for AI initiatives. It will examine the trade-offs between building and buying capabilities, the role of governance and cost management, and how these factors influence the ability to move from experimentation to production. Attendees will gain actionable guidance for assessing their current state, prioritizing investments, and aligning data initiatives with business outcomes.

Topics include:

• How to assess your organization’s data foundation across key capability areas, including architecture, data quality, governance, lifecycle management, unstructured data, semantic layers, and organizational readiness
• Why organizations that prioritize data engineering, observability, and governance are more likely to achieve sustainable AI outcomes than those focused primarily on model development
• Approaches for mapping AI and ML use cases to underlying data requirements and prioritizing investments based on business value and ROI
• How to develop a phased data readiness roadmap, from assessment and prioritization to building core capabilities and scaling for production