Data Governance as an Enabler for Data Mesh and Data Fabric

Last Published: Aug 16, 2023 |
Robert S. Seiner
Robert S. Seiner

President and Principal, KIK Consulting & Educational Services

In the first blog of this three-part series, I explained how to associate data governance with data mesh and data fabric architectures. In the second blog, I compared data mesh and data fabric based on three factors: accountability of people, methodology of process and technology used. In this blog, I will present data governance as an imperative. It's a requirement to drive successful data mesh and data fabric architectures.

As stated in the earlier blogs, data stewardship is a core tenet of suitable data governance. It also places proper custodianship for a company’s data with people closest to the data. Stewardship sits at the heart of data mesh implementations. One goal of data fabric is to democratize — or improve understanding for all. This enables companies to fully exploit their accountable data resources. Getting the “right” people to do the “right” thing with the “right” data is not always easy. It requires clear identification of stakeholders, empowerment, recognition and rewards.

Data Governance as an Enabler

Data governance is critical in enabling enterprises to successfully adapt to changing business and regulatory environments. Limiting data governance to a narrowly defined objective of compliance is short-sided. Its overall value to the enterprise must incorporate the achievement of business goals. At the same time, it needs to facilitate compliance with regulations, policies, standards and more. Data governance is a key enabler for data mesh and data fabric. It influences successful action with the following characteristics:

Take Charge

According to Gartner, “data fabric requires substantial experience across multiple industries in the continuous use and reuse of data to discover, infer and propose data management infrastructure designs and to validate data objects.” Considering new and emerging architectures requires careful evaluation of the organization’s present data constructs. They must also plan adequately for modernization.

Data fabric recognizes and tracks data use cases to support technology reuse. It also supports the ability to validate and standardize data products. Data mesh holds the appropriate people formally accountable for the definition, production and use of those data products. Data governance is at the heart of enabling these data fabric and data mesh objectives.

A data fabric encourages augmented data management and cross-platform orchestration to minimize human design, deployment and maintenance efforts. The governance model bestows Influence, ownership and empowerment. It also provides the ability to take charge on data mesh and fabric assets while maximizing data engagement and use.

Exercise Influence and Empower the Data Community

In my book, Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success, I define data governance as the “execution and enforcement of influence over the management of data.” A governed data environment must influence the users and community to follow standards. These include quality, rules and protection guidelines. It also needs to empower them to easily engage with data to deliver business value.

Data fabric architecture requires the incorporation of next-generation enterprise data management (EDM) disciplines. Specifically, it requires a pragmatic, incremental approach to data governance. It also must relieve physical limitations and provide uniform access to data. This reduction of complexity requires consistency and standardization. This in turn requires accountability for following data definition, production and usage criterion.

In October of 2021, Gartner stated that as a technology trend (I paraphrase), organizations that are implementing data fabric must replace individual, isolated data management tools and metadata platforms with governed tools and integrated platforms that share data and metadata in a much broader sense. The implementation of these tools requires that standards are defined and followed, stem from influence and result in consistency.

Establish Standards

Sharing data and metadata requires rules and principles for data and metadata definition, production and use. The rules and principles become the standards that enable the organization to provide data consistently. Refer to blog two in the series for details on formal standards for accountability of people, methodology of process and technology used.

Data mesh architecture provides a behavioral framework for accountability. It does so by focusing on business domain owners and their responsibility for specific domains, or subject areas, of data. Data mesh governance includes the development of enterprise standards defined by the business domain owners for the distributed entities of the organization. Data governance is as much about governing people’s behavior as it is about governing data.

Organizations implementing data fabric architecture aim to deliver a standard umbrella of technology that virtually overlays various data repositories. They also must consider the independent or potentially non-standard requirements of the already distributed and independent tools and systems.

Ensure Best Practices

To govern data mesh and data fabric effectively, best practices associated with people, processes and technologies must be established. Consistent onboarding of business domain owners must also be put in place. This will enable them to effectively govern the data associated with their domain across the architectures. These best practices effectively connect business-oriented actions to governance programs.

Institute Collaboration with Guided Participation

The coordination and cooperation of people and other resources is essential to build out successful data mesh and data fabric architectures. The governed order of these resources is imperative to moving the organization forward to leverage their data to its fullest extent.

Federated or regionalized and decentralized (with a higher order of governance) management models can be very effective. They help manage accountability to provide governed data mesh and data fabric environments. Data governance is a directive for data mesh. Data fabric focuses on improving the organization’s ability to gain valuable insights from trusted data. This enables them to make better business decisions from their data.

The modern approach to data governance empowers data users. But it also expects them to contribute to the quantity of knowledge and create active engagement. It is essential to empower users to participate in a community where they feel welcome and rewarded for collaborating, sharing and contributing their knowledge to improve data understanding and standards.


This blog focused on the essence of governing your data mesh and data fabric efforts. In the blog, I outlined several best practices for providing governance and guidance. These days, data governance plays an important role in digital and business transformation. And the leveraging of data as a valued business and technical asset. I hope you now have a better understanding of the association and dependencies between data governance, data mesh and data fabric. The data in the new architectural considerations will not govern itself.

To learn more, watch the on-demand webinar "Does Data Governance Differ in Support of Data Fabric vs. Data Mesh?"

First Published: Jun 06, 2022