Siemens Builds an Enterprise-Wide Data Mesh Platform to Accelerate Innovation

With Snowflake, Siemens created the Siemens Data Cloud, an open data mesh platform ecosystem that enables cloud transformation, centralizes data, improves decision-making and scales the use of AI.



Projects across business divisions running in Siemens Data Cloud on Snowflake


Data warehouses integrated into Snowflake

Two data scientists working on a project
Munich, Germany

Data to connect everything and everyone

Siemens is a global technology enterprise that combines the digital and physical worlds to benefit customers and society. The company focuses on intelligent infrastructure for buildings and decentralized energy systems, process and manufacturing automation and digitalization, and smart mobility solutions for rail and road transport.

Siemens believes that data is one of its most important assets. And the company is on a mission to create a data culture underpinned by greater transparency and cross-domain connections—ultimately, an ecosystem where data supports and connects everything and everyone.

Story Highlights
  • Unlocking the true potential of data:  Snowflake’s deep integrations with a diverse range of third-party tools and services have enabled Siemens to explore new opportunities and become a truly data-driven enterprise.

  • A single, scalable platform:  Without worrying about infrastructure scalability, Siemens can combine multiple data sources, perform data processing tasks and automate hundreds of critical processes in one platform.

  • Efficient data democratization:  Through Snowflake’s collaboration capabilities, Siemens has unlocked endless possibilities for people to get the most from data—regardless of their role or technical skills.

Disparate legacy systems restricting data-driven innovation

For Siemens, being data-driven is less about transforming digitally and more an all-encompassing philosophy. For several years, the company has been rethinking how it uses data, looking to gain key operational insights that power decision-making and automate processes to unlock innovation.

As Christian Meyer, Head of Cloud Operations and Chief Technology Architect at Siemens AG, explains, the scale of the company’s data ambitions unearthed significant challenges in its existing infrastructure: “At the time, we had one of the world’s biggest on-premises SAP HANA data lakes. And while it had served our needs well, it was difficult to scale and struggled to support a mix of structured and unstructured data. Extracting data to the cloud and integrating AI solutions at scale posed a challenge. And a lack of separation between storage and compute was accelerating costs.”  

To properly execute its data-driven strategy, adopt and develop Software-as-a-Service platforms, and overcome its technical challenges, Siemens decided to modernize its approach to data. It upgraded its data platform to one that could offer data lineage capabilities, handle any type and amount of data, and deliver advanced data-sharing capabilities so more business users could put data to work.  

"We used this opportunity to rethink the way we had been managing our data,” Meyer says. “We decided to migrate from on-premises infrastructure to the cloud, taking advantage of the additional benefits that come with that change so we could more effectively serve our partners. Snowflake was able to meet our high cybersecurity requirements and optimize the operational costs.”  

The migration effort presented Siemens with some challenges. First, it had to replicate all data from the source systems in near real time. And it also had to analyze all dependencies between data products.

“By using SNP Glue to replicate more than 50 ERP systems and over 1.5 billion changes per day to Snowflake and developing a data flow in dbt and the Siemens inner source GitLab platform, we managed complexity, successfully migrated to Snowflake and have been reaping the benefits ever since," Meyer says.

The Siemens Data Cloud: A secure and scalable ecosystem for data

After conducting an intensive data warehouse evaluation, Siemens chose Snowflake as its global solution for data-driven innovation. “We looked at every major data cloud vendor, but Snowflake’s unique Data Cloud offering, market position and trajectory clinched the decision,” says Meyer. “In particular, the immense data sharing capabilities, idea of separating compute and storage to reduce cost, deep integrations with our existing strategic cloud partners like AWS, and strong governance and security capabilities really appealed to us.”  

With Snowflake in place, Siemens was able to build the Siemens Data Cloud—an open data mesh platform ecosystem that enables data product curation, downstream applications and sharing in one place through full IT stack integration.  

The company’s Data Cloud now forms the backbone of its data strategy. It has enabled Siemens to migrate several legacy data applications—including its mission-critical ERP data—to the cloud, and replicate ERP data from nearly 50 systems in one place and in near real time.  

Ultimately, Snowflake’s ease of use, support for multiple languages and granular access controls enable Siemens to share comprehensive data and accelerate its teams’ ability to execute data projects.

Simplified data sharing with endless possibilities

To ensure seamless data sharing around its global organization through a single source of truth, Siemens has created a fully automated data-as-a-service framework. By integrating Mendix’s low-code application development platform with Snowflake, it can offer drag-and-drop capabilities that internal teams can use to create and deploy data products.   As a result, a broader, less IT-focused user base now has access to data, and can put it to work on specific manufacturing and process improvement use cases.  

Data sharing also helps ensure colleagues working for different Siemens entities can exchange relevant data and collaborate quickly and effectively. “Working with Snowflake is a chance for us to innovate,” says Meyer. “We’re always focused on end-to-end automation that empowers people to drive new ideas independently. Self-service is essential; it frees people to focus on data.”

Flexible integration possibilities for impactful ML and AI use cases

Key to Siemens’ data-driven ambitions is using ML and AI to automate processes, free valuable resources, unlock new revenue streams and add business value. To make this happen, Siemens uses Snowflake’s deep service integrations with other cloud platforms and technology providers to ensure its data scientists have the best possible tools and capabilities to improve performance and accelerate time to value.  

Another important aspect of Siemens’ Snowflake integration is its well-established partnership with AWS, which enables Siemens to use the best tools for the task at hand. “We have long-standing partnerships with several major cloud vendors, including AWS,” explains Meyer. “Snowflake’s technical integrations reinforce the importance of these partnerships, in terms of both technology and our joint vision, to help us achieve our cloud transformation. We use Snowflake as a data source for Amazon SageMaker Data Wrangler without the need for any coding. Snowflake is highly flexible and enables us to use the best technology for each use case—something we’re always focused on.”  

Through the Siemens Data Cloud, the company’s data scientists now have the freedom to scale their use of AI, ensuring they’re all empowered to experiment with the technology and maximize its benefits.

Two people working at a laptop

“We have longstanding partnerships with several major cloud vendors, including AWS. Snowflake’s technical integrations reinforce the importance of these partnerships, in terms of both technology and our joint vision, to help us achieve our cloud transformation.”

Christian Meyer
Head of Cloud Operations and Chief Technology Architect, Siemens

Reducing supply chain risks with Snowflake in the Siemens Data Cloud

One example of how Siemens uses Snowflake to automate key processes is in its factory supply chains, where production outages from raw material shortages were becoming increasingly common. Operating with manual processes that relied on human experience and gut instinct, the company realized there was a significant opportunity to reduce its reliance on tribal knowledge, and instead use data-driven automation to identify risks and improve stock availability.

“It’s important to have one platform that connects every data source,” says David Reindler, IT Teamlead Business Intelligence and Data Analytics at Siemens AG. “Snowflake gives us scalability and stability, and makes it easy to implement models and solve business questions. Our compute power is also isolated from other use cases, meaning we never struggle with performance.”  

In fact, this was one of Siemens’ first proofs of concept using Snowflake in the logistics domain to connect multiple data sources, applying it across three factory automation production sites in Germany and China. The company aimed to identify materials at risk of undersupply through data-driven algorithms.  

By assigning risk scores to each material in the supply chain process and connecting all its systems and data from multiple sources in Snowflake, Siemens Data Cloud can now identify potential risks and trigger preventative actions autonomously. What’s more, this information can be easily shared with users through Tableau, providing easy-to-digest visual insights that improve decision-making.  

“We set out to prove to the business what’s possible with the Data Cloud,” says Rebecca Funk, IT Business Partner at Siemens AG. “The Siemens Data Cloud gives us the speed and flexibility to build data models that add value to our business departments and help improve our business processes. What fascinates me now is the number of live projects in Snowflake—it’s grown so fast.”

Technical and business value—together as one

With greater flexibility and a cloud-agnostic platform that supports streaming, countless integrations, APIs, Internet of Things (IoT) data, shop floor data and strong data governance, Siemens has given its employees access to a Data Cloud built with the latest technology, making the company more competitive and agile.  

Moreover, because of an elastic compute engine and multi-cluster architecture, the platform’s scale and cost-effectiveness have driven significant efficiency savings and boosted performance across the business.

People working in an office behind a glass door

“Snowflake has given us substantial cost savings and efficiency gains. We don’t have to worry about platform management or the wrong people having access to the wrong data, and the time-to-market benefits have opened unique opportunities.”

Christian Meyer
Head of Cloud Operations and Chief Technology Architect, Siemens

Snowflake also offers significant security benefits, meeting standards that no other cloud data solutions can match. Because of these capabilities, Siemens’ self-service users can spin up the resources required to conduct highly sensitive data projects in minutes rather than weeks.  

“The ability to provide users with a self-service platform that meets our highest security and governance requirements gets people working with data in new ways and allows them to gain insights at scale,” says Meyer.

Automation opportunities beyond the company's borders

Building on the company’s success moving the majority of its legacy applications to the cloud, Siemens now intends to extend this reach beyond its internal processes, aiming to create new revenue streams by offering advanced data automation solutions as a service.  

“The value of Snowflake is in how the platform allows us to handle and share data,” says Meyer. “In addition, Snowflake’s deep integrations with major cloud service providers are a real value add. For an enterprise as large as Siemens, these integrations hugely improve the efficiency and performance of our IT stack and technology platforms and initiatives.”

Start your 30-DayFree Trial

Try Snowflake free for 30 days and experience the Data Cloud that helps eliminate the complexity, cost and constraints inherent with other solutions.