In the data mesh implementation, the central IT still exists to build a self-service data platform, but it does not own the data. For instance, a marketing company with the central IT team responsible for delivering an enabling technology, is still responsible for overarching governance and security for connected systems but individual functional teams have responsibility for the data itself
Data mesh offers a modern development approach to data analytics and software teams. It reduces data latency by providing instant access to query data from proximate geographies without access limitations.
The distributed data enables sales and marketing teams to curate a 360-degree perspective of consumer behaviors and profiles from various systems and platforms to create more targeted campaigns, increase lead scoring accuracy, and project customer lifetime values (CLV), churn, and other essential performance metrics.
Data mesh enables development and intelligence teams to create virtual data warehouses and data catalogs from different sources to feed machine learning (ML) and artificial intelligence (AI) models to help them learn, without having to consolidate data in a central location.
Data mesh implementation in the financial sector creates faster time-to-insight at lower operating costs and operational risks. Distributed data analytics compacts fraudulent behavior modeling to detect and prevent fraud in real-time. It allows international financial bodies to analyze data locally – within any particular country or region, to identify fraud threats without replicating data sets and transporting them to their central database.
A decentralized data platform makes it easy to comply with worldwide data governance rules to provide global analytics across multiple regions with end-to-end data sovereignty and data residency compliance.
Up to $500 in usage credits included