Data Mesh 101: The impact of federated computational governance

Thought leadership

In our final blog on the four principles of data mesh, we’ll focus on federated computational governance (or FCG).

Previously, we’ve published blogs on an overview of data mesh, domain-driven ownership, data-as-a-product and self-service data-infrastructure.

Data mesh is not only a hot topic; it is a foundational strategy for many organizations across numerous industries. Its use in fields as disparate as automotive and cancer research demonstrates its widespread impact.

“Data mesh is a decentralized sociotechnical approach in managing and accessing analytical data at scale.’

– Zhamak Dehghani

The idea behind data mesh is to enable autonomous teams within an organization to take ownership of their own data domains, and to develop and operate data products that meet the specific needs of their respective domains. This approach allows for greater agility and innovation in data management, as well as the more efficient use of computational resources.

Defining FCG

To help you and your organization get the most value from your data, data mesh is centered on four guiding principles:

  • Domain-driven ownership
  • Data as a product
  • Self-service data infrastructure
  • Federated computational governance

In a data mesh approach, data is treated as a product, and each data domain operates as a self-contained business unit that is responsible for its own data products. This means that each domain has the autonomy to choose its own technology stack, development process and governance model.

The concept of federated data governance enables each domain to maintain control over its own data while still adhering to a common set of standards and practices. 

FCG drives more efficient, effective data sharing across the organization, as well as greater transparency and accountability in decision-making.

Why federate? 

Federating the governance model can have several benefits. Providing global oversight with local context improves the overall effectiveness of the governance process. Here are a few reasons why federating the governance model can be advantageous:

  • Increased participation: By allowing multiple autonomous entities to participate in the governance process, a federated model powers greater participation and representation of diverse perspectives, which can lead to better decision-making outcomes.
  • Greater agility: Federated governance helps generate more agile decision-making, as each entity has greater autonomy and can respond more quickly to changing circumstances. The result — more responsive and effective governance.
  • Improved innovation: By decentralizing decision-making and encouraging greater participation, federated governance fosters more innovation and experimentation, as entities are more empowered to try new approaches and ideas.
  • Improved efficiency: Federated governance can also lead to more efficient use of resources, as decision-making is distributed across multiple entities and can be tailored to the specific needs of each entity.

To summarize, federating your governance model can help create a more transparent, accountable and effective decision-making process, while enabling greater participation, agility, innovation and efficiency.

Why compute? 

Computational governance is an emerging approach that involves using software code to automate governance processes and ensure compliance with policies and regulations. 

Here are some of the advantages this approach:

  • Consistency and accuracy: Computational governance ensures consistency and accuracy in governance processes, as the code is designed to enforce policies and regulations in a standardized and automated way.
  • Faster and more efficient governance: By automating many routine governance tasks, such as compliance checks and audits, computational governance can save time and increase efficiency.
  • Greater transparency and accountability: By making governance processes more transparent, as the code is visible and auditable, enterprises can ensure accountability and reduce the risk of errors or misconduct.
  • Increased agility: Computational governance enables more agile governance, as policies and regulations can be updated and enforced more quickly and easily.
  • Scalability: Computational governance improves scalability, enabling governance processes to be automated across large and complex systems.

Overall, computational governance can help to improve the effectiveness, efficiency, and agility of governance processes, while ensuring compliance with policies and regulations and reducing the risk of errors or misconduct.

How did Collibra’s Data Office approach this?

In our blog on domain driven ownership, we describe how we built a discipline within Collibra of high-performing distributed expertise. 

In 2020, the Collibra Data Office was mandated to create ‘data ecosystems,’ so our data analysts across the organization could truly excel in their roles. To achieve our mandate, we leveraged data mesh as our framework for advancing our mission.

The Data Office initiative was a perfect opportunity to apply a federated approach, incorporating a global standard while enabling a range of domains. 

At Collibra, different teams use different BI Tools, but everyone shares the goal of making data -driven insights. So we established tiered Certification levels and criteria with workflows to ensure the criteria are applied consistently across the distributed business domains. 

In fact, the best way to ensure consistency is to apply these requirements programmatically.   This is one of the simplest ways to start with computational governance. 

How does Collibra support FCG?

Collibra supports FCG in a variety of ways, including:

  • Automated workflows: Collibra’s workflows can be automated through the use of APIs and integrations, enabling the automation of routine governance tasks, such as data quality checks and data lineage tracking.


  • Collibra Protect: Data stewards can safeguard sensitive data by easily creating policies that control access. Our no-code solution helps data stewards secure data without the need to rely on technical teams to create policies. With a few simple clicks, data within Snowflake Data Cloud is protected. And you and your team can rest easy knowing your data is safe.


  • Policy management: Collibra enables the creation and enforcement of policies in a code-based format, which can be used to automate compliance checks and other governance processes.


  • Collaboration and communication: Collibra supports collaboration and communication among governance teams, enabling them to work together to manage code assets and governance artifacts.

Overall, Collibra supports computational governance through its automated workflows, and its support for policy management and collaboration. With Collibra, organizations can manage code assets alongside other governance artifacts, automate routine governance tasks, and ensure compliance with policies and regulations.

Learn more by reading from our data mesh series, including blogs on an overview of data mesh, domain-driven ownership, data-as-a-product and self-service data-infrastructure.

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