This year, data strategy has been front and centre, thanks to the explosion of AI and the growing awareness of the challenges that come with it. In a recent panel discussion, three data experts shared their insights on the trends shaping the ways businesses approach data.
The two sides of AI
AI has become an undeniable force in business and is now on every boardroom’s agenda. As Dane Buchanan, chief data and analytics officer at M&C Saatchi Performance, puts it: “AI is in every conversation, whether you want it to be or not, in good ways and bad ways. One of the biggest impacts AI has had over the last year is the speed at which you can test things.” The ability to test and prototype faster has unlocked exciting possibilities for many businesses.
Organisations are realising that a lot of work will be needed to get their critical unstructured data into a situation where they can use it safely
But the flip side of this rapid adoption? It has exposed long-standing issues with how companies manage their data. Adrian Kunzle, chief technology officer at Own Company, cautions that this rush to embrace AI has also “exposed the fact that, for years, many companies have not looked after their data properly”. He explains that many organisations lack a clear understanding of where their data resides, its quality and how much protection it needs.
Jack Berkowitz, chief data officer at Securiti, echoes this concern, particularly in the context of generative AI tools, which can inadvertently expose sensitive information if data governance practices are inadequate. “Organisations are realising that a lot of work will be needed to get their critical unstructured data into a situation where they can use it safely,” he says.
So what’s the solution? Berkowitz believes that companies must move quickly to lay solid data foundations. “You have the information, but it’s important to put it together into a single control plane or capability so that you understand the data, the models, the permissions, the regulations and the use cases all in the same context,” he says.
The panellists’ observations underscore the need for a balanced approach to AI adoption – one that embraces its transformative potential while acknowledging and mitigating its inherent risks.
The evolving role of the chief data officer
As businesses become more data-driven and lean into AI, the job of the chief data officer is changing fast.
“When it was introduced, it was primarily seen as an extension of the CIO’s role, focusing on infrastructure – getting your cloud and data systems right. Today, it has expanded, and is much more about how analytics can drive tangible business value,” says Buchanan.
He explains that in forward-thinking companies, the role has evolved beyond the CIO function and now has a seat in the boardroom. “I report directly to our CEO,” he says. “This gives you a different perspective on what the company expects from data, as it keeps you focused on real business problems and how data can solve them.”
This sentiment is shared by Berkowitz, who says that “the role has seen a major shift as the compliance responsibility is now secondary to the business leadership and transformation needs.” He argues that the modern CDO requires a new set of skills, focused on driving business outcomes and enabling data-driven transformation across the organisation.
But this evolution hasn’t come without challenges. Kunzle acknowledges the increasing pressures faced by CDOs. They are now expected to juggle AI adoption, data governance and regulatory requirements. To avoid CDOs getting swamped with requests and overloaded, he stresses the need for support from other C-suite executives.
Building a data-literate workforce
Of course, none of this works without a workforce that understands data. All three panellists agree that data literacy is essential for all employees, not just data professionals.
Buchanan notes that it’s becoming an expectation that teams will be using GenAI and that they have the skills to do so safely and effectively. But Berkowitz points to a gap between people’s comfort with AI in their personal lives and their reservations with the technology at work.
He notes that employees often claim to be “not a data person” despite regularly using AI-powered apps on their phones. This observation highlights the need for training schemes that make data at work less intimidating and show it can help people in their job roles. Kunzle adds that a collaborative approach to training is needed, bringing in various stakeholders to cover data governance, ethics and applications.
Takeaways for 2025
The panellists’ insights provide a roadmap for organisations planning their data strategies for next year. They emphasise the need to:
Get the basics right
As Buchanan puts it: “The focus on building strong data foundations remains as relevant in 2025 as it was in 2023 and 2024. However, now teams need to make sure those foundations are applicable for LLMs and GenAI.” This involves establishing clear data governance policies, investing in data quality initiatives and ensuring data security is baked into every stage of the data lifecycle.
Break down barriers
Berkowitz stresses the importance of getting everyone on the same page. “You need to break down the organisational borders superimposed on your data, by using disconnected operations, compliance and permissioning systems,” he says. “Otherwise, you will take ten times longer to deploy, and probably never have full confidence in what you are building.”
Embrace experimentation
Kunzle’s advice is clear: “If you’re not already, you’ve got to go play around with AI.” Organisations should encourage a culture of experimentation with AI, building sandboxes where teams can safely explore its capabilities and identify potential use cases.
Communicate the value of data
Buchanan stresses the importance of demonstrating the tangible benefits of data initiatives. This involves translating technical concepts into business-friendly language and showcasing how data can drive better decision-making, improve customer experiences and unlock new revenue streams.
By focusing on these principles, companies can turn data into a strategic asset, using AI to drive innovation and growth – while staying mindful of the risks. Data leaders, with their increasingly crucial role in the executive team, are poised to play a central role in the future of their organisations.
To find out more, visit owndata.com
