From her early days exploring data within a traditional agency to founding a data-driven “marketing science agency,” Danielle Timmins has championed digital transformation. As Chief Digital, Data & Analytics Officer at Freerange Creatives, Danielle and her team in Cape Town and Amsterdam leverage data at every stage—from strategy to content planning—bridging creativity with data-driven insights
Ahead of her appearance at CDAO Europe, we spoke with Danielle Timmins, Chief Digital, Data & Analytics Officer at Freerange Creatives. Danielle brings a unique perspective on leveraging data within the creative industry, transforming it from a supporting tool into a powerful driver of strategy and innovation.
In this exclusive interview, Danielle shares her journey in data and analytics, the evolving role of data in the creative process, and the emerging trends and challenges facing AI, data, and digital transformation today.
Register now for CDAO Europe to hear directly from Danielle and other industry leaders about the future of data-driven strategy across industries.
C: What are some of the most pressing or interesting issues in the industry that you're looking forward to discussing at CDAO Europe?
Danielle Timmins: What I find interesting is that, while a lot of the core topics have remained the same over the past few years, the context around them has evolved—especially with the rise of generative AI.
Data quality, for instance, has always been essential, but it’s even more critical now in the context of generative AI. The importance of clean, reliable data is amplified, as any flaws can significantly impact AI outputs.
Another enduring challenge is the divide between data teams and non-data teams. These groups often struggle to communicate effectively because they approach problems so differently. And with the accelerating pace of technological advancement, that gap may widen further, which places greater demands on data leaders.
They need to manage stakeholder expectations, upskill their teams, and ensure others in the organization don’t get left behind. So, in a way, we’re still discussing similar themes, but the stakes and urgency have increased due to the rapid changes in technology.
C: How do you think the rise of generative AI has changed the conversation around ethics in data and AI, not just in theory but in practical terms for business and data leaders?
Danielle Timmins: Ethics in data has always been a key topic, focusing on managing data responsibly and minimizing bias. We’ve long discussed the importance of ensuring diverse representation in datasets and using fair algorithms. However, generative AI has made this conversation even more critical.
With generative AI, there's a heightened need for vigilance around data quality, ensuring all groups are represented, and reducing bias. The consequences of missteps are amplified, given the broader and more immediate impacts of generative AI applications.
This shift has pushed ethical considerations from just a theoretical discussion to something business and data leaders must address more rigorously and practically than ever before.
C: Europe has been at the forefront of data regulation with initiatives like GDPR, which had a big impact on customer data handling. As more regulations around data and AI emerge, especially in Europe, what do you expect on this front in the coming years, and how should businesses prepare?
Danielle Timmins: There’s often debate around whether Europe’s extensive regulation slows things down compared to, say, the U.S. But regardless of external regulations, I believe companies need to establish their own internal frameworks—focused on data quality, ethical AI use, and transparency. This isn’t just about compliance; it’s about taking responsibility.
Companies can’t afford to wait for government regulation to dictate their ethical standards. Instead, they should proactively ask, “What are we doing, how, and why?” and ensure that their practices align with both business goals and a broader positive impact. While regulation might sometimes feel restrictive, it’s ultimately about using data and AI responsibly, which has the potential to benefit society.
I’d love to see more examples of data and AI being used for good, not just profit, in sectors beyond healthcare or finance. There's a lot of focus on the potential risks of AI, but I’m hopeful we’ll see more examples of AI-driven innovation making a positive impact on people’s lives. That’s what excites me about the future.
C: What are some areas where you think AI could be used to make a positive impact?
Danielle Timmins: Healthcare is an obvious area, but financial institutions also have a huge opportunity, especially in regions like Africa where I’m based. Here, there are many initiatives aimed at helping individuals who might traditionally be excluded from financial systems. Data collaboration plays a big role in this; by combining multiple datasets, we can uncover insights that would be hidden if we only looked at data in isolation.
For example, mixing different data sources can help identify creditworthiness for people who might otherwise be invisible to the financial system. In Africa, there are massive cash-based economies, like those in townships, with billions circulating outside formal financial structures. By layering in new data sources, we can see this economic activity and potentially provide services, like credit, to those previously left out.
The same goes for healthcare. When we combine various data sources, we can answer complex questions that were previously beyond reach. This kind of data collaboration could truly transform lives and is, to me, one of the most exciting aspects of AI’s potential.
C: How has the ability of technology to influence real decisions changed over the past five years, and where do you think we're headed?
Danielle Timmins: I think we’re starting to see real progress, especially in healthcare, with AI driving some exciting advancements. However, data collaboration remains a challenge for many companies, particularly around how to do it safely within privacy regulations. But I’m encouraged by the gradual movement toward safe and effective data-sharing practices.
Over the next five years, I hope we’ll see more initiatives that go beyond profit and focus on truly transforming people’s lives. This intersection of ethical AI and data collaboration has been a passion of mine for at least a decade, and while progress can feel slow, at least it’s moving forward. I’m optimistic we’ll continue to see positive change.
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Click here to register now for CDAO Europe to hear directly from Danielle and other industry leaders about the future of data-driven strategy across industries.
