Flipping the Data Model

A Fresh Perspective on Data Transformation

Financial institutions know the challenges of fragmented data: siloed solutions, data swamps, and legacy architectures leave leaders struggling to scale effectively. The challenge is not just about strategy; it is about execution.

So how can organisations shift from good intentions to real, scalable outcomes?

From Concept to Execution

Flipping the model means beginning with business needs and partnering with IT to design a scalable, controlled, and usable data ecosystem. 

This right-to-left (consumer-to-producer) approach ensures that the data businesses require drives the architecture, not the other way around.

How This Works in Practice

• Business determines the data needs: alignment with decision-making, reporting, and regulatory requirements.

• Data champions are identified: subject matter experts ensure data is usable, accurate, and risk-aware in their respective domains, working collectively across the organisation.

• Controls are embedded and automated: errors are logged, governance is applied, and data quality is actively managed.

• Governance processes are defined: clear ownership, metadata management, and change control are essential.

• Technology determines the best delivery mechanism: optimising for efficiency, scale, and resilience once governance and requirements are in place.

The key is partnership: business needs set the direction, and technology enables scale and resilience.

Flipping the model reveals the clarity to act.

Challenges to Watch For, and How to Overcome Them

• Lack of data ownership: without business accountability, the approach unravels.

  • Mitigation: assign data champions who are responsible for maintaining clarity, structure, and continuous refinement.

• Misalignment between business and technology: business leaders want agility, while technology teams need structure.

  • Mitigation: embed governance from the start, creating a common language.

• Over-engineering the process: some firms aim for perfection before execution, delaying action and eroding momentum.

  • Mitigation: use an agile, iterative approach, proving value in phases rather than waiting for a “big bang” implementation.

• Lack of executive sponsorship: without strong leadership buy-in, data initiatives stall in bureaucracy.

  • Mitigation: establish tone from the top, reinforcing that data isn’t just a technical concern, it’s a business asset.

The Takeaway

Data transformation requires more than strategy on paper. It requires execution that embeds governance, accountability, and business-driven design from the outset. By putting business needs first and building backward, organisations can scale, control costs, and deliver real business value without getting caught in endless, resource-intensive initiatives.

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