Data Mesh is an analytical data architecture and operating model where data is treated as a product and owned by teams that most intimately know and consume the data.
Today, data is ubiquitous. Data is the by-product of any and every digital action we take. Everything, every system, every process, every sensor generates data. Technology makes it easier for organizations to collect and store data, for businesses to leverage to make better decisions or create more tailored experiences for their customers.
However, organizations are struggling to enable and empower their employees to make the most informed and timely decisions possible. Centralized data platform architectures fail to deliver insights with the speed and flexibility scaling organizations need. Data Mesh serves a solution to these problem.
Data Mesh applies the principles of modern software engineering and the learnings from building robust, internet-scale solutions to unlock the true potential of enterprise data.
Four principles of Data Mesh
Reducing the hops between analytical data consumers and data sources.
Applying design thinking for data assets. Encapsulating related code, policies and infrastructure in a cohesive product.
Removing friction and technological complexities from the interaction between data producers and consumers.
Automating data governance policies without a centralized authority.
Recent success stories
A new White Paper by Harvard Business Review Analytic Services discusses the benefits of using a decentralized approach to data and the disadvantages of a centralized data management systems. It explores how companies are implementing Data Mesh, what challenges they are facing, and how they are overcoming them.
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