Data Mesh Architecture: Why it matters in 2024

Explore the decentralized approach of data mesh architecture - trending in 2024. This blog will help you understand its definition, core concepts, and best business benefits.

If you’re plugged into the data industry, chances are you’ve heard the buzz about “data mesh“. But what exactly is it, and why should you care? The blog will delve into the meaning of data mesh, its architecture and principles, the problems it solves for organizations, and real-world benefits to illustrate its impact. This year, approximately 120 zettabytes of data will be generated.

Organizations are constantly bombarded with information from various sources. This creates a significant challenge: how to effectively manage, integrate, and utilize this data for informed decision-making. Traditional centralized data management approaches often struggle to keep up with the ever-increasing volume and complexity of data.

This is where the data mesh architecture emerges as a powerful and relevant solution in 2024.

What is a Data Mesh?

A data mesh is an architectural framework for managing and scaling data within organizations. It emphasizes decentralized ownership and governance of data and allows individual teams/departments to manage their own data domains.

Here’s an example to help you understand better:

Let’s say you’re a retailer with stores in different cities. Each store collects data on sales, inventory, and customer preferences. With a data mesh architecture, you can bring all this data together in one place and analyze trends across your entire business. But here’s the kicker – each store still maintains control over its own data. So, while the central hub makes it easier to access and analyze the information, individual stores can still set their own rules about who can access their data and how it can be used.

“Data Mesh is a cultural and organizational shift for data management focusing on federation technology that emphasizes the authority of localized data management.”

The concept of a data mesh was first introduced in 2019 by Zhamak Dehghani, a principal consultant at ThoughtWorks. Dehghani proposed a paradigm shift in how organizations manage their data, advocating for a decentralized approach that aligns with modern software development practices such as microservices and domain-driven design.

Since its inception, the idea of a data mesh has gained traction within the data engineering and analytics communities. Did you know that in 2023, the worldwide data mesh market was worth $1.2 billion? It is predicted to grow by 16.4% annually from 2023 to 2028. In fact, by 2028, it’s estimated to reach a revenue of $2.5 billion.

Data Mesh Architecture

In contrast to conventional centralized data setups, where all data operations are managed in a single location, a data mesh architecture promotes decentralized data management. It facilitates the distribution of data responsibilities across various domains or business units and treats data as a valuable product. Each domain manages its own data pipelines.

Components of Data Mesh Architecture:

1. Domain-Oriented Ownership

Domain-oriented ownership is the foundational principle of data mesh architecture. It involves organizing data into domain-specific areas, with each domain managed by the team responsible for that business function. 

This promotes accountability and ownership, as teams have direct control over their data and can make decisions based on their specific domain expertise. By aligning data ownership with business functions, organizations can ensure that data is managed in a way that best serves the needs of the business.

2. Data as a Product

In data mesh architecture, data is treated as a valuable product that is created, managed, and consumed by various teams across the organization. These data products encapsulate business logic, metadata, and access controls and make it easy for teams to discover, access, and utilize data assets.

For a successful data mesh implementation, each domain team must adopt a product-centric mindset towards their datasets. They should view their data assets as products, themselves as data product owners, and consider other teams within the organization as their data consumers.

Let’s say there’s a financial services company that offers personalized investment recommendations to its clients. They can create data products such as customer profiles, investment portfolios, and risk assessments. These data products can be distributed and owned by various teams within the organization, such as financial advisors, risk analysts, and compliance officers.

3. Self-Serve Data Platform

The self-serve functionality of data mesh architecture empowers users within an organization to independently access and analyze data without relying on specialized technical skills or assistance from IT teams.

This functionality is enabled through self-serve data infrastructure, which includes a suite of tools and capabilities designed to streamline the data access and analysis process. There are tools for data discovery, exploration, transformation, and visualization, as well as automated data pipelines for data ingestion and processing.

4. Federated Governance

Data mesh architecture promotes federated governance, where each domain has autonomy over its data governance policies while still adhering to overarching organizational standards. This decentralization of governance enables agility and flexibility and allows teams to adapt to changing business requirements while ensuring compliance and security.

Example: Imagine a healthcare organization that collects sensitive patient data across multiple departments, including medical records, billing information, and research data. By implementing federated governance, each department can define access controls, data retention policies, and privacy measures tailored to their specific requirements. This ensures compliance with regulatory requirements such as HIPAA while still allowing for collaboration and data sharing across the organization.

Benefits of Data Mesh Architecture

Imagine data as scattered puzzle pieces. Each piece represents a specific area of your business such as sales, marketing, and more. Traditionally, these pieces are locked away in a central box and make it difficult for anyone outside a small team to access and use them.

Here’s how data mesh changes the game:

Facilitates data democratization and everyone gets involved

73% of companies’ data goes untapped, according to studies. This disconnect between data we have and data we use is often due to bottlenecks in accessibility. Data resides in centralized repositories like traditional data warehouses, controlled by specialists.

But with data mesh, data becomes accessible to various teams, not just specialists. This means anyone who needs data can find and use it easily. This leads to faster decision-making and fewer bottlenecks.

Supports a cost-effective approach

Instead of one giant storage box, data is stored in smaller, cloud-based containers. This is like renting storage units instead of owning a massive warehouse. You only pay for what you use, making it more affordable and scalable.

Reduces clutter, enhances efficiency

A central data storage often becomes cluttered and slow over time. Distributing data by domain keeps things organized and efficient, similar to how keeping your space clean makes it easier to find things.

Enables easy collaboration

Data mesh ensures standardized formats across different domains, making it easier for teams to connect and share data across the organization. This fosters collaboration and helps create a clearer view of all relevant information.

Wrapping Up

Ready to implement the data mesh architecture for your organization?

We can help. LumenData is an Informatica Enterprise Platinum Partner and recipient of Informatica’s Global Delivery Channel Partner of the Year award. Our expertise in Informatica’s Intelligent Data Management Cloud (IDMC), combined with our QuickStart programs and accelerators, can help you achieve a go-live in weeks.

Initiate a conversation today.


Shalu Santvana

Shalu Santvana

Content Crafter

Mohd Imran

Mohd Imran

Senior Consultant