Grip and Value: Balancing Defensive and Offensive Data Management

In the first part of this Insight series, we posed a fundamental question: why are data management and governance essential? We concluded that these practices are the foundation of data-driven work and value creation. They’re not just theoretical frameworks but powerful organizational tools.

In this second part, we go one step further. We explore how organizations can not only maintain control over their data but also realize its value. That starts with balancing two perspectives: the defensive and the offensive approach to data.

We often see organizations approaching data management either primarily from a defensive perspective – such as risk and compliance requirements (GDPR, Archiving Act, Data Governance Act, AI Act, audit obligations) – or from an offensive strategic goal, such as improving services, smarter policymaking, or AI use. But to create lasting impact, it's essential to balance both perspectives. Only then can a flexible and future-proof data organization emerge.

An overly defensive approach leads to sluggishness, a lack of agility, and passive data teams. On the other hand, a purely offensive strategy without governance results in data sprawl, duplication, unreliable information, and compliance risks.

The core question is: how do we find that balance as an organization? How do we ensure that data governance and data management not only comply with laws and regulations, but also contribute to societal challenges, innovation, and strategic goals?

Effective organizations adopt a dual data strategy, explicitly incorporating both defensive and offensive objectives.

Every data-driven organization starts with a solid foundation. Defensive data management focuses on managing risks, complying with laws and regulations, and creating reliable conditions for responsible decision-making. This includes ensuring data quality, clear definitions, and traceability.

Ownership is made explicit, responsibilities are allocated properly, and systems are configured to comply with external frameworks such as GDPR, Open Government Act (Woo), Archiving Act, and the Dutch BIO standard. Data is secured, access is controlled, and audit questions can be answered efficiently.

These “hygiene factors” are not goals in themselves – they enable confident growth. First control, then growth.

Once the foundation is solid, space is created for strategic data use. Offensive data management is all about value creation: connecting data to policy goals, organizational priorities, and social challenges.

We help organizations embed data-driven working, make evidence-based decisions, and optimize internal processes. This includes developing data products, self-service analytics, and dashboards that actually get used. We also support the responsible implementation of AI and RPA technologies for better insights and innovation.

By balancing defensive and offensive elements, we enable scalable and sustainable solutions: less ad-hoc Excel work, more reusable datasets, and faster, better-informed decisions.

The power lies in the combination of defensive and offensive data management. Organizations that integrate risk control and value creation achieve tangible benefits: faster access to reliable data, fewer interpretation issues, and accelerated decision-making.

At the same time, they reduce risks, meet oversight requirements, and build trust among staff, leadership, and external parties. Innovation flourishes, collaboration becomes easier, and management costs drop thanks to reuse and standardization.

We combine substantive expertise with a change-oriented approach. We start with daily practice, assess data maturity, and determine together where the most impact can be made.

We then implement a working structure: define ownership through clear roles, set up a data governance desk, and implement a company-wide data quality framework. With data catalogs, metadata registration, and data stewards, we make data findable and usable.

Simultaneously, we invest in people: embedding data-driven behavior and strengthening skills so everyone can work with data – from policy maker to administrator.

With this approach, we create balance: strong on content, realistic in execution.

In the third and final part of this trilogy, we explore the question: where should an organization start? Which parts need attention now, and which later? We introduce a risk-based prioritization model for data governance and management – with practical tools for anyone who wants and needs to make data work.