The ROI of Trusted Data: Why Finance Leaders Must Prioritise Data Foundations

Finance leaders are under pressure to provide quick, reliable insights and demonstrate the value of every digital investment. But many still struggle with time-consuming manual reconciliations, disconnected systems and inconsistent processes that delay decisions and weaken confidence.

A lack of trusted, connected data is holding back finance leaders.

Poor quality data impedes transformations

A typical finance transformation programme promises automation, insight and agility. But when data is incomplete or inconsistent, those promises fall flat. Research from McKinsey and Gartner shows that poor data quality contributes to 40% of transformation delays, while over 70% of digital initiatives fail due to weak governance and stewardship.

The consequences of poor data are particularly acute in finance. Close cycles extend by days or even weeks, audit findings multiply and AI pilots fail to gain traction because foundational data is unreliable or non-compliant.

Take McKinsey’s Value Intelligence Platform, for example. It can help businesses understand the drivers of value for a business using Return on Invested Capital (ROIC) tree decompositions. But in order to derive meaningful insights, the data feeding these calculations must be accurate, complete and well-governed.

Without trusted data, CFOs risk making decisions on faulty assumptions, driving cost, risk and inefficiency across the enterprise.

A new definition of data ROI

The concept of data ROI has evolved. It’s no longer about the return from a technology investment; it’s about the measurable financial impact of improving data itself.

The financial benefits of trusted data are well-documented. Across multiple industries, finance leaders have achieved:

• Up to 40% reduction in reporting errors through improved reconciliation.
• 25–40% faster reporting cycles, freeing capacity for analysis and strategy.
• £3–£5 ROI for every £1 invested in data quality and governance.
• 50% fewer audit findings through better controls and transparency.

One finance organisation unified operational and financial data across multiple business units, cutting manual effort by half and enabling real-time visibility of performance. Another global enterprise achieved near-total reconciliation of ESG and finance data, reducing reporting cycles from weeks to days.

These results illustrate a clear principle: trusted data delivers measurable ROI, not just in cost savings but in speed, confidence and the ability to scale.

Driving ROI with Data Foundations

5Y Data Foundations for finance helps you calculate the financial impact of improving data and plots a path to transformation.

Designed specifically for finance and FP&A leaders, it provides a structured, four-week diagnostic that quantifies the business value of trusted data. Conducted entirely within the organisation’s environment (no data leaves the tenant), the engagement provides a practical roadmap for improvement.

Key deliverables include:

• Financial Data Scorecard – evaluating ERP and finance data for completeness, consistency, and accuracy.
• Governance & Control Assessment – ensuring PDPL/GDPR compliance and audit readiness.
• Maturity & Process Mapping – benchmarking close, consolidation, and reporting practices against leading frameworks.
• ROI Modelling – quantifying the cost of inefficiency and modelling the financial upside of improved governance.
• Board-Ready Reporting – clear, modular deliverables tailored for executive decision-making.

Data foundations turns theory into practice. It delivers a measurable board-ready baseline that ties data maturity directly to financial performance.

Becoming AI-ready

Intelligent automation and AI-driven insight are the next frontiers in finance. But automation can only succeed when it’s built on a foundation of governed, auditable data.

McKinsey’s governance research underscores this point: without strong data integrity, AI systems suffer from model drift, hallucinations and bias, all of which undermine reliability and compliance. For CFOs, the implication is clear: AI readiness begins with data readiness.

By investing in data foundations, clarity of lineage, completeness of datasets and consistency of controls, finance functions lay the groundwork for advanced analytics, predictive forecasting and autonomous reconciliation.

Finance leaders increasingly recognise that data is a financial asset in its own right, one that can be measured, managed and optimised. The same principles that govern capital investment now apply to information.

To maximise the ROI of that asset, CFOs should focus on three imperatives:

1. Sovereignty: Keep control of data within your environment, ensuring security and compliance.
2. Continuity: Establish frameworks and KPIs that survive system and platform change.
3. Speed-to-value: Deliver actionable insights quickly through targeted, time-boxed assessments.

A structured, diagnostic approach like that provided by the 5Y Data Foundations Package helps CFOs quantify both value created and risk avoided. It provides a transparent view of where data investments deliver the greatest return and where governance gaps may expose the organisation to future cost or compliance risk.

Finance leaders who treat data as a strategic asset, not a technical afterthought, gain the advantage. They move faster, report with confidence, and build the trust required for AI-enabled decision-making.
Trusted data underpins every transformation initiative, every forecast and every executive decision.

As one CFO recently put it after completing a data maturity review:

“For the first time, we can trust our numbers. Every board discussion is faster, more confident and focused on what matters.”

In an era defined by uncertainty, trust is the most valuable currency. For finance leaders, that trust begins with data