Why data governance needs to evolve
Traditional data governance often puts control in the hands of a single person or team. It may sound safer, but in practice, it slows everything down. That’s why we can't rely on centralized models anymore. We need a system that’s flexible, scalable, and secure, one that can handle growing volumes of data without holding teams back.
For this, we should use decentralized data governance. This approach shares control across teams and gives people self-service access to the data they need, while still following clear, automated security rules. It makes data faster to access and removes bottlenecks that slow teams down.
In this guide, you’ll learn what decentralized data governance means, the key principles behind it, the benefits of implementing it, and how to put it into practice in your organization. By the end, you’ll know exactly how to make governance a strength as your company grows.
What decentralized data governance is
Decentralized data governance is an approach where each team manages its own data with shared rules across the company. Unlike centralized models, it gives teams more freedom while still keeping things secure and consistent.
Centralized governance uses top-down control which puts one team in charge of all data. They set the rules and manage everything. This rigid structure slows down data use because it creates bottlenecks for teams to access data in real-time and policies become more complicated when data grows in volume.
On the other hand, decentralized governance gives more control to teams that create and use the data. It eases domain ownership (allocating responsibility and accountability to specific data domains based on business units and functionalities) and creates federated policies (framework in which entities in an organization are given the authority to govern their own data under specifically defined standards), which speeds up the governance process.
Decentralized governance doesn’t mean there is no control. It means teams have freedom, but within the set rules. Everyone follows a shared framework of management and policy adherence, so data stays trusted and well-managed.
Core principles of decentralized data governance
Decentralized governance is based on a few key principles that guide how teams manage and protect data. They are:
Federated ownership
When governance is distributed across teams and domains, experts are empowered to manage their own data policies. That way, people with the most intimate knowledge of the data oversee its quality, compliance, and security. This approach also promotes agility and accountability within organizations.
The Scottish Environment Protection Agency (SEPA)’s federated center of excellence (COE) model allows over 1,000 of its employees to access data daily without compromising governance standards. By assigning data experts directly to business teams, SEPA combines the benefits of centralized data oversight with the flexibility of local expertise.
Interoperability & standardization
Decentralized governance needs consistency and that’s why we use metadata-driven policies. These policies act like a common language across teams to make sure everyone follows the same rules, even as they move quickly and independently.
The company, Procter & Gamble (P&G), once faced challenges with its complex data systems because of a lack of central data control. To overcome these problems, they deployed a data quality software for managing their master data across multiple SAP instances. It helped them transition from manual to automated data quality checks. This improvement in data accuracy helped standardize data governance practices across all units.
Policy automation
We must automate data policies by codifying them into a system that itself keeps things running smoothly. This is the smartest way to cut down manual bottlenecks and achieve higher compliance levels because instead of relying on manual checks, rules are built right into the system. As a result, everything happens faster and more reliably.
Self-service access with guardrails
Decentralized governance emphasizes organizations to empower their teams with self-service data access without risking security and compliance. To do so, implement a data governance framework built on a strong foundation of agile principles and integrate automation tools to optimize every process. Such systems allow teams to use data freely within their departments and take accountability for adhering to compliance policies.
GE Aviation has smartly adopted this self-service access principle by creating a self-service data governance framework. Their employees now use this Self-Service Data (SSD) program across various departments to access and analyze data independently. SSD has fostered a data-driven culture in GE with more than 2,000 data products created by over 1,800 users worldwide.
Key benefits of decentralized data governance
77% of people believe that they need to adjust their current governance approach with time because centralized data teams suffer from a lack of domain expertise. That’s why more and more organizations are turning to decentralized governance because it makes decision-making more open and flexible. But that’s not all, there are many more reasons for this shift. Let’s see what they are:
Failing to achieve data compliance results in both financial and reputational loss (20 million euros or 4% of a company's global turnover as per GDPR). However, decentralized governance uses automated policy enforcement, which means that no one has to verify everything manually.
When governance is shared across teams instead of handled by one overloaded group, each team manages its own data, so there's no long wait or pile-up of requests. As the company grows, governance grows without slowing things down.
Decentralized governance works well with modern data setups like data meshes where each team owns and manages data like a product in a decentralized data mesh. This aligns with the concept of shared governance, where teams make decisions and adhere to shared rules.
How decentralized data governance works in practice
During COVID-19, Panera shifted to a decentralized model to encourage a culture of data sharing and collaboration across their business. They called this approach One Panera — where everyone, from chefs to data scientists, actively takes part in managing metadata.
In this model, individual chefs and restaurant managers are responsible for their own data domains. They maintain and update the metadata related to their day-to-day operations. Updates may vary, but everyone still follows centralized governance standards maintain compliance. Panera also used a single metadata system that built policy enforcement directly into its workflows ("policy-as-code").
Thanks to these changes, employees can now easily find and understand the data they need for their roles. They can also trace the flow of data from its source right through to how it’s used.
The One Panera initiative has made processes easier and built a more collaborative culture. Business and IT teams now work side by side and data governance has become a natural part of daily operations.
Step-by-step guide to implementing decentralized governance
Now, if you want to implement this decentralized governance or shift from a centralized approach to this one across your organization, here's a step-by-step strategy:
Step 1: Establish a governance framework
Set the ground rules before handing off control. Define who’s responsible for what and what policies they will follow. Also, assign decision-making authority to one person in each team — they will be called the data steward. By doing so, you will have a proper structure for the whole process. Without this foundation, things may get messy because people lack control internally without a guide to support them.
Step 2: Assign domain ownership
Then, give teams ownership of their own data. For example, the marketing team will handle campaign data, the finance team will own budgeting data, and so on. However, while each team gains more control, they must still adhere to company-wide standards. This will create a balance between freedom and alignment as teams move fast, but stay connected to the bigger picture.
Step 3: Implement policy automation
Policy automation is the future of successful data governance. Businesses that use technologies like generative AI, blockchain, or cloud computing to decentralize compliance report 50% fewer non-compliance incidents compared to traditional systems.
Manual approvals slow everything down, so code governance rules — this is called policy-as-code. For example, if only certain roles can access sensitive data, that rule is built into the system. It will automatically check access and enforce policies in real time. This keeps processes compliant and secure without all the back-and-forth hassle to manage access.
Step 4: Enable self-service access
Make it easy for users to find and use the data they need through a central catalog where they can find and understand data without needing help from IT. This access procedure still has to follow rules, but these rules will be automatically implemented through a system, so nobody has to wait in long queues. That’s how you can achieve frictionless data access while keeping governance policies intact.
Step 5: Use metadata to maintain governance consistency
Metadata completeness adds contextually rich information to data assets. So, tag and organize data with metadata to keep governance consistent even when different teams manage their own domains. This will also automate rules and track data flow.
Step 6: Monitor, iterate, and optimize
Decentralized governance isn’t a one-time effort. You have to monitor what’s working and what’s not. The best way to continuously improve your governance processes is to gather feedback from teams and monitor policy performance. Then, you can make adjustments as needed. Just like any good system, it improves with regular monitoring and maintenance.
The future of data governance is decentralized
Decentralized governance is the future for companies that handle vast amounts of data across multiple teams. As data environments become increasingly complex, relying on a single, centralized team to manage everything is no longer sufficient.
Metadata, automation, and policy-as-code are the key building blocks that make decentralized governance possible and overcome this issue. They help organizations stay compliant and scale without sacrificing control.
At data.world, we make this happen. Built for modern data teams, our platform supports metadata-driven, automated, and federated governance models to help you manage your data with confidence, no matter how fast your organization grows.
Take the next step toward decentralized data management and ditch the old-school systems – schedule a data.world demo.
