For years, data has been regarded as the raw material of the digital economy. That’s a phrase that’s been repeated so often that it now sounds a bit like something out of the archives. Nevertheless, it remains true. But for a long time, something crucial was missing: a reliable framework in which data could not only be collected, but also shared, exchanged, and reused in a trustworthy manner.
This is exactly where the Data Governance Act comes in. It brings European data governance into German administrative practice. It thus transforms a broad European framework into a national model of responsibility. And that’s less dry than it sounds.
After all, if you want to use data effectively, you need more than just storage space. You need rules, roles, procedures, and trust.
The European Data Governance Act is intended to facilitate data use and promote data exchange across sectors and countries. The German Data Governance Act now governs its implementation at the national level.
The focus is on three topics in particular:
This makes it clear: It's not about using data at any cost. It's about using data in a structured way.
Description: Illustrative image of governance
With the entry into force of the Data Governance Act, the Federal Network Agency assumes new responsibilities. It is responsible for the registration process for data brokerage services, monitors compliance with their requirements, and registers data altruistic organizations.
It also maintains the public registry of recognized data altruistic organizations in Germany. This not only imposes obligations on providers and organizations but also gives them a clear place within a new data ecosystem.
This is important because trust in data isn't built on pretty words. Trust is built on verifiable roles.
A data intermediary service is specifically not supposed to manipulate the data it transmits at will. It is supposed to remain neutral between data owners and data users. This neutrality is not just a decorative label; it is the very essence of the model.
In addition to the Federal Network Agency, the Federal Statistical Office also plays a key role. It is tasked with supporting public agencies in the reuse of certain protected data, while also serving as a central information hub.
In practical terms, this means that information on the reuse of data should be easier to find, more structured, and more accessible. This also includes an inventory of available data resources with information on, for example, data format, data scope, and conditions for reuse.
That sounds like administration. But it's actually infrastructure.
After all, data can only be used effectively if one knows what data is available, under what conditions it may be used, and what protective measures are required.
For companies, the Data Governance Act is particularly interesting because it shows the direction in which the European data landscape is moving: away from unregulated individual cases and toward regulated data flows.
In the future, anyone who works with data will have to pay closer attention to the role they play in a data ecosystem. Is the company a data owner, a data user, an intermediary, a technical service provider, or part of a data partnership? Is the data used only internally or shared with third parties? Does it involve personal data, trade secrets, public data, or non-personal industrial data?
These questions determine what requirements arise.
And they show that data governance is not a specialized field reserved for particularly patient lawyers. It is becoming an operational prerequisite for digital business models.
One particularly interesting component is data altruism. The idea behind it is that data can be voluntarily made available for purposes of general interest, such as research, health, education, or climate protection.
The same applies here: A good cause is no substitute for a good structure.
When individuals or organizations voluntarily provide data, it must be clear what the data will be used for, who will process it, what safeguards are in place, and which organization is responsible for it.
Data altruism, then, does not operate on the principle of “Let’s just collect the data and see what happens.” It requires trust, transparency, and transparent processes.
The Data Governance Act is also relevant because data forms the basis of many AI applications. Without accessible, reliable, and legally usable data, artificial intelligence often remains little more than a very polite computer with little substance.
At the same time, simply making data available is not enough. Especially in the context of AI, additional questions arise: Where does the data come from? Under what conditions may it be used? Is it suitable? Are intellectual property rights involved? Does it contain personal information? Who documents its origin, purpose, and disclosure?
Data governance and AI governance are therefore not separate worlds. Rather, they sit at the same table—just sometimes at opposite ends.
The Data Governance Act clearly illustrates what is currently happening in the field of digital regulation: responsibilities are becoming more clearly defined, procedures are becoming more transparent, and oversight is becoming more operational.
For organizations, this means that it is not enough to address data strategy, AI strategy, and compliance separately in three different presentations. These topics must be integrated.
In practical terms, this involves questions such as:
Those who address these questions early on not only ensure regulatory certainty; they also ensure the ability to act.
The Data Governance Act isn't a flashy law. It doesn't come with bright spotlights and dramatic music. But it's important because it's intended to make data use in Germany more structured, transparent, and trustworthy.
Especially for companies that use data strategically, develop AI applications, or build data-driven business models, the message is clear: data requires governance.
Not as an obstacle, but as a prerequisite for ensuring that data can be used in a meaningful, secure, and scalable way.
K11 helps companies effectively integrate data governance, data protection, and AI governance—with clear roles, robust processes, and a realistic understanding of day-to-day business operations.