Mike Congdon is Head of Data & Analytics at Southern Cross Healthcare and an internationally recognised Data, Analytics & AI leader with over 25 years of experience. He has led multiple greenfield data and analytics transformations across healthcare, finance, telecommunications, logistics, and shared services. Mike is an International Institute for Analytics Expert Network member, a two-time Corinium Global Top 100 Data & Analytics Innovator, a DataIQ Global Data & AI Leader of the Year finalist, and a HotTopics Global CDO 100 award winner. A passionate advocate for data literacy, governance, and responsible AI, he is a sought-after conference speaker and trusted advisor to executive leaders.
Recently, in an exclusive interview with CIO Magazine, Mike shared insights from a 25-year journey leading greenfield data and analytics transformations across healthcare, finance, telecoms and more. On AI, Mike sees the next decade moving from retrospective reporting to embedded, autonomous decision support, with the real challenge being to balance speed and automation with governance, trust and human oversight. His advice to future leaders is to look beyond dashboards and models: focus on building trust, strong governance, data and AI literacy, and the ability to translate complexity into real business and people outcomes. The following excerpts are taken from the interview.
Hi Mike. Every data leader starts with a moment when data clicked for them. What was the first problem you solved with data that made you realize its power to change business outcomes?
Early in my career at UDC Finance, I was involved in building out business intelligence and data warehousing capabilities at a time when much of the organisation was still relying on fragmented reporting and intuition. What struck me was how often conversations changed once people could see a trusted, enterprise-wide view of performance. The first time I saw senior leaders move from debating the accuracy of numbers to discussing actions and outcomes, I realised the real value of data was not the information itself, but its ability to create alignment and drive better decisions. That experience shaped my career and convinced me that data, when managed well, can fundamentally change the trajectory of an organisation.
As a CDAO, you sit at the intersection of tech, business, and culture. How do you prioritize when all three demand attention?
I believe successful data leadership is about maintaining balance across those three dimensions. While technology often receives the most attention, technology alone rarely creates meaningful value. Equally, a strong data culture and engaged business stakeholders are essential, but without a scalable technical foundation, they can only take an organisation so far. Sustainable success comes from recognising that none of these elements can succeed in isolation.
My approach is always to start with business outcomes and work backwards. Before considering platforms, tools, or analytical techniques, I ask a simple question: what problem are we trying to solve, and how will success be measured? If an initiative does not clearly support a strategic objective, improve decision-making, enhance customer or employee outcomes, reduce risk, or create tangible business value, it is difficult to justify prioritising it. Once the desired business outcome is understood, I then focus on the technology, governance, processes, and organisational change required to enable success.
Over the course of my career, I have learned that the most mature organisations are those that advance people, process, technology, and governance together. When these elements are aligned, organisations move beyond simply producing data and reports to creating trusted insights, driving informed decisions, and embedding data and AI into the way they operate every day.
Maturing D&AI capability is your specialty. What are the three signals you look for to know an organization is truly maturing?
The first signal I look for is whether data has become embedded in everyday decision-making. In mature organisations, leaders increasingly ask for evidence, insights, and facts before making decisions. Conversations shift away from opinion-based debates and towards informed discussions grounded in trusted data. When this becomes the norm rather than the exception, it is a strong indicator that the organisation is progressing.
The second signal is trust. Organisations with mature data and AI capabilities have confidence in their data because they have invested in governance, quality management, standards, and clear accountability. As a result, they spend far less time questioning the accuracy of reports and more time focusing on what the data is telling them and how they should respond.
The third signal is ownership. In less mature organisations, data is often perceived as the sole responsibility of the data team. In mature organisations, business leaders understand they have a critical role to play in areas such as data quality, stewardship, governance, and outcomes. Data is viewed as an enterprise asset, owned collectively and managed collaboratively, rather than something that belongs to a single function or department.
D&AI is moving from experimentation to core operations. What do you see as the single biggest shift in how organizations will use data by 2030?
By 2030, I believe the biggest shift will be the move from retrospective reporting toward embedded and autonomous decision support. Historically, data has largely been used to explain what happened yesterday. Increasingly, AI will help organisations determine what is likely to happen next and recommend the best course of action in real time.
The real transformation will not be AI as a standalone capability, but AI embedded directly into business processes and workflows. Insights will be delivered at the point of decision, allowing organisations to act faster and more consistently. The challenge for leaders will be balancing this increased automation with strong governance, transparency, and human oversight.
Data literacy is the new digital literacy. How will organizations need to upskill their workforce to stay competitive?
The organisations that will thrive in the coming years will be those that view data and AI literacy as essential workforce capabilities rather than specialist skills reserved for analysts, data scientists, or technology teams. Just as digital literacy became a prerequisite for success in the modern workplace, data and AI literacy are rapidly becoming foundational skills for employees at all levels of an organisation. Individuals do not need to become technical experts, but they do need the confidence and capability to interpret information, challenge assumptions, understand risk, ask the right questions, and use data effectively to support decision-making.
At the same time, the conversation is expanding beyond data literacy into AI literacy. As generative and agentic AI become more deeply embedded in business processes, employees must understand not only how to use these technologies effectively, but also their limitations, potential biases, ethical considerations, governance requirements, and associated risks. The ability to critically evaluate AI-generated outputs will become just as important as the ability to generate them.
The workforce of the future will require a blend of data awareness, AI literacy, business acumen, critical thinking, and responsible decision-making. Organisations that invest early in building these capabilities and fostering a culture of continuous learning will be far better positioned to innovate, adapt to change, and maintain a sustainable competitive advantage in an increasingly AI-enabled world.
Thought leaders need sources of inspiration. What book, paper, or thinker has most shaped how you view data, analytics, and AI?
Over the years, I have been influenced by a number of people and frameworks, but if I were to identify one major influence, it would be Thomas Davenport. His work helped elevate analytics from a technical discipline to a strategic business capability. What resonates most with me is his consistent focus on organisational value rather than technology for technology’s sake.
I have also drawn heavily from the principles contained within DAMA’s Data Management Body of Knowledge. It provides a practical framework for balancing governance, quality, architecture, and business outcomes. Together, these influences reinforced my belief that sustainable success comes from combining strong foundations with a relentless focus on delivering value.
Music, art, or sport often teaches leadership lessons. Which non-work passion has influenced your leadership style the most?
One of the biggest influences on my leadership style has been my long-standing passion for weight training. I have been lifting weights for many years, and it has taught me lessons that apply just as much in leadership as they do in the gym. Progress is rarely dramatic or immediate; it comes from consistency, discipline, and a commitment to continuous improvement over a long period of time.
Weight training also teaches accountability. Nobody else can lift the weight for you. Success is ultimately determined by the effort, discipline, and focus you bring each day. I believe the same principle applies in business. High performance is built through individual ownership, combined with a clear understanding of the goal you are working towards.
Another lesson is that growth occurs when you are prepared to challenge yourself. In the gym, that means progressively increasing the demands placed upon you. As a leader, it means stretching people beyond their comfort zones, providing opportunities for learning and development, and supporting them as they take on new challenges. Over time, these experiences build resilience, confidence, capability, and ultimately high-performing teams capable of delivering exceptional results.
What is your biggest goal? Where do you see yourself five years from now?
My biggest goal is to continue helping organisations realise the full potential of data, analytics, and AI while ensuring these capabilities are implemented responsibly and sustainably. I am particularly passionate about building environments where data-driven decision-making becomes part of the organisational DNA rather than a specialist activity.
In five years’ time, I expect to still be operating in a senior leadership capacity, leading and advancing organisational maturity in data and AI. I would also like to expand my contribution to the global analytics community through thought leadership, mentoring, speaking engagements, and advisory work. Developing the next generation of data and AI leaders is something I find deeply rewarding and would like to pursue even further.
If you could leave one message for the next generation of data and AI leaders, what would it be?
Remember that your role as a data and AI leader is not simply to build dashboards, data platforms, or AI models. Those are important enablers, but they are not the end goal. The real purpose of our profession is to create better outcomes for people, whether that means improving customer experiences, supporting employees, reducing risk, or helping organisations make better decisions.
Technology will continue to evolve at an extraordinary pace, and the tools we use today will inevitably change. What will remain constant, however, are the fundamentals of good leadership. Focus on building trust. Prioritise strong governance. Never lose sight of the people who ultimately consume and benefit from your work. Develop the ability to communicate effectively with business leaders, understand organisational priorities, and translate technical complexity into practical business value. The most successful future leaders will be those who combine technical expertise with empathy, ethics, curiosity, and commercial acumen. If you can achieve that balance, you will create a meaningful and lasting impact both within your organisation and beyond it.