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Data Mesh: A Pit Stop on the Road to a Data-Centric Culture

“What we need is data that’s integrated without data integration. That will help us reach the real promise of a data-centric environment, which is secure, compliant, and speedy data-enabled collaboration.” - Karan Jaswal

Originally published on Dataversity

The noble effort to build a “data-centric” culture is really a journey, not a destination. With that perspective, we can understand that no matter how good a given environment seems to be –especially compared to whatever existed before – there’s always room for enhancement. As more technologies, strategies, and disciplines emerge, the ongoing evolution ensures constant improvement. And by the way, we’re not even close to real data-centricity yet, but at least we’re getting closer. The growing discipline around data mesh architectures represents a perfect milestone in this endless odyssey. There’s progress here, for sure, but it’s just as important to keep looking ahead. 

Data mesh offers a (relatively) fresh approach in that the focus is on the data itself, rather than the data lake or data warehouse resources and pipelines that move and/or store it. This strategy is founded on a federated data model – the data architecture is organized to meet the needs of diverse business domains, and ownership is assigned to domain-specific teams instead of a central authority. With a data mesh model, data is a product that’s more easily accessible to appropriate constituencies, not hoarded by a few select parties. Among other benefits, this eases scaling and analytics within larger organizations that have heterogeneous infrastructures. 

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