Data Strategy and Data Management: Two prerequisites for Data-Driven Finance

The need for structure is strong, and for good reason. It starts with the multitude of data requests that Finance receives, being by definition the organization’s data gatekeeper and responsible for master data governance in cross-functional processes

“There are numerous internal stakeholders,” says Alexander Declerck, Business Unit Leader Transition & Support at TriFinance. “The CEO wants certain data, as do procurement, sales, and HR. On top of that come external stakeholders such as bankers, auditors, and investors. Each requires specific information and expects it to be accurate, complete, and available on time which is why a clear data and analytics strategy matters.”

On top of that, some profiles prefer to work with data themselves rather than rely on predefined dashboards or reports. “To get to the most relevant insights, they take their own steps in data processing, management, and analysis, essentially building informal finance data analytics capabilities,” explains Maarten Lauwaert, Expert Practice Leader Data & Analytics. “That’s not a bad evolution, but it does require vigilance. When departments sit at different points on the spectrum from spoon-fed to self-service, it becomes even more important to strengthen the organization and safeguard efficiency, quality, and security.”

“The risk is that everyone ends up creating their own version of the truth,” adds Alexander Declerck. “The resulting debates only undermine efficiency. The key is to establish a clear framework with shared definitions, interpretations, and tools, so everyone looks at the same data in the same way and siloed data is avoided.”

Another shift that heightens the need for solid data management is the move from descriptive to predictive and eventually even prescriptive data. When it comes to forecasting, Finance still lags behind some other functions, but the potential is significant. In planning and budgeting, and well beyond, organizations must consider an advanced analytics strategy and a targeted predictive analytics strategy that specifically addresses predictive analytics in Finance.

Maarten Lauwaert: “With predictive analytics—AI-driven or not—you can anticipate which customers might pay late or which suppliers may run into trouble. Acting on those insights proactively gives your organization a real edge. But that only works if the foundation is solid. Both current and historical data must be accurate. Data flows must run smoothly. Platforms and models must be properly set up and consistently maintained. Data flows must run smoothly. Platforms and models must be properly set up and consistently maintained, which is why enterprise data analytics strategy and clear MDM (master data management) practices are essential.

In the shift toward a more data-driven organization, Finance has a pivotal role to play. “Finance and data analytics together create the evidence base for strategic decision-making,” says Maarten Lauwaert. “Finance professionals should strengthen their knowledge in this area. Understanding both the opportunities and the technological and organizational impact helps them take the right steps, and drive the creation of the framework every company needs.”

That framework starts with a well-thought-out data strategy and often requires investment in data science skills and tooling so insights can scale. Alexander Declerck: “Analyze carefully which data you truly need and dare to differentiate. What are the priorities and what are the nice-to-haves? It’s a cross-functional exercise: mapping organizational needs, aligning them, and translating them into clear KPIs.”

That also raises key questions around data management. “What level of data quality are we aiming for?” asks Maarten. “The related costs make that a strategic decision in itself.”

A CFO with at least a basic understanding of the technical dimension is ideally placed to act as a bridge. Maarten: “The business understands where the company needs to go but often lacks insight into technical implications. The technical experts know unified data architecture and lakehouse architecture inside out but not the strategic roadmap or operational consequences. Only by connecting those worlds can you truly be a business partner.”

Prioritizing critical data points is not a minor detail when developing a data strategy. It is closely linked to data management requirements. Alexander Declerck illustrates this with an aviation example: “The cockpit of an old Boeing had hundreds of instruments. If one turned red, that was barely visible. Modern aircraft limit themselves to two clear screens with straightforward alerts.”

This illustrates Finance’s modern gatekeeper role: avoiding data overload while looking ahead.
“Many organizations still spend too much time digesting the past,” Alexander notes. “Forward-looking models are far more valuable than yet another variance analysis, whose insights quickly become outdated. The more predictive the data and the more automatic alerts, the better you can steer proactively. That’s why rolling forecasts are preferable to time-consuming year-end budgeting, which is often obsolete by January.”

Modern applications, of course, require modern systems. The same principle applies: less is more. Departments need to collaborate, break down data silos, and replace disconnected applications. Experts advocate centralized data platforms (the next generation often built on platforms like Microsoft Data Fabric) increasingly supported by built-in, low-friction AI agents. To make this work you must invest in data engineering skills and training such as Power BI training, while ensuring tools like a data room (data room finance)  exist for secure sharing with external stakeholders.
Maarten Lauwaert: “Platform technology offers flexibility and room for innovation within a robust structure. It creates the right balance of self-service, forward-looking insights, and governance.”

In the less is more approach, Maarten Lauwaert sees a key tip for moving toward a better data-driven decision-making: “You don’t need to do everything at once. Once the big picture is clear, you can make progress step by step.” The prerequisite is that management actively builds a data-driven culture and supports the developing a data analytics strategy process. “Leadership must champion both the data strategy and its implementation. Change management is essential.”

Making it an ongoing practice also allows for regular reflection. “In a changing world, data needs evolve. Regularly review which data points, dashboards, reports, or models are still in use. Remove what isn’t. Excess data distracts employees instead of supporting them, and you quickly end up back at square one.”