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Data Mesh

Enabling data mesh principles 

What is data mesh?

A data mesh model is a domain-oriented decentralization for data access.

Data mesh architecture shifts the responsibility of data access from the data or technology teams, to the business teams. Empowering business teams to perform cross-domain data analysis independently, no longer relying on data and technology teams who are overburdened and unable to keep up with demand to gain expertise in every business domain.

The 4 principles of data mesh

Domain ownership

Mandates domain teams to take responsibility for their data, allowing them to own both analytical and operational data. 
Domain ownership

Data as a product

Treats domain data as public APIs, and domain teams are responsible for providing high-quality data to satisfy the needs of other domains. 
Data as a product

Self-serve data infrastructure

A dedicated team provides domain-agnostic functionality, tools, and systems to enable domain teams to seamlessly consume and create data products. 
Self-serve data infrastructure

Federated data governance

Promotes standardization and interoperability of all data products through the whole data mesh, creating a data ecosystem with adherence to the organizational rules and industry regulations.
Federated data governance

By aggregating data from multiple domains, teams can build comprehensive reporting, and use data products from other domains to create more innovative data products for the business. Traditionally this is limited to only analytical use cases.

Enabling Data Mesh Beyond Analytics

Using data mesh principles, Cinchy Data Collaboration Platform extends data mesh beyond analytics to operational data, liberating data from application silos and transforming it into a network of federated data products.

How enterprises use data collaboration to enable data mesh

Data products

Data is organized into domains as the governance body and can be structured by how the business unit uses and needs the data rather than how an application dictates.
Data products

Data ownership

Data and domains can be owned by different business units so domain experts can own and maintain their own data.
Data ownership

Data-layer access controls

Who has access to what data can be controlled at every layer by the data owners.
Data-layer access controls

Data change workflow

Data owners can enable change approvals and track those changes throughout the process.
Data change workflow

Data is organized by domains and control is directly on the data for data access at your fingertips, with full governance and controls at the data level, not the application level.

Cinchy network map-1
Practical Approaches to Implementing a Data Mesh
Analyst report from Eckerson Group
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Using Data Collaboration to Extend Data Products

Data Products are self-contained units of business intelligence that encapsulate both data and the tools needed to interpret that data. They transform raw data into meaningful insights, empowering organizations to make informed decisions. In a Data Collaboration environment, those data products can be extended to new capabilities where data products can be co-produced, dynamic, or both.

Co-Produced Data Products

Co-Produced Data Products take data sharing and interaction to the next level. They enable multiple users and systems to co-produce shared datasets, breaking down silos and fostering seamless collaboration across departments.

Dynamic Data Products

Dynamic Data Products evolve and adapt based on changes to data and metadata. They ensure responsiveness to evolving business requirements, making them ideal for scenarios where data models need continuous refinement.

Dynamic Co-Produced Data Products

Data products that are both dynamic and co-produced represent a powerful synergy, combining the adaptability of dynamic products with the collective intelligence of collaborative ones.

Incorporating Data Products, Co-Produced Data Products, and Dynamic Data Products into your data strategy empowers your organization to not only harness the value of information but also to foster a culture of collaboration and agility. Elevate your data management approach with these transformative concepts, unlocking new possibilities for innovation and growth.

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