Updated December 27th, 2024

Gartner Data Mesh: Is It the Future of Data Architecture in 2025?

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Gartner’s perspective on data mesh has sparked significant discussion in the data management community.

This innovative approach promotes decentralized data ownership, allowing teams to manage their data as products.

Understanding Gartner’s insights is crucial for organizations considering this paradigm shift.
See How Atlan Aligns with Gartner’s Data Governance Vision

The data mesh paradigm has stirred discourse across the industry. Gartner’s endorsement of the approach is both challenging traditional views and inviting speculation about potential successors.

In this article, we’ll summarize Gartner’s take on data mesh, see why some people disagree with their assessment, and discuss why the future of data architecture remains open-ended.

Table of contents #

  1. What is data mesh?
  2. Gartner’s stance on data mesh
  3. Challenges of data mesh architecture
  4. Is data mesh DOA?
  5. What the creator of data mesh thinks
  6. Conclusion
  7. How Atlan Supports Data Mesh Concepts
  8. FAQs about Gartner Data Mesh
  9. Gartner data mesh: Related reads

What is data mesh? #

Data mesh is a paradigm shift in how we approach data architecture.

Instead of traditional centralized architectures, the data mesh model promotes a distributed system where individual teams manage and build their own data as discrete products. These data products are then federated across different teams with mutually agreed-upon standards.

As Zhamak Dehghani, the creator of the concept of the data mesh:

“Data mesh, at the core, is founded in decentralization and distribution of responsibility to people who are closest to the data in order to support continuous change and scalability.”

Read more → Data mesh 101

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Gartner’s stance on data mesh #

Gartner’s perspective on the data mesh paradigm from their 2022 Hype Cycle for Data Management has stoked vigorous discussion in the industry. The Hype Cycle is a visual representation that outlines the potential risks and rewards of adopting specific technologies or applications.

Gartner situates data mesh in the “innovation trigger” phase in 2022, which is often seen as the conception of a technology’s lifecycle. Interestingly, Gartner also predicts that the data mesh concept will become “obsolete before plateau.”

The Gartner Hype Cycle for Data Management, 2022

The Gartner Hype Cycle for Data Management, 2022 - Source: Gartner.

This forecast does not necessarily imply that the paradigm is currently obsolete. Rather, Gartner thinks it may be supplanted by a different approach before reaching full maturity.

In terms of market penetration and benefits realization, Gartner rates both as “low”. The company projects that as businesses mature in their data governance capabilities, a more centralized approach might once again gain favor.

This shift might seem counter-intuitive, given the current trend towards decentralization. However, it underlines the complex, evolving dynamics of data management.

Challenges of data mesh architecture #

The allure of data mesh comes with considerable challenges that might have influenced Gartner’s predictions.

Among these hurdles, the demand for consistency in data governance stands as a significant consideration. Before an organization can contemplate adopting a data mesh, it must already have a decentralized approach to governance in place.

This necessitates a high level of maturity within the organization, both in terms of culture and processes.

However, many organizations aren’t there yet. Gartner’s 2021 Data and Analytics Governance Survey indicates that only 18% of organizations have reached the maturity level necessary to adopt the data mesh approach successfully.

Many of the advantages of a data mesh approach involve inherently associated challenges:

  • Domain boundary identification: It can be challenging to define clear boundaries for data domains, especially in complex organizations with overlapping business functions.
  • Technical complexity: Implementing a data mesh requires significant changes to the existing data infrastructure and could necessitate complex technologies and techniques, which could be difficult for some organizations to manage.
  • Team skills and training: A successful data mesh implementation requires each team to possess data management skills. Training teams and ensuring they have the required skillset can be a significant task.
  • Interoperability: Ensuring seamless interoperability between different data domains can be challenging and requires robust standards and protocols.
  • Cultural change: A data mesh approach requires a significant cultural shift from a centralized to a decentralized approach, which may be met with resistance within the organization.
  • Data quality management: Ensuring data quality across different data domains managed by different teams can be a significant challenge.
  • Regulatory compliance: Complying with data protection regulations can be complex in a decentralized environment. This is especially challenging when data is distributed across geographical regions with different regulatory landscapes.

Is data mesh DOA? #

As we’ve noted, Gartner’s prediction about data mesh becoming “obsolete before plateau” prompts the question: What will replace data mesh if it truly becomes obsolete?

Ex-Gartner analyst and current Head of Data Strategy at Profisee Malcolm Hawker believes that Gartner is positioning “data fabric” as the alternative to data mesh.

What Malcolm Hawker, ex-Gartner analyst and the current head of data strategy at Profisee, has to say about data mesh.

What Malcolm Hawker, ex-Gartner analyst and the current head of data strategy at Profisee, has to say about data mesh. - Source: LinkedIn.

Data fabric is another emerging paradigm in data architecture that aims to seamlessly integrate data from various sources to support a range of business use cases.

However, this view is not universally shared.

Scott Hirleman disagrees with Gartner’s stance, calling it a “vendor-first, technology-first” approach. He suggests that such a perspective may overlook the practical and business realities that are driving the adoption of decentralized data management practices like data mesh.

Scott Hirleman on data mesh and Gartner’s hype cycle analysis.

Scott Hirleman on data mesh and Gartner’s hype cycle analysis. - Source: LinkedIn.

Additional data mesh alternatives include:

  • Flexible data lakes
  • Traditional data warehouses
  • Convergent data hubs
  • Abstractive data virtualization layers
  • Federated database systems

What the creator of data mesh thinks #

Zhamak Dehghani originally created the concept of a data mesh while working for Thoughtworks. She now leads her own company, Nextdata, which is creating its own set of “data mesh-native” tools to enable the next generation of data sharing.

Dehghani isn’t keen on what she sees as Gartner’s premature declaration of data mesh’s demise.

In a comment on Scott Hirleman’s LinkedIn thread, Dehghani says, “If Gartner’s opinion is the result of quantified analysis, I would love to see the raw data behind it.”

Dehghani goes on to note that she works with companies on a daily basis that are implementing data mesh architectures. “…the gap between a single analyst’s point of view and the realities on the ground seems quite vast.”

Conclusion #

As we contemplate the future of data mesh architecture, it’s crucial to understand that different approaches may suit different organizations, depending on where they are in their data journey. Both data mesh and data fabric present viable paths, each with its unique strengths and considerations.

The choice between these paradigms should align with the organization’s key challenges and objectives. For companies where data accessibility is the primary concern, the data mesh model could be a fitting choice. On the other hand, for organizations that prioritize data quality and trust, the data fabric approach might be more suitable as it emphasizes seamless data integration and interoperability.

Regardless of the approach chosen, one element remains critical: the data catalog. In either a data mesh or data fabric model, a data catalog is vital for data discoverability and data governance.

How Atlan Supports Data Mesh Concepts #

Atlan helps organizations implement data mesh principles by enabling domain teams to create and manage data products that can be easily discovered and consumed by other teams.

Data products in Atlan are scored based on data mesh principles such as discoverability, interoperability, and trust, providing organizations with insights into their data mesh maturity.

Atlan’s automated lineage tracking and metadata management capabilities further support data mesh implementation by providing a comprehensive understanding of data flows and dependencies across domains.

How Autodesk Activates Their Data Mesh with Snowflake and Atlan #


  • Autodesk, a global leader in design and engineering software and services, created a modern data platform to better support their colleagues’ business intelligence needs
  • Contending with a massive increase in data to ingest, and demand from consumers, Autodesk’s team began executing a data mesh strategy, allowing any team at Autodesk to build and own data products
  • Using Atlan, 60 domain teams now have full visibility into the consumption of their data products, and Autodesk’s data consumers have a self-service interface to discover, understand, and trust these data products

Book your personalized demo today to find out how Atlan supports data mesh concepts and how it can benefit your organization.

FAQs about Gartner Data Mesh #

1. What is Gartner’s definition of data mesh? #


Gartner defines data mesh as a decentralized approach to data architecture that promotes domain-oriented data ownership. This model allows teams to manage their data as products, enhancing scalability and flexibility.

2. How does data mesh differ from traditional data architectures? #


Data mesh contrasts with traditional architectures by decentralizing data ownership. Instead of a centralized data lake, data mesh empowers individual teams to manage their data, fostering agility and responsiveness to business needs.

3. What are the key principles of data mesh according to Gartner? #


The key principles of data mesh include domain-oriented decentralized data ownership, treating data as a product, self-serve data infrastructure, and federated computational governance. These principles aim to improve data accessibility and governance.

4. What challenges might arise when adopting a data mesh approach? #


Challenges in adopting data mesh include ensuring consistent data governance, managing technical complexity, and fostering a cultural shift within organizations. Organizations must address these challenges to implement data mesh successfully.

5. How does data mesh enhance data governance and security? #


Data mesh enhances governance through federated computational governance, allowing domain-specific policies while maintaining global standards. This decentralized approach improves accountability and compliance with data regulations.

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