Tech & AI LIVE London: Schroders' Peter Jackson's Fireside
In a packed 20-minute fireside chat on the Cyber Stage at Tech & AI LIVE, Peter Jackson, Global Head of Data Management at Schroders, unpacked the vital connection between robust data strategy and successful AI implementation.
Drawing on cross-sector experience in utilities, regulation, asset management and tech, Peter issued a clear message to the audience: “You can’t train LLMs on bad data — otherwise you’ll get bad output.”
Back to basics: strategy before shiny tools
Opening the conversation with a reality check, Peter reminded the room that organisations often dive headfirst into AI without first establishing the fundamentals.
“The CEOs are all coming back from their flights across the pond, reading the latest newspapers, and it’s ‘we must have AI by Thursday.’ My response is, ‘we must have good data management by Wednesday then.’”
A solid data strategy, he argued, is about more than technology. It requires behavioural change across an organisation, from ownership and quality to data lineage and cataloguing.
“It’s a lot around behaviours and people understanding how they treat and manage data,” he said.
He added that AI doesn’t just require good data — it exposes bad data.
“Only once you’ve installed a specific use case does it tell you that your data strategy and management have been quite poor,” said Peter.
Building culture and linking value
Culture was a recurring theme throughout the session.
According to Peter, the most overlooked element in building a successful data strategy is people.
“You have to create a culture from the top down where people value data,” he said, stressing that without leadership sponsorship, most data strategies will fail.
He believes the link between data management and business value must be made explicit.
“Good data management might bring you revenue. Good data management might reduce regulatory risk,” he explained.
This alignment, he added, helps break the direct cost-to-growth correlation by enabling operational efficiency.
For organisations starting with legacy systems and siloed infrastructure, Peter advised adopting a data mesh or data product approach.
“Identify the data you need to achieve the business goals, bring that over in a very governed way and build your outputs from there,” he recommended.
He was also clear that data strategy shouldn’t be led by IT or business functions.
“It should sit with the data team. Data is a specialist skill,” he said. “You have to be able to speak both business and technology.”
Making it real: Practical takeaways
Asked how to embed this mentality across a complex organisation, Peter favoured incentives over enforcement.
“It’s a lot more carrot than stick,” he said, underscoring the importance of board-level sponsorship and regular engagement from executives.
He also tackled the challenge of equipping employees with the right level of knowledge.
“People don’t know what they don’t know. Not everyone needs to be a Python coder, but they should understand their part in the data value chain,” he explained.
When questioned about the role of synthetic data in model training, Peter recognised its value but cautioned against its risks without proper quality control.
“You have to understand the quality of your synthetic data — and therefore your data,” he noted.
Looking to the future, Peter’s advice was to leverage the current AI investment boom to secure funding for foundational data work.
“Hang off the coattails of AI,” he said. “We did a lot of good data management off the back of GDPR budgets. Now it’s AI’s turn.”
He urged organisations to tie their data strategies into wider business transformation programmes.
“If you're installing a new CRM system to improve customer satisfaction, start with the data. What shape do you want it in? How are you going to manage it?” he added.
On the ongoing debate around cloud versus on-premises, Peter took a pragmatic stance. “Cloud first often gets translated into cloud only. Assess if it’s right.”
He concluded by reinforcing a message that echoed across Tech & AI LIVE London 2025 — the people matter just as much as the tools: “Everyone today seems to be saying that it’s about the people. People, people, people.”
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