How data management enables faster, better decision-making across the organization

Garbage in, garbage out

The more an organization relies on data to make decisions, the greater the need for clarity and consistency. Data must represent objective facts everyone agrees on. Only when all team members use the same definitions for key metrics and KPIs does it make sense to bring data into strategic discussions.

Capturing the right data and interpreting them correctly is essential, but it requires more than good intentions. Data collection must be error-free at all times, with accurate input and output across systems. Data quality is the keyword. It demands both built-in controls and a data culture that puts rigor at the center.

“Data from all kinds of measurements help organizations make strategic choices,” says Alexander Declerck, Business Unit Leader Transition & Support. “That is, when they are relevant, correct, and properly interpreted. In that sense, data management also enables objective, transparent, and externally readable reporting.”

The building blocks of Data Management

Data management consists of several components, typically grouped into four domains:

• Data capture: collecting and structuring data
  Data security: safeguarding data processes in compliance with laws and regulations
• Data governance: building clear organizational structures for data and data ownership
• Data analytics: interpreting data and translating them into actionable insights

What’s often overlooked,” says Maarten Lauwaert, Business Unit Leader Management Information & Systems and Expert Practice Leader Data & Analytics, “is that data management exists to realize your data strategy. Designing that strategy in alignment with the overall business strategy must always be the starting point. More specifically, where do we want to go as a company, and what role do data play in that journey?”

Data management and data strategy go hand in hand. Executing that strategy starts with the right choices around people, processes, and systems, each requiring clear structure and direction. Data management thus functions as both the protective and guiding framework of the entire data landscape.

Data Management more critical than ever

The importance of data, data strategies, and data management continues to rise. Growing regulatory and reporting requirements from banks and other external parties are one driver, but beyond compliance, organizations increasingly recognize data as essential, even indispensable.

That awareness is shaped by two forces:  economic pressure, which pushes organizations to act and decide faster, and technological evolution, which produces ever more data and new ways to use them. Acceleration and scalability both depend on solid data management.

"In the trend toward more data and data management, we see IT becoming less dominant,” says Filip Ceulemans, CFO Services Client Partner. “It’s logical that the CFO and their team, who are most familiar with the strategic use of data and the many interdependencies involved, take the lead. The evolution toward data-driven finance and organizations is a natural next step but requires a clear vision. The CFO plays a pivotal role as a bridge between technology and business strategy.”
(Read more on this in the expert debate with four TriFinance specialists)

Building on a Strong Foundation: Data Strategy and Data Management

Experience shows that data projects succeed only when they stem from a smart, organization-wide vision and are embedded in a solid data management framework. Skipping those steps easily leads to silo problems. Typical examples include Department A developing a tool in isolation while Department B rolls out another solution. Reversing that fragmentation later is difficult.

“A data project is often a never-ending story and requires significant investment,” says Maarten Lauwaert, Business Unit Leader Management Information & Systems & Expert Practice Leader Data & Analytics. “Without a clear roadmap, you risk setting the wrong priorities and making decisions that are hard to undo.”

The rule of thumb: start with a consistent plan that includes a clear data management environment, then build the processes, systems, and culture to execute that plan successfully.

Starting with a data architecture and only later thinking about data governance is not an option. Technical and strategic choices must align seamlessly. That’s why data management serves as the foundation, not as a patch for data engineering challenges.

This includes investing in teams with robust data engineering skills, individuals who can connect business requirements with technical architectures and ensure smooth data movement between analytical and operational systems.

Organizations should start this journey as early as possible. It’s an exercise that covers all elements of data management, from building a data dictionary and implementing security mechanisms to establishing clear agreements on roles, responsibilities, and analytical processes.

“A relatively simple but often missing element of data management is a data dictionary,” notes Jonas Willems, Senior Project Consultant, Financial Institutions. “It documents all relevant definitions, conventions, and metadata. Ideally, it’s centrally accessible and easy to search. Such a dictionary ensures consistent interpretation and proper handling of data.”

Data Quality: A Continuous Priority

The various components of data management work together to ensure (master) data quality. Start by determining what level of data quality is needed to achieve your objectives. Then build the right mix of technical measures, clear organizational agreements, and embedded controls to keep data flows accurate and reliable.

“A one-time cleanup isn’t enough,” says Filip Ceulemans, CFO Services Client Partner. “The focus on data quality must be firmly embedded throughout the entire organization.”

The future of data management

Driven by technological progress and continuous waves of innovation, data management is becoming increasingly important. One striking trend within data engineering is the rise of the lakehouse architecture.

A data lakehouse combines the scale, flexibility, and exploratory capabilities of a data lake with the structure and reliability of a data warehouse. 

The result: faster and more efficient data-driven decision-making. Within a lakehouse, organizations can bring together different types of data (structured, semi-structured, and unstructured), while supporting both descriptive and advanced predictive and prescriptive analytics.

This evolution makes strong governance more critical than ever. And that, ultimately, is the essence of this story: the importance of data continues to grow every day, and attention to data management must not only keep pace but lead the way.