Presentation: Data Mesh in Action: A Journey From Ideation to Implementation

Horse Powertrain's shift from centralized data platforms to domain-driven ownership signals a maturation we're seeing across manufacturing and heavy industry. The Data Mesh model works here because it decouples teams from a single bottleneck—typically a 15-person data platform team drowning in requests—and distributes accountability to domains that understand their own data quality requirements. For engineering teams implementing this, the real operational challenge isn't the philosophy; it's building the guardrails. You need federated governance, contract-based data sharing via APIs or event streams, and observability that scales across dozens of autonomous domains. The connection to RAG and AI workloads is direct: when your data lives closer to the teams generating it, you reduce latency and hallucination risks in production ML pipelines. My recommendation is to start small—pick two or three domains with clear ownership and let them own their data products end-to-end, including SLAs. Measure whether you've actually reduced platform team load before scaling further.