Siemens Builds Enterprise-Wide Data Mesh Platform on Snowflake to Accelerate Cloud Transformation and AI Innovation

Siemens, a global technology enterprise operating across intelligent infrastructure, manufacturing automation, and smart mobility, recognized data as a critical strategic asset but faced significant constraints from its legacy on-premises SAP HANA data lake. The platform struggled to scale, could not efficiently handle unstructured data, and lacked separation between storage and compute, which accelerated costs. To execute its data-driven strategy and overcome these technical limitations, Siemens required a modern cloud-based solution that could support data lineage, handle any data type at scale, and enable advanced data-sharing capabilities across its global organization. The company selected Snowflake as its global data platform, valued for its cybersecurity standards, cost optimization, compute-storage separation, deep integrations with AWS, and governance capabilities.

Siemens built the Siemens Data Cloud, an open data mesh platform ecosystem powered by Snowflake, that now serves as the backbone of its data strategy. The migration effort replicated more than 50 ERP systems processing over 1.5 billion daily changes to Snowflake in near real time using SNP Glue for replication and dbt for data workflow orchestration. The platform now hosts 600+ projects across business divisions and has integrated 4,800 data warehouses into a single unified environment. By embedding Mendix's low-code application development platform with Snowflake, Siemens created a fully automated data-as-a-service framework that enables non-technical users to create and deploy data products through drag-and-drop capabilities. This democratization of data access significantly accelerated teams' ability to execute analytics projects and develop machine learning solutions.

The Siemens Data Cloud delivers measurable business value through both operational efficiency and strategic innovation. In factory supply chain management, Siemens implemented data-driven automation to identify materials at risk of undersupply by assigning risk scores and connecting multiple data sources; the platform now autonomously identifies potential risks and triggers preventative actions, with insights shared via Tableau dashboards. The elastic compute engine and multi-cluster architecture drive substantial cost savings and performance gains, while the platform's security capabilities enable self-service users to spin up resources for sensitive data projects in minutes rather than weeks. By unifying data infrastructure and scaling AI capabilities, Siemens has positioned itself to extend its reach beyond internal operations, creating new revenue stream opportunities through advanced data automation solutions offered as a service to external customers.