Why You Should Start Thinking About Data in Terms of Domains, Products, and Mesh: 5 steps to success

Mastering Snowflake
5 min readApr 11, 2024


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In organizations large and small data powers decision-making, drives innovation, and serves as a foundation for competitive advantage. Yet, the traditional centralized models of data management are increasingly proving to be inadequate. This is where the concepts of data domains, data products, and data mesh come into play, offering a compelling approach to data management that aligns with the agility and complexity of modern business operations.

Understanding Data Domains

A data domain is a logical grouping of data that is aligned not with technology but with specific business operations. These domains can be source-aligned, customer-aligned, or even aggregate data, effectively encapsulating all data operations, from generation to API integration. As illustrated by the diagram below, domains are owned by the teams closest to the data, ensuring that those who understand the data best are responsible for its management. This decentralized approach marks a significant shift from traditional models where a central data team oversees all corporate data, often leading to bottlenecks and inefficiencies.

The Case for Data Domains and Mesh

Embracing data domains necessitates a paradigm shift in how companies perceive and handle data. Unlike the monolithic structures of yesteryear, data domains offer a modular, scalable approach, where data is managed in self-contained units by the respective business teams. This fosters agility, simplifies cross-team collaboration, and enhances data accuracy and management. The concept of a data mesh further amplifies these benefits by promoting interoperability and a federated governance model across these domains, facilitating a seamless flow and utilization of data across the organization.

The diagram below illustrates the intricacies of a data mesh, revealing its core structure and fundamental components. It delineates the ecosystem of data domains, each functioning as a standalone entity, yet interlinked through a cohesive framework that embodies the principles of a data mesh. The digram encapsulates how raw data (RD) is transformed into valuable data products (DP), which in turn, are utilized across various use cases (UC). This interconnected system is underpinned by four foundational elements: federated data governance, domain-centric data processing, localized data ownership, and domain-agnostic tooling, all resting on a robust technological infrastructure. The layout offers a strategic blueprint for organizations aiming to leverage data mesh architecture for enhanced efficiency, agility, and data democratization.

Five Steps to Adopting Data Domains and Mesh

Adopting a data domain and mesh approach involves a series of strategic steps:

1. Initiate a Domain-Oriented Mindset

Organizations must transition from a centralized to a domain-oriented mindset, where data is viewed as a product managed by domain-specific teams. This involves reorganizing teams around business functions or data types and fostering a culture that encourages ownership and accountability of data within these domains.

2. Design and Develop Data Domains

Designing a data domain requires identifying data sources, defining data schemas, and establishing data operations that align with the domain’s business objectives. This step is crucial for ensuring that data domains are well-defined, functional, and capable of addressing specific business needs.

3. Implement a Centralized Data Platform

While data domains operate independently, a centralized data platform is essential for standardizing data storage, transformation, and governance practices across the organization. This platform should support self-service and provide the necessary tools and infrastructure for domain teams to efficiently manage their data products.

4. Foster Cross-Domain Collaboration

With data domains defined, the next step is to ensure that these domains can interact seamlessly. This involves establishing clear interfaces, APIs, and data contracts that facilitate data sharing and collaboration while maintaining data integrity and compliance.

5. Iterate and Scale

Data domains are not set in stone; they should evolve in response to changing business needs and technological advancements. Organizations must adopt an iterative approach, continuously refining data domains, enhancing data products, and exploring opportunities for new domains to support innovation and growth.


The shift towards data domains, products, and mesh represents a significant evolution in data management strategies. By decentralizing data ownership and embracing a domain-oriented approach, companies can improve data accuracy, enhance collaboration, and accelerate innovation. This not only aligns with the dynamic nature of modern business landscapes but also positions organizations to leverage data as a strategic asset effectively. The journey to adopting these concepts involves careful planning, cultural shifts, and strategic investments but promises a future where data empowers every facet of business operations.

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About Adam Morton

Adam Morton is an experienced data leader and author in the field of data and analytics with a passion for delivering tangible business value. Over the past two decades Adam has accumulated a wealth of valuable, real-world experiences designing and implementing enterprise-wide data strategies, advanced data and analytics solutions as well as building high-performing data teams across the UK, Europe, and Australia.

Adam’s continued commitment to the data and analytics community has seen him formally recognised as an international leader in his field when he was awarded a Global Talent Visa by the Australian Government in 2019.

Today, Adam is dedicated to helping his clients to overcome challenges with data while extracting the most value from their data and analytics implementations. You can find out more information by visiting his website here.

He has also developed a signature training program that includes an intensive online curriculum, weekly live consulting Q&A calls with Adam, and an exclusive mastermind of supportive data and analytics professionals helping you to become an expert in Snowflake. If you’re interested in finding out more, check out the latest Mastering Snowflake details.



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