In the evolving landscape of data management, age-old approaches are gradually being outpaced to match the demands of modern organizations. Enter as a savior: Data Mesh, a revolutionary concept that modern organizations harness to reshape their business models and implement “data-driven decisions.” Therefore, understanding and implementing Data Mesh principles is essential for IT professionals steering this transformative journey.
At its core, data mesh is not just a technology but a strategic framework that addresses the complexities of managing data at scale, as it proposes a decentralized approach where ownership and responsibility for data are distributed across numerous domains.
This shift enables each domain or department to manage data pipelines, maintain and develop new data models, and perform analytics across all interconnected integrations to facilitate infrastructure and tools that empower domain teams to manage their data assets independently.
At the core of the data mesh architecture lies a robust domain team that is the powerhouse behind the creation, delivery, and management of data products. This team comprises professionals with domain-specific knowledge who will epitomize the decentralized nature of data mesh to foster greater ownership, accountability, and agility within the organization.
This AITech Park article will explore how to build a data mesh team by outlining roles and responsibilities to drive success in an organization.
Data Product Owner (DPO)
The DPO, or Data Product Manager, is an emerging role in the field of data science that manages the roadmap, attributes, and importance of the data products within their domain. The DPO understands the use cases in their domain to serve as per UX and gets acquainted with the unbounded nature of data use cases to create combinations with other data in numerous forms, some of which are unforeseen.
Data Governance Board
After infrastructure, the data governance board is a critical part of the data mesh as they oversee the enforcement of data governance policies and standards across data domains. The board represents data product managers, platform engineers, security, legal, and compliance experts, along with other relevant stakeholders, who will tackle data governance-related problems and make decisions across the various domains within the business.
Building and maintaining a data mesh team needs careful planning, strategies, and commitments to develop talents across all boards. Therefore, organizations must adopt a hybrid organizational structure so that they can establish roles and responsibilities that help drive innovation, agility, and value creation in the digital age.
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