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Data Mesh

Concentrate data management on specific business domains, decentralize ownership, overcome scale challenges, improve data quality and availability, and build a collaborative data-driven culture. 
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How to treat data as a product?

The key to our approach is that data is treated as a Product focusing on consumer needs. This forces them to take responsibility for data from production to consumption by business teams, encourages closer alignment with business goals, and leverages domain expertise. To enable this, we deploy a self-service data infrastructure providing the tools and services necessary to acquire, process, store, and discover data without central bottlenecks. Our federated approach maintains data quality, security, and privacy while allowing data management and use autonomy.

Increase scalability

Take advantage of distributed data ownership and management. With Data Mesh, you can scale more efficiently than in a centralized model, adapting to data growth and complexity without creating bottlenecks.

Achieve Agility and Innovation

Innovate using the iterative work of autonomous domain teams developing their own data products, accelerate the development cycle, and implement faster responses to market changes or business needs.

Improve Data Quality and Availability

Let domain experts manage their own data. This will improve data quality, relevance, and availability, leading to more accurate analysis and more effective decision-making.

Focus on Collaboration and Productivity

Foster a culture of collaboration between domain teams, reduce silos, and encourage sharing of best practices; this will lead to increased productivity across the organization.

Overview

Data mesh is a new approach to data management, based on decentralizing and distributing data ownership across domain-specific teams, such as marketing, customer service, or sales. By treating data as a product, instead of a by-product, data mesh architecture gives individual teams more ownership over their domain data sets while ensuring seamless sharing and governance. The data mesh approach breaks down data silos and eliminates operational bottlenecks associated with traditional storage systems, such as a data lake with a central data team. As a result, data consumers can more easily access and use data across the organization. Key principles of the data mesh paradigm are domain-driven data ownership, data as a product, self-serve data platform, and federated computational governance.

We observe that many organizations adopting Data Mesh are focused on implementing a robust, scalable, and flexible data infrastructure and tools that support decentralized data management. Meanwhile, we point out that adopting Data Mesh requires significant cultural and organizational changes, including redefining roles and responsibilities and promoting a product-centered approach to data, which means establishing transparent, interoperable management practices and data standards to ensure consistency, quality, and usability of decentralized data products. Only by the Data Owner taking full responsibility for the data being shared as a product "sold" within the organization will the power of data decentralization be realized.

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