Data Governance: The Hidden Engine Behind Finance Transformation

By Dimitris Kaskantanis, in discussion with James Keef, Co-founder of Sub One

Introduction

Finance transformation programmes promise streamlined processes, smarter systems, and sharper insights. But behind the shiny dashboards and new ERP platforms lies a less visible, yet decisive factor: data governance.

To explore why governance matters, I spoke with James Keef, co-founder of Sub One and a seasoned transformation leader. James spent half of his career at Reuters in Finance Director roles across Europe, before specialising in large-scale finance transformation, shared services, and Workday system implementations.

Our conversation revealed that the success of transformation isn’t just about the technology or processes — it’s about making sure data is defined, trusted, and owned from the very start.

“Knowing what we don’t know”

For James, the earliest governance challenge in any global transformation is grappling with hidden inconsistencies.

“In many instances it is trying to ‘know what we don’t know’,” he explained. “The higher up the data chain you go, the more opaque numbers can become. Similar items might be coded to different accounts, or data piped from different sources might be manipulated in inconsistent ways.”

This “apples with pears” problem, as James calls it, can derail standardisation efforts and create mistrust in numbers.

Principles as a “living bible”

So how does Sub One tackle these issues? For James, the answer lies in anchoring governance to agreed principles.

“We start by confirming the design principles with the client,” he said. “Decisions such as whether the primary set of books should follow group requirements, or how profit and cost centres are used, need to be defined upfront. We then treat this framework as a ‘living bible’ throughout the project.”

This bible evolves over time but provides a constant reference point. Alongside it sits the Finance Data Model (FDM) — a comprehensive definition of every dimension in use.

James recalled one client where “the project bucket had become a catch-all, with more projects than staff.” By redefining what each dimension should do, the team restored clarity and consistency.

Engaging stakeholders early

Strong data governance also smooths the path for stakeholder engagement.

“Based on the design principles and FDM alignment, it becomes easier to have transparent discussions with stakeholders,” James noted. “If stakeholders are brought on board early, adoption becomes much easier.”

Yet, he acknowledged a recurring tension: balancing the need for rigour against the realities of cost and time.

“There’s always a trade-off between trying to get the data element 100% correct at the start versus recognising that some unknowns will only surface during detailed migrations.”

When governance delivers measurable results

One story that stands out for James is a UK company with global operations. With 80% of its cost base in staff, integration between HR, payroll, and finance data was critical.

The company faced timing mismatches, manual integrations, unclear system boundaries, and inconsistent project costing.

“The success story started with choosing the right HR and Finance system,” James recalled. “In this case, the systems were integrated, giving one source of truth. HR data was aligned immediately with financials. If it was wrong, it had to be corrected at source, not adjusted later in the accounts.”

The built-in governance ensured auditability and eliminated data debates. As James put it:

“Data discussions and issues became a thing of the past. The more governance you can build into the system of record, the less human intervention is needed.”

Looking ahead: governance in the age of AI

James is pragmatic about the role of AI in finance:

“I’m not an AI expert, but I strongly believe the first stage of any future data governance is to ensure the basics are correct. Code right first time and make sure data flows properly. AI will increasingly automate coding and provide more of the analytics—but accountants will remain essential for interpreting the analysis and making decisions.”

This perspective reinforces that governance is not just a compliance exercise—it is the foundation for trustworthy automation and AI adoption.

Conclusion

Our discussion underscored a simple truth: transformation projects don’t fail because of software—they fail when data isn’t trusted. By embedding clear principles, living data models, and stakeholder alignment, organisations can transform finance with confidence.

Data governance may not be the headline act of transformation, but it is the hidden engine that keeps everything running smoothly.

About the contributors

James Keef is Co-founder of Sub One and has led multiple large-scale global finance transformation projects, with a focus on Workday implementations, shared services, and financial leadership.

Dimitris Kaskantanis is experienced internal audit and data analytics professional, currently Global Internal Audit Director at Qualco, and a member of the ICAEW Data Analytics Community Advisory Group.