Why a Data Mesh Might Be the Solution

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Many companies have built centralized data lakes and assembled dedicated data teams with the goal of becoming data-driven. While these setups may deliver some early successes, organizations often find that the central data team quickly becomes a bottleneck. The team struggles to keep up with the constant stream of analytical requests from executives and product teams. This delay is problematic—timely, data-informed decisions are critical for staying competitive. Consider questions like: Should we offer free shipping during Black Week? Will customers tolerate longer delivery times if they’re more reliable? How does a change to the product page affect checkout rates or returns?

Although the data team is eager to deliver insights quickly, they’re often bogged down by technical issues—frequent breakages in data pipelines caused by upstream changes in operational databases. With limited time left, they must then identify and interpret domain-specific data, often without the necessary context. Each new question demands a steep learning curve in domain knowledge, making it incredibly challenging to provide accurate, actionable insights at speed.

Conversely, many organizations have embraced domain-driven design, empowering autonomous domain teams—often called stream-aligned or product teams—and adopting a decentralized microservice architecture. These domain teams have deep expertise in their specific areas, fully understanding their business’s information needs. They independently design, develop, and manage their web applications and APIs. However, despite their domain knowledge, these teams still rely heavily on the overburdened central data team to access critical data insights.

As the organization grows, the strain on both the domain teams and the central data team intensifies. A promising solution is to transfer data ownership and responsibility from the central team to the domain teams themselves. This principle lies at the heart of the data mesh approach: decentralizing analytical data management by domain. By implementing a data mesh architecture, domain teams gain the ability to conduct cross-domain data analysis independently, while seamlessly connecting and sharing data much like APIs function within a microservices ecosystem.