At last week’s Data & AI Symposium, I had the pleasure of hosting a panel discussion full of insights on a topic that continues to sit at the heart of every AI ambition: data strategy. 

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Titled Transforming Data Strategy into Real Business Impact, we brought together an experienced lineup of enterprise leaders:

• Peter Jackson, Global Head of Data Office at Schroders
• Steve Perry, Head of Data Delivery at Convex
• Dean Eaves, CIO at EnBW
• Jez Davies, Global Head of Data Products at Northern Trust
  
  

Each of them brought unique insight into how businesses can move beyond buzzwords and bold ambition to create a strategy that delivers real value.

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The Key to Success: Business-First Data Strategy

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The core theme across the panel was clear: if your data initiatives aren’t driving growth or cutting costs, you need to question why you’re doing them at all. Data strategy should be inseparable from business strategy. That means:

• Prioritising value creation over technical complexity
• Leaning into revenue generation and clear commercial impact
• Creating measurable links between data projects and business outcomes

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Peter Jackson summed it up perfectly: “You have to be able to prove impact. If you can’t show value, you lose credibility and momentum.”

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Avoiding Distraction: The Danger of the Shiny Tool

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In a world of ever-evolving tools and platforms, the temptation to chase the latest technology is strong. But as EnBW’s Dean Eaves noted, effective data leadership means cutting through the noise and staying focused on what matters. A strong data strategy requires leaders with:

• The confidence to say no to unnecessary complexity
• The clarity to prioritise long-term business enablement
• The discipline to invest in foundations over flash
  
  

This idea echoes themes from our recent piece on the importance of data strategy to capitalise on business value, by our CTO Tareq Abedrabbo, where we explored how getting your strategy right up front can dramatically increase your odds of AI success.

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Decouple Data from Technology

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One of the most powerful takeaways of the session came from Convex’s Steve Perry, who challenged the assumption that data strategy is just a tech problem. It’s not.

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In fact, the most effective strategies actively decouple data from technology – instead viewing it through the lens of product, business value, and culture. This was a sentiment explored in our blog on addressing inherent complexity, which calls for tackling organisational and structural complexity head-on if we want data to become a truly strategic asset.

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Empowering People: Skills, Culture, and Leadership

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While tools are important, everyone on the panel agreed that the real enabler of data transformation is people. The right skills and understanding at every level gives a broad understanding of how AI can be harnessed in the right way. Success depends on:

• Hiring smart people and giving them the space and tools to succeed
• Creating a culture of data literacy, where insights are accessible and understood across all levels
• Positioning data leaders in strategic roles, so they can influence product, operations and communications, not just IT
  
  

With rising boardroom demand for data-driven insight, this is a moment of opportunity — not just to accelerate delivery, but to revitalise how data is viewed and valued across the organisation.

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‍Getting the Foundations Right: Quality In, Quality Out

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The panel closed on a reminder that’s as old as the discipline itself but more relevant than ever:

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“Bad data in, disaster out.”

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If we want AI, automation and analytics to deliver, data quality can’t be an afterthought. Clean, trusted, governed data is the backbone of every successful initiative – and without it, even the most sophisticated systems will fail.

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Final Thoughts

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What stood out most to me from this panel was the pragmatism. No one was chasing hype. Everyone agreed: it’s time to go back to basics, revisit our strategies, and refocus on business outcomes.

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If you’re reviewing your own data strategy, consider:

• Is it aligned with business objectives?
• Can you measure its impact?
• Are your people empowered to use it effectively?
• Are you investing in data quality, not just AI tooling?
  
  

Now is the time to ask those questions – because the gap between ambition and value is only getting wider. But with the right approach, data can still be the most powerful asset you have.

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