A data mesh is a decentralized architectural framework that provides advanced security to an organization’s data assets. It treats data as a product, managed by domain-specific teams, and gives these teams a self-service data infrastructure.
This shift from centralized data lakes and warehouses to distributed, domain-driven data products helps businesses make faster decisions. Let's walk through how a data mesh is implemented, and how it benefits organizations.
What is data mesh?
When decentralized data ownership is created in order to mange data from different sources, it's a new approach to data management known as data mesh.
In traditional architectures, like data lakes or warehouses, data is stored and managed in a single location. That centralization can lead to data bottlenecks and inefficiencies when it comes time to scale.
The data mesh approach was created to prevent those bottlenecks, by distributing data ownership in different business domains like sales, marketing, finance, and beyond. Decentralization uses the domain-driven design (DDD) principle, where each domain within an organization has its responsibilities and boundaries.
Data is managed by the teams that understand it best. That way, organizations can produce data products that are easy to find and don't infringe on the company's collaborative efforts.
Advantages of data mesh
Data mesh is becoming popular due to the problems it solves in traditional architectures. Here are some of the main benefits:
Eliminating data silos
Traditional data architectures create data silos, where data is isolated within different departments or systems. This isolation prevents detailed analysis and collaboration across the organization.
It becomes complex and time-consuming to integrate data from these silos, leading to inconsistencies and errors. Limited visibility within silos restricts the ability to gain a holistic view of the organization’s data architecture.
A data mesh overcomes these challenges by placing the data management and access back in the hands of the domain teams who generate it.
Eradicating slow delivery
Centralized data management processes in traditional architectures delay data processing and delivery. If data ingestion is slow, the entire pipeline is impacted, and delivery is slowed in general.
Latency issues make real-time or quick data processing difficult. High latency means data takes longer to travel from source to destination. Even minor delays can lead to a noticeable lag in system responses.
A data mesh removes the bottlenecks associated with a single team. Teams are free to quickly adapt to changes in the business environment, unencumbered by slowness or dependency that comes with having one central data authority.
Freeing up self-service
In traditional data architectures, users have to rely on central data teams to get access to data and perform analysis. This autonomy limits individual users' ability to explore and use data independently. It also causes frustration and decreases team members' productivity, threatening a healthy collaborative work environment.
Data mesh solves this problem by giving users a self-service data infrastructure to access and analyze data without depending on central teams. This self-service model speeds up the entire data lifecycle from ingestion to analysis.
Fixing data quality issues
Traditional data architectures create inconsistent data definitions across different departments. Each department is free to define and use data elements differently which creates confusion and errors when data is aggregated or compared.
Such centralized data management means no single team feels fully responsible for the data’s quality. Departments feel disconnected from the data they need to use. That’s why there are also problems of misalignment and misuse of data. This lack of trust in data quality creates hesitation in using data for strategic planning and operational improvements.
A data mesh governance system solves this problem by assigning clear responsibility to domain teams for the quality and reliability of their data products. This encourages better data management practices, as domain teams are directly accountable for their data.
Centralized bottlenecks
Previously, data architectures relied heavily on a central data engineering team to manage data preparation or any other data management processes. This tends to overload the central team with requests from every department.
That means a limited capacity to handle numerous data requests, and delays in data preparation and delivery. Departments wanting to analyze data quickly are out of luck.
A decentralized approach means distributed responsibility across domain teams. Wait times are reduced, just like that. Domain teams manage their own little bottlenecks, but don't have to deal with all of the others in the queue, outside of their department. They get faster access to insights, and can make better decisions.
When to consider data mesh?
Companies should consider adopting a data mesh architecture under several specific conditions, namely they're looking at:
Large, distributed data volumes:
Data mesh best suits organizations with extensive and diverse data across various departments and locations. Centralizing such vast amounts of data can create bottlenecks in data processing and access.
That’s why such organizations should choose a data mesh that decentralizes data ownership from creation to maintenance and allows data to be managed and processed closer to its source.
A need for self-service analytics:
Data mesh is an ideal solution if there's significant demand for self-service analytics across various business domains. Technical and non-technical stakeholders can access and analyze data independently, without relying on IT or data engineering teams.
A focus on data agility and rapid experimentation:
Data agility means an organization can quickly adapt to changing data conditions. Along those lines, rapid experimentation means your teams are hard at work, testing new hypotheses and strategies, using data all the while. If that sounds like your organization, a data mesh may be suited for your team.
Core principles of data mesh
Domain-oriented data ownership
Data mesh is all about decentralizing data ownership and management architecture so that business domains own their data assets.There is a clear delineation of responsibilities. Each domain is accountable for the quality and usability of its data products.
Data-as-a-Product
In the data as a product principle, each data asset is managed with the same care and precision as a commercial product. Data products are well-defined, and specific domain teams accountable for their usability.
Each data product comes with explicit service level agreements (SLAs) that outline data accuracy and reliability expectations for data consumers. A data mesh also standardizes interfaces for data consumption through APIs. These APIs provide consistent and easy-to-use access to data across the organization.
Self-serve data infrastructure
Self-serve data infrastructure is at the core of a data mesh. It's often referred to as the "democratization" of data access, which allows non-technical members to use data without needing the help of central data engineering teams. It also allows data teams to quickly build and deploy products tailored to their needs.
Federated computational governance
Data mesh uses federated computational governance, which follows a centralized framework and policies. Federated governance helps individual domains make autonomous data access decisions.
Technical architecture explained
To understand the technical architecture, let's understand the key architectural components and principles that underlie a data mesh implementation:
Data mesh components
A data mesh has 3 major components:
Data products: Each product represents specific business datasets relevant to the domain’s operations. These data products are treated as high-quality reusable assets that are discoverable and understandable.
Self-serve infrastructure: The necessary tools and platforms, including APIs, data pipelines, and other data management tools, for data ingestion and serving.
Data governance registry: A registry that catalogs all data products by defining access rules and usage guidelines to maintain data security and compliance. It works like a central repository where governance policies are enforced so all data products follow organizational standards.
Data mesh implementation
Data mesh implementation, at a very high level, is made up of the following three steps:
Landing zones are set up for raw data ingestion from different sources. These landing zones serve as initial repositories where raw data from different domains and systems is collected and stored.
Data transformation pipelines are created within each domain to cleanse and enrich this raw data. They convert raw data into structured products aligned with each domain’s requirements.
Data APIs are used to publish and consume these data products. These APIs provide standardized interfaces that allow other domains and applications to access and use the data product easily.
How to implement data mesh?
Starting the data mesh journey
A data mesh journey begins with some initial considerations and planning. First, assess your organization's readiness. Check whether your current data practices and infrastructure can support shifting to a decentralized model.
Ask these questions:
Are your teams prepared to take on more ownership of their data?
Do they have the tools and skills needed for this new approach?
How can teams maintain data quality and security within a decentralized approach?
Technical and infrastructure requirements
The technical implementation of a data mesh is somewhat unique to your organization, and is too large a topic to be covered in just one paragraph (or indeed, in one blog post alone). At a very high level, some of the technical tools and infrastructure elements that make up a data mesh include:
Data integration tools
Data warehousing system
Metadata management tools
Data governance and sensitive data protection tools
Data discovery and monitoring tools
Self-service analytics data platform
Platforms like data.world can make this job much easier with their agile data cataloging and integration features. All data products are cataloged to be easily discoverable and understandable. The repository has a user-friendly interface where you can search for and access the data your team needs.
Some other platforms that can help you with mesh implementation include:
Collibra
Informatica
Atlan
Alation
Among these tools, data.world is the ideal platform for implementing a data mesh architecture due to a comprehensive approach to treating data as a product. Within our platform, all organizations can create well-defined data products with clear ownership and functionalities.
Operationalizing data mesh
If we break the whole data mesh implementation process in steps, it looks like the following:
Review existing data infrastructure, governance policies, and data management practices to understand your organization’s readiness.
Conduct workshops and training sessions to educate teams about data mesh principles and benefits.
Analyze the different business domains and their corresponding data products.
Assign data product ownership to specific domain teams, making them responsible for data quality and usability.
Define service level agreements (SLAs) for data products to ensure reliability and performance.
Deploy a modern data catalog, like data.world to facilitate data discovery and management.
Create data pipelines for data ingestion and transformation.
Set up a federated governance framework with centralized policies and decentralized enforcement.
Develop and document APIs for easy access and integration of data products.
Continuously refine data products and infrastructure based on feedback and evolving needs.
Challenges and solutions with data mesh implementation
Data mesh implementation is not as easy as it may sound. You may encounter several challenges. Here are the most common (and some ways you can overcome them):
Cultural resistance: Data mesh requires a cultural shift towards decentralization and domain-oriented design. So, convincing teams to take ownership and cooperate can be difficult.
High costs of infrastructure: Data mesh implementation can be costly, when it comes to maintaining infrastructure and developing data products. In 2023, the global size of the data mesh market was valued at $1.2 billion and is expected to grow by 16.4% by 2028.
Master data management (MDM): Each domain follows different standards, and without proper collaboration, data is not shared in real time. To solve this, create a central MDM team to oversee and support master data integration across domains.
Data virtualization and duplication: Since there are no centralized guidelines, issues like inconsistent formats and overlapping data sources arise, further creating redundancy and integration challenges.
Key roles for data mesh implementation
Without the right people in these roles, data mesh implementation falls apart. You need a mix of aligned data and technology experts to make this work. Some of those roles include:
Data product owner: Manages specific data assets as products to ensure that data is stored in high quality and available to everyone.
Domain experts: Provide subject matter expertise and oversee the data within their respective domains.
Data engineers and architects: Design and maintain the infrastructure to support distributed data architecture.
Governance and compliance officers: Ensures data usage complies with both internal policies and external regulations.
Change managers: Facilitates organizational change to support adopting data mesh principles and practices among data teams.
data.world’s approach to data mesh
data.world can build a strong foundation for your data mesh implementation, because it:
Provides data discovery and cataloging features to help organizations locate and comprehend their data products before mesh implementation.
Treats data as a product to ensure data assets are managed with clear ownership and governed by service level agreements (SLAs).
Uses a knowledge graph to connect and understand relationships between various data products to enhance your data ecosystem's coherence and usability.
Works on an agile data governance philosophy that supports federated governance to give you a scalable framework that ensures compliance and quality across distributed domains.
OneWeb—a communications company used data.world’s platform to provide satellite engineers with global access to distributed data on satellite design and performance. This self-service infrastructure helps users quickly discover and use the needed data while improving satellite design and operational efficiency.
If you'd like to see the same efficiency in your data mesh implementation as we've described above, schedule a demo with data.world today and see how it can transform your data architecture.
