Transforming Shared Services with Data Mesh: Embracing Scalability and Agility

Data Mesh is a relatively new concept and architectural approach for managing and organising large-scale data systems within organisations. It was introduced by Zhamak Dehghani, a software architect at ThoughtWorks, and it aims to address the challenges that arise as companies deal with increasingly complex and distributed data landscapes. In a traditional centralized data architecture, a single monolithic data warehouse is used to store and process all the organization’s data. However, as organizations grow and data volumes increase, this approach can lead to issues of scalability, agility, and data ownership. Data Mesh proposes a different approach, inspired by ideas from domain-driven design and micro-services architecture, to better manage and utilise data resources.

Key principles and concepts of Data Mesh include:

• Domain-Oriented Approach: Data Mesh suggests organising data and teams around specific business domains. Each domain has its own dedicated data products and data teams responsible for data quality, governance, and operations.
• Data Product Thinking: Data is treated as a product, much like software services in a micro-services architecture. Each data product has its own lifecycle, versioning, documentation, and service level agreements (SLAs).
• Decentralised Data Ownership: Instead of centralising data ownership and control, Data Mesh decentralises ownership to domain teams. This enables quicker decision-making and more effective data management.
• Data Mesh Platform: The Data Mesh platform consists of tools, practices, and patterns to support the management, discovery, consumption, and sharing of data products. It includes data catalogs, data pipelines, data quality frameworks, and more.
• Federated Data Architecture: Data Mesh promotes a federated architecture where data is distributed across different domains and teams. Instead of moving all data into a single data warehouse, data products are distributed and accessed through well-defined APIs.
• Data Observability: Ensuring that data is observable and understandable becomes important. Monitoring data quality, lineage, usage, and access patterns is crucial for effective data governance.
• Data Mesh Culture: Beyond just technology, Data Mesh emphasises the need for a cultural shift in the organisation to encourage collaboration, cross-functional teams, and a data-driven mindset.
• Data Mesh is intended to address the challenges that organisation’s face when dealing with large, complex, and distributed data landscapes. It aims to make data more accessible, manageable, and aligned with the needs of different business domains, while also encouraging a more agile and collaborative approach to data management. It’s important to note that Data Mesh is still an evolving concept, and its implementation might vary based on an organisation’s specific needs and context.

Now, lets come to the main point that why shared-services must adapt data-mesh:

Adopting Data Mesh for Shared Services can offer several benefits that address the unique challenges and requirements of shared service functions within an organisation. Shared Services typically provide centralized support services to various business units or departments. Here’s why Shared Services should consider adapting Data Mesh:

• Scalability and Agility: Shared Services often deal with a wide range of data needs from multiple business units. Data Mesh’s decentralized approach allows Shared Services to scale and adapt more effectively by distributing data ownership and management. This helps in handling diverse data requirements efficiently and responding quickly to changing demands.
• Domain Expertise: Shared Services often support various domains such as HR, Finance, IT, and more. Data Mesh aligns well with this structure as it encourages domain-oriented data ownership. Each domain can have its own data team within the Shared Services, ensuring that data products are managed by those with expertise in the respective domains.
• Customised Services: Different business units have unique data needs. With Data Mesh, Shared Services can create customized data products tailored to specific business domains. This enhances data relevance, quality, and usability, leading to better decision-making.
• Data Collaboration: Data Mesh promotes collaboration between Shared Services and business units. Shared Services can provide well-defined data products and APIs, allowing business units to consume data without needing to understand the intricacies of its source and processing.
• Data Quality and Governance: Data Mesh emphasises data product thinking, which means each data product has its own data quality and governance standards. This ensures that data from Shared Services is well-managed and adheres to consistent quality standards, even as data needs diversify across the organisation.
• Reduced Bottlenecks: Traditional centralised data architectures can lead to bottlenecks and delays in data provisioning. Data Mesh’s federated approach enables business units to access and manage data independently, reducing dependency on the Shared Services team for every data request.
• Enhanced Observability: Data Mesh encourages monitoring and observability of data products, including tracking data lineage, quality metrics, and usage patterns. Shared Services can ensure transparency and accountability in data operations by providing business units with insights into data health and performance.
• Culture of Collaboration: Data Mesh promotes a cultural shift towards collaboration, empowerment, and shared accountability. This aligns with the collaborative nature of Shared Services, where cross-functional teams work together to provide support to the organisation.
• Flexibility in Technology Stack: With Data Mesh, Shared Services can adopt a variety of technologies and tools that suit the requirements of different domains. This flexibility allows for the use of specialised tools and frameworks while still maintaining a coherent data ecosystem.
• Future-Proofing: As data complexity grows, Shared Services must evolve. Data Mesh provides a scalable and adaptable framework that can accommodate the organisation’s changing data needs and technological advancements.

It’s important to note that while Data Mesh offers advantages, its implementation requires careful planning, cultural adjustments, and technological considerations. Shared Services should assess their existing processes, data landscape, and organizational structure to determine how best to adapt Data Mesh to their unique context.

Contact US