Data Strategy: The Missing Link in Digital Transformation

In boardrooms and strategy sessions across industries, digital transformation often dominates the conversation. It’s become a top priority, commanding significant budgets and resources, especially as organisations seek to leverage AI to revolutionise customer engagement and operational efficiency. Yet, while companies are making strides with new technologies, many are discovering that their digital transformation journey is taking longer and delivering less than anticipated. The reason? A crucial missing link that can bridge the gap between digital ambition—including AI—and realised potential: a robust data strategy.

The Overlooked Catalyst

For years, digital transformation efforts have focused on enhancing front-end customer experiences and automating manual, paper-intensive back-end processes. Tools like journey mapping, agile development, API ecosystems, robotic process automation (RPA), and cloud services have driven noticeable improvements.

However, a significant risk lurks beneath the surface of many of these efforts: the further balkanisation of data and decision-making. Too often, organisations inadvertently create additional silos by decentralising critical decisions across separate platforms—in apps, in marketing, in operations, in the contact centre—each working on its own fragmented view of the customer. This fragmentation not only delays progress but also makes it more difficult to achieve a coordinated, personalised customer experience.

Bridging the Gap: A Common Data Platform

Having led several major transformation initiatives, I’ve observed that organisations with a well-defined data strategy—one that brings together key data into a common platform—are better positioned to make rapid, transformative progress. Centralising key data for decision-making, rather than dispersing it across multiple systems, enables organisations to move from incremental improvements to genuine, coordinated change. This approach creates a consistent customer view, supporting AI and data-driven decision-making across the entire organisation.

Case Study: Westpac's Customer Service Hub

A good example of this comes from my experience at Westpac.? Following the acquisition of St. George Bank in the late 2000s, Westpac found itself managing two separate banking systems, which meant increased operational costs and duplicated change efforts for every regulatory or market-driven update. Neither system alone could handle the scale and functionality required, and the cost of a full replacement was prohibitive. The challenge was further compounded by duplicate channel, risk, and pricing systems, each with their own databases and decision criteria.

As we grappled with this issue, we realised that centralising analytical decision-making—particularly around customer engagement, pricing, and risk assessment—would allow us to make meaningful progress without overhauling everything at once. By centralising key customer data into a central ‘customer service hub’, we would be able to better coordinate decision-making on customer engagement, pricing, and risk across our channels and brands, without having to wait for an entire new system.?

This approach also removed complexity from both front-end channels and back-end product systems, making subsequent consolidation efforts less complex and less expensive. (Note: While full consolidation of Westpac’s systems remains a multi-year program of work, this data & decision centralisation approach has been critical to earlier improvements in areas like mortgage origination.)

Avoiding Balkanisation: Centralised, Structured Data Models

This challenge of data fragmentation isn’t unique to banking.? Many organisations face the challenge of scattered data and intelligence. In healthcare, for example, patient data often exists in silos across different departments, electronic health records, and legacy systems. This can slow progress toward creating the seamless, patient-centric experience that digital transformation promises.

The solution isn’t simply centralising all data into one massive system, but rather creating a structured, common data platform & data model that integrates key data from disparate systems. This unified approach avoids the balkanisation of decision-making and instead enables coordinated, data-driven decisions at every touchpoint. An effectively designed platform provides business users with access to a low-code analytical workbench and sophisticated AI tools, while ensuring real-time, scalable data access from source systems.

Case Study: Enabling Personalised Health and AI with a Unified Data Platform

In my current role at Quantium Health, we've seen firsthand how a well-designed data platform can enable ambitious AI-driven initiatives. We recently worked with a global health insurer who sought to deliver hyper-personalised nudges and rewards to customers that will improve customer health outcomes. Our team helped the company build a modern data platform that consolidates data from a range of disparate systems in real time and organises it into a structured, common model that is accessible through a range of low-code analytical tools across multiple businesses and geographies.

This platform doesn’t just make it easier to access and manage customer data across channels; it provides the foundation needed to build and scale machine learning models that fully leverage the organisation's data. It also supports the application of large language models in the contact centre, complaints analysis, and other personalised messaging.

Moreover, in today’s regulatory environment, this kind of platform plays a crucial role in ensuring compliance with data privacy and regulatory requirements, such as GDPR. The structured approach made it easier for the insurer to manage sensitive health data securely and meet the growing scrutiny on data usage and privacy.

The AI Imperative: Why Data Strategy is Critical

With AI and generative AI taking centre stage in digital transformation, the stakes are higher than ever. These technologies have the potential to revolutionise customer engagement and streamline operations, but without a well-structured, secure data foundation, AI implementations can falter. Clean, unified data is essential for AI to scale and deliver meaningful results.

At Quantium , we’ve repeatedly seen that organisations with a strong data platform—not just a scattered data model—are the ones best positioned to unlock the true potential of AI. Without this foundation, even the most advanced AI systems will struggle to achieve their promise.

For CEOs and Transformation Executives, the obvious question is, where to start?

From Data Lake to Data Ecosystem

A common trap we see among large organisations is what I call the “field of dreams” approach:? The argument is, “we need to make a major investment in a new data platform, and then in two year’s time life will be great.”? Typically, two years go by, and the value isn’t clear.

The other trap is what I call Proof-of-Concept-itis.? That is, dozens of small proofs of concept in AI or analytics, some of which deliver results, but none of which can scale.

The solution is to design a data strategy that includes a roadmap of analytical priorities, a target architecture, and a continuous development approach to migrating data into a centralized platform over time. This method allows teams to use and refine data sooner, rather than waiting for a full build-out that may never reach completion.

Key benefits of this approach include:

• Delivering Incremental Value: Organizations can start deriving insights and value from their data early in the process, rather than waiting for the "field of dreams".
• Flexibility: The strategy can evolve as the organization's needs change, ensuring the data platform remains aligned with business objectives.
• Quality Control: Continuous development allows for ongoing data quality checks and improvements, preventing the accumulation of "dirty" or unusable data.
• Scalability: As the platform proves its value, it becomes easier to secure buy-in and resources for further development.
• Risk Mitigation: By starting small and scaling up, organizations can identify and address potential issues before they become systemic problems.

A Call to Action for Transformation Leaders

If you’re a CEO or transformation leader and your digital transformation efforts are taking longer and delivering less than you’d hoped, it’s time to step back and ask yourself:

1.??? Do we have a clear data strategy that underpins our transformation efforts?

2.??? Are we centralising key data elements to enable coordinated decision-making, or are decisions being fragmented across apps, operations, and other silos?

3.??? Are our initiatives scalable, or are we stuck in proof-of-concept mode?

4.??? Are we equipping our people to meet evolving regulatory requirements around data privacy and security?

The next wave of digital transformation will be driven not only by new technologies but by how effectively organisations harness their data. As leaders, we must shift our focus from simply digitising processes to building data ecosystems that enable agility, insight, and innovation, while ensuring compliance with privacy regulations.

The journey may be complex, but the rewards—in customer satisfaction, operational efficiency, and competitive advantage—are immense. Elevating your data strategy and platform to the heart of your digital transformation will unlock your organisation’s full potential.

Brian Hartzer is the CEO of Quantium Health, a leading data analytics firm, and the former CEO of Westpac Banking Group.