#361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks

2026-05-25 · Source: DataFramed · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

Veronika Durgin, VP of Data at Saks Global, discusses the evolving landscape of data careers amidst generative AI advancements. She posits that while AI automates tasks like SQL generation and dashboard creation, human roles are shifting towards translation, data modeling, and judgment. Durgin highlights analytics engineering as the future's "catch-all" role, emphasizing its focus on understanding business nuances and developing conceptual data models. She notes that routine BI reporting and basic data engineering are becoming less specialized. Key skills for data professionals now include critical thinking, active listening, translating business needs into data structures, and navigating organizational politics. Durgin advocates for data teams to be proactive business partners, promoting centralized data governance with decentralized analytics. She stresses the importance of robust conceptual data modeling as a prerequisite for effective AI integration, reducing data reconciliation issues, and enabling future innovations like universal semantic layers.

Key takeaway

For data leaders and professionals adapting to AI, prioritize developing "bridge skills" like conceptual data modeling and business translation. Focus your team's efforts on understanding nuanced business needs and proactively engaging stakeholders, rather than just fulfilling requests. Invest in continuous learning and allocate time for exploratory projects to stay current and drive innovation, ensuring your data foundation supports future AI capabilities and avoids costly reconciliation issues.

Key insights

AI shifts data roles from production to translation and conceptual modeling, making analytics engineering central.

Principles

• Automation requires mastery of fundamentals.
• Data teams must proactively engage business.
• Agility means small increments, frequent iteration.

In practice

• Focus on conceptual data modeling.
• Prioritize asking questions and listening.
• Dedicate time for creative "happy place" projects.

Topics

• AI in Data
• Data Careers
• Analytics Engineering
• Conceptual Data Modeling
• Semantic Layers
• Data Team Strategy

Best for: CTO, VP of Engineering/Data, Executive, Data Scientist, Data Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by DataFramed.