a. Increased Adoption of Domain-Driven Design: DDD has become a critical factor in the implementation of data mesh. Besides, structuring data management around business domains it also drives alignment of data initiatives with business objectives. This trend is also driving the emergence of domain-oriented data platforms where each domain team is empowered to manage their data as a product, which in turn will foster better alignment between data strategy and business needs.
b. Advancements in Data Product Management: Treating data as a product, has become a trend now. Data product management itself is getting more and more sophisticated, including product roadmaps, user feedback loops, and processes for continuous improvement. It aims to create more robust data products that have thorough documentation, are easily consumable, and are continuously refined based on user needs.
c. Enhanced Self-Serve Data Tools: Self-serve data tools are highly sought after. Organizations are investing in technologies that enable teams to serve and analyze data on their own. Data discovery, cataloging, and visualization tools become increasingly usable and integrated to drive non-technical users to take advantage of data. In this respect, it is a necessary component to make the Data Mesh as decentralized as possible.
d. Focus on Data Quality and Observability: This trend ensures data quality and observability, with data ownership, becoming ever more decentralized. Organizations are adopting this trend of practices for monitoring of data quality and lineage tracking, with automation in validating data. It maintains the trustworthiness and reliability of data products to be maintained across various domains.
e. Cloud-Native Technology Integration: Data Mesh increasingly integrates cloud-native technologies. Cloud platforms provide scalable and flexible infrastructures that go well with the decentralized principles of Data Mesh. Data lakes, serverless computing, and managed data services are some of the services used to natively build and manage data products in the cloud.
