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

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 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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