Empowering controllers with data engineering skills: a new financial era

The days of the controller as a back-office scorekeeper are over. Modern controllers work hand-in-hand with operations and strategy teams, analyzing the financial impact of new initiatives before they happen. This shift transforms controllers into co-pilots for business strategy, not just financial historians—an essential change in any data driven finance environment.

The controller’s toolkit has expanded beyond Excel. Key enablers of this transformation include:

• Real-time data integration
• Automation of routine tasks
• BI platforms and advanced analytics
• Programming skills (SQL, Python)

• AI in financial reporting and forecasting tools

As Paul Van Brabant notes, data mesh thinking, where data ownership is decentralized, demands that controllers understand data flows and governance. This is why platforms like Power BI, lakehouse architectures, and Microsoft Fabric are becoming must-haves.  These technologies strengthen both data management and the application of AI in financial reporting.

Ignoring data engineering skills is a risk finance teams can’t afford. Without automation and analytics, controllers lose their strategic relevance. In a data driven finance context, lagging behind in automation and data management directly undermines the reliability of AI in financial reporting. A recent study shows 47% of companies still lack automation (Rossum’s 2025 Automation Statistics Report), delaying financial closings and insights. And if finance doesn’t lead in data, other departments will, creating conflicting narratives and eroding trust.

Failing to adapt comes at a price:

• Delayed closings and insights
• Loss of influence in strategic discussions
• Conflicting data narratives driven by other departments

If finance doesn’t lead in data, someone else will. Controllers who ignore data capabilities risk irrelevance. - Maarten Lauwaert, Expert Practice Leader Data & Analytics

“Integrating diverse data sources in a user-friendly way, enabling dynamic pricing and operational analysis is a real challenge”, says Paul Van Brabant. Data engineers play a critical role, but controllers must understand the landscape to guide decisions effectively. In the age of data driven finance, finance professionals must combine financial expertise with data management, analytics, and AI in financial reporting.

To thrive, controllers need a blend of technical, analytical, and human skills:

• Technical skills: Data engineering basics, SQL, Python, BI tools, AI agents
• Analytical skills: Interpreting diverse data sources for predictive insights
• Soft Skills: Change management, cross-functional collaboration, data storytelling

Upskilling resources like DataCamp can help bridge the gap. Discover our training offering for Controllers

Data-driven decision-making only works when data is accurate, consistent, secure, and ethically managed. As finance takes a leading role in analytics and AI in financial reporting, controllers become custodians of data integrity and responsible access—core elements of strong data management.

This responsibility spans across four critical areas:

• Data quality & consistency
  Flawed data produces flawed insights. Controllers must ensure financial and operational datasets are clean, standardized, and free from duplication or gaps. This includes working with data engineering teams to implement quality assurance rules and validation processes.
  
  
• Governance & access control
  Not everyone should have access to all data. Controllers play a key role in designing and enforcing role-based access frameworks, ensuring individuals only access data relevant to their role. This minimizes data leakage, insider risks, and compliance violations, particularly under regulations like GDPR, SOX, and local privacy laws.
  
  
• Cataloging & traceability
  Every data point should be traceable to its source. Implementing proper cataloging systems helps maintain transparency, auditability, and trust across decentralized data environments.
  
  
• Ethical use of Data
  Controllers must lead the conversation on responsible analytics:
  • Avoiding bias in predictive models that could unfairly influence decisions.
    Ensuring AI and automation do not compromise fairness or compliance.
  • Being transparent about how insights are generated and used, maintaining trust with internal and external stakeholders.

In short, ethical stewardship is now a core competency for modern controllers. They must balance innovation with responsibility, ensuring security, fairness, and compliance while enabling advanced analytics and real-time decision-making.

Ethical stewardship of data is now part of the controller’s mandate. Maarten Lauwaert, Expert Practice Leader Data & Analytics

The role of controllers is no longer about looking backward; it’s about steering the future. They are shifting from financial historians to data-driven strategists empowered by automation, advanced analytics, and strong data management. These capabilities—combined with AI in financial reporting—define a new hybrid profile: the Financial Controlling Analytical Engineer.

This professional blends deep financial expertise with cutting-edge data engineering skills to enable smarter, faster decisions in a data driven finance environment.