Federated Model for Data Governance

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As Data Governance has evolved over the years, the governance models have also been changing along side. Through the years, these have been the core governance models:

• The Centralized Model
• The Decentralized Model
• The Hybrid Model
• The Federated Model

In this article, we will discuss “The Federated Model” and cover some key highlights.

The federated model distributes a team of Data Governance resources among the business functional teams, and a centralized Data Governance leader is accountable for the overall Data Governance program. This model is recommended to enable lean and nimble data governance without unnecessary hierarchy and tiers.

Figure 1: Federated Model

Here are some key highlights of this model:

1.Centralized standards, policies, and governance of certain data (e.g. reference data) allowing for de-duplication of effort and preventing similar issues across business groups

2.Domain self-sovereignty — allows for different governance requirements by different teams

3.The decentralized functions are closely aligned with the business units, providing responsiveness and agility

4.This model is a critical requirement of a Data Mesh

5.This model leverages existing resources across business groups as well as provides centralized oversight without full time resources

Data Mesh is a separate topic in itself, that we would not cover here. However, we would provide the points below to give some idea especially from a governance standpoint.

i.The mesh follows a distributed system architecture of independent data products with independent lifecycles, built and deployed by independent teams

ii.Enables decentralization and domain self-sovereignty but also interoperability through global standardization

iii.Enables users to get value from aggregation and correlation of independent data products

Data-driven approaches like the data mesh can be a complex and difficult process, as it includes:

a) migrating legacy systems

b) prioritizing data management

c) balancing the mandate of making data accessible to data analysts and data scientists, while ensuring data is used responsibly

d) meeting stringent industry and sovereignty regulations. Decentralizing ownership can create risks if there is a lack of controls, since the compliance and regulatory landscape continues to evolve across geographies (e.g. GDPR, LGPD, CCPA)

Federated Computational Governance is a critical requirement of the data mesh, it helps mitigate these governance challenges

Figure 2: Federated Computational Governance for Data Mesh

Hopefully, this gives a good starting point to look into the Federated Governance Model including the business use cases and applicability from a Data Organization standpoint.