How Data Use Within Public Finance Management Can Support Transformation

Why aren't Public Finance professionals using data to transform public finances?

Public finance management (PFM) sits at the core of effective governance. It determines how governments plan, allocate, and monitor resources — and how they deliver results for citizens. Yet, across many administrations, financial decisions are still based on partial or delayed information. As governments move towards digital transformation, the smarter use of data in public finance management has become essential.

When properly collected and applied, data allows governments to see clearly where money is being spent, what value is being achieved, and how performance can be improved. By using data at every stage — from budget planning to procurement and performance review — PFM systems can become more transparent, efficient, and accountable.

This article explores how better use of data can drive transformation across PFM. It outlines the opportunities created by digitalisation, the challenges governments face, and practical steps to help decision-makers use data to strengthen financial management and service delivery.

The Transformative Power of Data in Public Finance Management

In recent years, the ability to collect, manage, and analyse large volumes of financial and administrative data has changed what is possible in government. Data in public finance management provides the detailed evidence needed for informed decision-making and improved outcomes.

When properly integrated, data supports more credible budgeting, more efficient procurement, stronger performance-based budgeting (PBB), and better detection of mismanagement or corruption. It gives finance leaders a clearer understanding of the cost and impact of every programme or activity — and helps them make more balanced, evidence-based decisions.

Data-driven public finance enables governments to move away from top-down, aggregated reporting towards detailed, actionable insights. The result is more trust in public finances and a stronger link between spending and results.

Why Data Matters for Better Governance

Improving Budget Credibility

Accurate data helps governments prepare realistic budgets based on real costs and verified needs. Without reliable information, budgets can become disconnected from operational realities. Data-driven budgeting builds trust in fiscal plans and supports a more predictable flow of resources to departments and local governments.

Enhancing Performance-Based Budgeting

Performance-based budgeting relies on clear, measurable data. By linking resources to outcomes, data allows ministries to assess efficiency and value for money. This helps ensure that public funds are directed where they have the greatest impact.

Strengthening Public Procurement

Procurement remains one of the most significant areas of public spending — and one of the most vulnerable to inefficiency or fraud. Data can identify inflated prices, irregular suppliers, or duplicated contracts. It also supports fair competition by making benchmarks and procurement outcomes transparent.

Increasing Accountability and Oversight

Transparent data allows both internal auditors and the public to see how funds are being managed. Patterns of spending can be analysed, compared, and scrutinised, improving accountability at all levels of government.

Why Data Systems Are Underused in Many Governments

Despite the clear benefits, data systems within PFM remain underdeveloped or underutilised in many countries. Implementation often faces barriers such as fragmented data platforms, limited analytical capacity, and unclear institutional mandates.

In many cases, ministries manage separate financial, procurement, and administrative systems that are not connected. This lack of integration makes it difficult to create a complete picture of government spending and performance. Manual data consolidation — often through spreadsheets — is slow and error-prone.

Another challenge is perception. Many institutions see data management as costly or technically complex. This has discouraged adoption, particularly in low- and middle-income countries where resources are constrained.

However, the rise of digital infrastructure, interoperability, and automation now offers an opportunity to change this narrative. Governments can make better use of existing systems, integrate data across departments, and unlock new insights — all without requiring expensive system replacements.

Digital Transformation: Making Data Work for PFM

Digital transformation is changing how governments manage their finances. Advances in connectivity, analytics, and automation have made it possible to capture, process, and use financial data more effectively than ever before.

Building Interoperability

Interoperability — the ability of systems to exchange and understand information — is essential for modern PFM. It allows different ministries, agencies, and departments to share financial and administrative data securely and consistently.

By connecting existing systems through Application Programming Interfaces (APIs) and standardised data protocols, governments can automate data flows in real time. This ensures that financial information from schools, hospitals, or local authorities is captured directly into central systems, reducing duplication and delays.

Automating Data Collection and Reporting

Automation eliminates the need for repeated manual input, saving time and reducing errors. When data flows automatically from source systems into central databases, finance officials can access accurate and up-to-date information to support decision-making. This also enables near-real-time reporting, improving budget control and responsiveness.

Standardising Data for Consistency

To make interoperability and automation effective, governments need standard data structures. Defining consistent categories — such as cost centres, programme codes, and performance indicators — ensures comparability across institutions and levels of government. Standardisation also strengthens the link between financial and performance data, supporting performance-based budgeting.

An Example Data Schema Components for Cost Accounting Integration

1. Cost Centre Hierarchy

Field	Description
Cost_Center_ID	Unique identifier for the cost centre.
Cost_Center_Name	Name of the cost centre (e.g., Payroll).
Parent_Cost_Center_ID	Identifier for the parent cost centre. Use NULL for the top level.
Level	Numerical level of the cost centre in the hierarchy (e.g., 1 for top level).
Org_ID	Identifier of the associated organisation.
Description	Description of the cost centre.

2. Service Disaggregation Hierarchy

Field	Description
Service_ID	Unique identifier for the service.
Service_Name	Name of the service (e.g., Emergency Care).
Parent_Service_ID	Identifier for the parent service. Use NULL for the top level.
Level	Numerical level of the service in the hierarchy.
Program_ID	Identifier of the associated programme or project.
Description	Description of the service.

3. Linking Cost Centres and Services

Field	Description
Mapping_ID	Unique identifier for the mapping.
Cost_Center_ID	Identifier of the associated cost centre.
Service_ID	Identifier of the associated service.
Allocation_Percentage	Percentage allocation of the cost centre to the service.
Start_Date	Start date of the mapping.
End_Date	End date of the mapping (nullable for ongoing).

Using Data Analytics and Machine Learning to Drive Insights

As data systems mature, the next step is to use data analytics and machine learning (ML) to gain deeper insights.

ML models can identify unusual spending patterns, detect anomalies, and forecast financial risks. Predictive analytics can highlight where cost overruns are likely to occur, allowing managers to take early action.

These analytical tools make oversight and audit functions more proactive. Instead of waiting for year-end reports, governments can use live data dashboards to track trends and spot problems before they escalate.

While ML in PFM is still developing, even basic analytical tools can provide valuable benefits — from detecting inefficiencies to improving service delivery planning. As digital capacity grows, the potential to use data for predictive and prescriptive analysis will expand rapidly.

Embedding Data in Core PFM Processes

To deliver lasting transformation, data must be fully integrated into the core processes of public finance management. This requires clear institutional frameworks, capacity development, and phased implementation.

Institutional Integration

Data use should be embedded in all core PFM activities — budget preparation, procurement, monitoring, and performance evaluation. Finance ministries should lead integration efforts, working with line ministries to ensure data is not just collected but actively used in decision-making.

Building Capacity and Skills

For data-driven PFM to succeed, governments need staff who can collect, analyse, and interpret data. This means investing in both technical training and management awareness. Data literacy should become a core skill for all financial and policy professionals.

Iterative and Practical Implementation

Transformation should be approached incrementally. Starting with pilot projects that deliver clear benefits — such as identifying procurement inefficiencies or improving reporting accuracy — helps build support for broader reform. Each success reinforces the value of data-driven approaches and encourages further adoption.

Common Challenges and Practical Solutions

Fragmented Systems

Many governments operate multiple financial and administrative platforms that are not connected. The solution lies in creating shared data standards and enabling interoperability so systems can communicate effectively.

Limited Budgets and Capacity

Budget constraints often limit investment in new systems. Governments can start small by enhancing existing digital infrastructure and adopting open-source tools. Partnerships with technology providers or regional institutions can also support innovation at lower cost.

Resistance to Change

Cultural barriers can slow data reforms. Communicating the benefits of data-driven decision-making and demonstrating real examples of improved efficiency help to build ownership among staff and leaders alike.

Data Quality and Security

Reliable data requires clear governance. Governments should establish protocols for data validation, privacy, and security to ensure information is trusted and well-protected.

Policy Priorities for Data-Driven Public Finance

To make the most of data in public finance management, governments should prioritise the following actions:

1. Invest in Digital Infrastructure – Strengthen the systems that support data generation, storage, and analysis.

2. Adopt Common Data Standards – Ensure financial and performance data are defined consistently across all sectors.

3. Enhance Interoperability – Connect data systems across ministries using APIs and shared protocols.

4. Develop Data Skills – Build technical and analytical capacity through training and recruitment.

5. Use Analytics and ML Responsibly – Establish clear ethical frameworks for automated analysis.

6. Integrate Data into Decision-Making – Embed data directly into budgeting, procurement, and performance reviews.

The Future of PFM: From Reporting to Intelligence

The future of public finance management lies in systems that learn and adapt continuously. Data makes this possible.

By linking financial data to outcomes and performance indicators, governments can identify what works, replicate success, and adjust failing programmes early. Over time, this creates a learning cycle within public administration — one that improves efficiency, transparency, and public trust.

A truly data-driven PFM system moves beyond compliance to become a tool for strategic management. Decisions are no longer based on guesswork or politics but on solid evidence drawn from accurate, timely information.

Conclusion: From Information to Transformation

Data is no longer just an administrative by-product; it is a strategic asset. When governments use it effectively, they can deliver better services, strengthen accountability, and manage resources more responsibly.

The transformation of PFM depends on more than technology. It requires leadership, clear policies, and a commitment to using data for continuous improvement. The focus should not be on collecting more data, but on using the data already available — consistently, intelligently, and collaboratively.

By combining institutional ownership with digital innovation, governments can make public finance more efficient, transparent, and responsive to citizens’ needs. Data, when used well, has the power to transform not just systems — but the way governments deliver value to society.

Key Points

• Data in public finance management enables better decisions, transparency, and accountability.

• Strong data systems improve budgeting, procurement, and performance-based management.

• Digital tools such as automation and interoperability make data collection faster and more accurate.

• Analytics and machine learning help detect inefficiencies and predict risks.

• Integration, capacity-building, and culture change are critical for success.

Recommendations

1. Make data use a central principle of public finance reform.

2. Prioritise interoperability and data standards across all government systems.

3. Build the technical and analytical capacity of finance and policy teams.

4. Start with achievable projects that demonstrate value quickly.

5. Establish governance mechanisms for data quality, ethics, and security.

6. Promote collaboration across ministries and with external partners.

Final Thought

Data is shaping the future of how governments plan, spend, and deliver. It brings precision, transparency, and insight into every stage of the public finance cycle. By taking a strategic approach to data use — integrating it into everyday decisions and investing in the people and systems that make it work — governments can drive lasting transformation.

For more insights on digital transformation, governance, and innovation in public finance, subscribe to George James Consulting’s articles at www.GeorgeJamesConsulting.com

References:

Lorena Rivero del Paso, Chloe Cho, and Ramon Narvaez Terron. "Making Cost Data Work for Public Financial Management", IMF Working Papers 2025, 159 (2025), accessed October 24, 2025, https://doi.org/10.5089/9798229021470.001