Data Mesh
Data Mesh architecture is a decentralized Data Management approach that organizes data across individual business domains.
What is Data Mesh?
Data Mesh is a relatively new concept, first introduced by Zhamak Dehghani in 2019 to describe the principles of a domain-oriented, decentralized architecture for analytical data. It represents an alternative to traditional centralized architectures, such as Data Warehouses or Data Lakes.
The core idea of Data Mesh is to manage data within the business units or teams where it is generated. For example, finance teams manage financial data directly. A key advantage is that the responsible departments have detailed knowledge about their data structures and understand the related processes. Each team follows unified standards for processing and delivering their data, ensuring they can provide relevant information to meet diverse business queries and perspectives.
Four principles of Data Mesh
Data Mesh is built on four fundamental principles:
1. Domain-oriented decentralized data ownership:
Teams own and manage data related to their specific domain.
2. Data as a product:
Data sets are treated as products, ensuring they are easily discoverable, accessible, and usable across the organization.
3. Self-serve data infrastructure:
A platform that enables teams to manage and use their data autonomously.
4. Federated computational governance:
Standards and policies are managed across domains to ensure consistency and compliance.
Benefits of Data Mesh
Data Mesh offers significant advantages that can facilitate the transformation into a data-driven organization:
• Improved agility and responsiveness to changing business needs
• Enhanced data quality and usability due to domain expertise
• Scalability due to decentralized ownership and management
• Faster access and increased efficiency in data handling
Implementing Data Mesh in organizations
Data Mesh presents a promising alternative to traditional centralized approaches like Data Lakes and Data Warehouses. However, implementation involves challenges, such as building technical expertise within domain-specific teams. Overcoming these challenges is crucial to meeting the high specifications required by Data Mesh and effectively extracting valuable insights from data.
The Data Mesh approach emphasizes not just data itself but the valuable information it contains, which is structured and presented as data products.
Data responsibility starts within the domains
We help you design a Data Mesh where domain expertise, governance and technology work together to build scalable, autonomous data structures with central quality assurance.
Book an orientation callThomas Howert
Founder and Business Intelligence expert for over 10 years.
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