S7 | Ep 31 | Data Foundations Only Get Funded When They’re Tied to a Business Outcome with Barry Panayi, Sarah Barr Miller, Sid Shah and Nina Bryant (recorded live at Driven by Data LIVE 2026)

In Episode 31 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Barry Panayi, Chief Data Officer at Howden, Sarah Barr Miller, Chief Data Officer at British Airways, Sid Shah, VP Data, International at Live Nation Entertainment, and Nina Bryant, Data, Analytics & AI Partner at Baringa, recorded live at Driven by Data LIVE 2026, where they discuss how data leaders balance foundational work with transformation, and why foundations only get funded when they are tied to a clear business outcome.

The panel covers how to prioritise foundations, how to deliver them through the use cases the business actually wants, and the trade-offs between speed, scale, control and innovation.

They also discuss:

• Why foundational data work gets funded when it is delivered “by stealth” through the use cases the business wants.
• How Howden’s “bullseye” approach shows the commercial prize, prices what it takes to unblock it, and lets the business choose.
• Why data leaders need to define when foundations are “good enough” and move on to building new capabilities.
• What it means to give the board a number for how far foundational work is complete, and how acquisitions change the denominator.
• Why AI is finally giving data leaders a way to put an ROI around data foundations.
• How the gap between past AI returns and C-suite expectations of a 20% return in two years creates pressure on data teams.
• Why transformation becomes noise when the data function treats it as a mission in itself.
• How to separate AI, automation and process reinvention when the business simply asks for “AI”.
• Why speed buys the right to take time on the next piece of work, and how to be honest about what quick wins cannot yet do.
• How a low-risk, high-value use case can be fast-tracked while higher-risk decisions are considered carefully.
• Why communicating wins matters as much as delivering them, and how perception shapes how a data team is judged.
• How bottom-up AI use cases risk duplication, and how to scale the highest-value ones across the organisation.
• Why most organisations are not yet managing AI spend effectively, and what happens when the early-adopter discount ends.
• How embedding commercial insurance experts in the data team gives a business context layer.
• What the future holds for the Chief Data Officer role, and whether AI needs a separate leader.
• Why “a 1% error rate equals a 100% issue rate” in the age of AI, and how that has changed board conversations about data quality.
• What new board questions, such as sustainability and sovereignty, may come next.

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