Most enterprise data problems trace back to one design choice: who owns the data. A data mesh answers that by giving each business domain, finance, HR, supply chain, ownership of its own data as a product, accountable for its quality and accessibility, while a shared set of global standards keeps naming conventions, access rules, and formats consistent across teams.
This is a deliberate trade against the centralised alternative. A single data team can become the approval bottleneck that slows every downstream AI project; a mesh distributes that load to the people who actually understand the data. The cost is real: it requires genuine cultural buy-in from every domain team, not just a platform migration, and governance gets harder to enforce across more independent owners.
JPMorgan Chase’s 2022 migration of over 3,000 internal data pipelines to a domain-driven mesh is the reference case: data discovery time dropped from weeks to hours, which is what made securely rolling out AI tools to 200,000 employees feasible in the first place.