What is a Data Mesh? Definition, Examples, and Best Practices

Data Mesh is an approach to data management that shifts from centralized data platforms to domain-oriented, decentralized data management. It breaks down data bottlenecks and silos within an organization, allowing each domain team to take full ownership of their domain data. This results in increased scalability, faster insights, and a more optimized data-driven decision-making process. Key concepts include domain-oriented decentralized data management, self-serve data platforms and APIs, and discoverability and accessibility of datasets. Implementing Data Mesh effectively requires adherence to best practices such as establishing clear interfaces and standards for data sources and pipelines, enforcing data governance and domain ownership, ensuring data quality and metadata, implementing access controls and automated processes, promoting a culture of collaboration and communication, investing in proper training and skill development, ensuring data discoverability, planning for scalability, iterating and evolving. Some real-life examples of companies successfully implementing Data Mesh include ING, Zalando, and Intuit.