Siemens Financial Services: The Financial Data Platform of the Future

Siemens Financial Services: The Financial Data Platform of the Future

Data-driven potential as a success factor for the financial sector

Efficient Financial Decisions Thanks to a Smart Data Platform

Siemens Financial Services is taking its data management to the next level - with a highly automated, cloud-based platform on Microsoft Azure and Snowflake. Together with b.telligent, a central single source of truth was created that integrates over 20 source systems, enables real-time analyses and guarantees maximum data security. Automated processes accelerate decisions, optimize workflows and create new potential for data science & machine learning - a real game changer for the financial sector!

Quick Facts About the Project

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Region & Sector: Germany, Financial Services

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Company size: Group

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Project duration: 18-24 Months

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Project type: Project

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Technology: Microsoft, Snowflake, Datavault Builder

About the client

20+

source systems were integrated into the new SFS data platform in just 24 months.

Initial Situation & Challenge

Siemens Financial Services GmbH (SFS) is an international provider of financial solutions in the B2B sector. A homogeneous data landscape is of great importance to the company to ensure quick and reliable decision-making, enhance efficiency, and reduce costs. Additionally, real-time analytics and high data quality are crucial to keeping up with competitors in the fast-paced financial world. However, in early 2022, Siemens Financial Services faced a rather fragmented data landscape in the Data & Analytics domain.

Based on an internally developed data strategy, it was determined that the establishment of a dedicated SFS data platform would be the objective. Siemens Financial Services therefore engaged the experts at b.telligent to ensure long-term stability and competitiveness through a new platform. This platform aimed to reduce complexity and serve as a central, reliable data source (“Single Source of Truth”). The project focused on two primary challenges: first, the immense workload, as it required integrating up to 30 individual source systems—often with several hundred source tables. Second, the regulatory requirements in the financial sector, particularly concerning data security, data protection, access permissions, and governance.

Solution

A "classic" solution was quickly ruled out by the project team due to the enormous workload. Instead, an early decision was made in collaboration with the client to adopt a metadata-driven approach, which also influenced the selection of components.

The new (primary) cloud platform chosen was Microsoft Azure, which could be ideally extended with b.telligent’s metaFactory. This not only allows for the automated ingestion of data sources into the Azure Data Lake (ADLS Gen2) but also analyzes these sources and adds extracted information to the metadata. The parameterized Synapse Pipelines enable processing up to the Historized Layer (Delta Parquet), where the source data is versioned and made available for further use.

Automated Processing and Streaming Architecture

Next, the b.telligent team focused on automated processing in the Snowflake Data Warehouse using Data Vault methodology for the Core Layer. To ensure seamless integration, they extended the b.telligent metaFactory with a generator for Snowflake External Tables. With this consistent ELT approach, automation with Datavault Builder was conducted exclusively within Snowflake. While metadata usage allowed for the automation of many processing steps, some development effort was still required.

Certain reporting requirements necessitated the establishment of a streaming architecture to provide users with timely and up-to-date data in Snowflake. Data originating from a PostgreSQL database was prepared for streaming using Debezium and written into a local Kafka cluster. Mirror Maker was then used to replicate the data from one Kafka cluster to another, enabling replication to the cloud via an Azure Event Hub, which subsequently stored the data in the Azure Data Lake via Event Capture. The interplay between EventGrid and Storage Queue allowed for the efficient transfer of data to Snowflake for further processing.

It quickly became evident that this area also needed to be automated as efficiently as possible. The approach included Continuous Integration/Continuous Deployment (CI/CD) via Azure DevOps, a carefully planned release management process, and the provision of independent development environments (sandboxes) for each use case. At peak times, up to 90 developers and 200 business testers worldwide were working on the project.

Secure Authorization Management for Regulatory Requirements

To address regulatory requirements from the outset, a range of functional roles for users (developers and consumers) were defined. These roles could be automatically mapped to technical roles and assigned to corresponding user accounts in applications such as Snowflake, Azure, and Datavault Builder via the central user provisioning system within Siemens. Additionally, in Snowflake, Row- and Column-Level Policies were automatically implemented to enforce the "Need-to-Know" principle, which is crucial in the financial industry. Individual permissions could then be requested via self-service and workflow within the user provisioning system.

infographic data achitecture btelligent siemens financial services

b.metaFactory

The b.telligent metaFactory is a powerful framework developed by b.telligent for data integration with Azure Data Factory. With over 100 connectors, including those for Oracle and SAP, it offers maximum flexibility in connecting various source systems. Parameterized pipelines and a central control table allow for the easy and quick integration of new data sources. Data is stored in a high-performance Delta format in the Azure Data Lake. Thanks to comprehensive logging and monitoring functions, the metaFactory ensures the highest levels of maintainability and security—all within the Azure Subscription. Fast, secure, and future-proof: your data, optimally integrated.

Voices From the Project

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Thanks to the outstanding expertise of b.telligent and the consistently excellent collaboration, Siemens Financial Services was able to implement a cloud-based data platform within 24 months, which forms a cornerstone of our data strategy. The new platform not only provides security and efficiency but also establishes a solid foundation for future innovations in Data & AI, serving as a central anchor for the ongoing consolidation of legacy reporting systems.

Michael Bruckner

Head of Data Management & IT Program Mgr. SFS Data Platform at Siemens Financial Services

With our extensive expertise, the b.telligent team successfully tackled the challenges posed by diverse source systems and stringent regulatory requirements. Our innovative, metadata-driven approach helped create a future-proof platform that not only reduces complexity but also sustainably optimizes processes. This enables completely new possibilities for self-service analytics and an AI-ready platform that efficiently supports users—clear proof of our leading competence.

Matthias Nohl

Account Manager & Management Consultant at b.telligent

b.telligent Services at a Glance

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Metadata-Driven Approach

Utilization of b.telligent metaFactory for automated data ingestion and analysis in Azure Data Lake

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Seamless Integration

Implementation of Snowflake as a Data Warehouse for efficient data processing and regulatory compliance

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Automation and Efficiency

Use of Datavault Builder for DWH automation, combined with CI/CD pipelines and flexible development environments

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Self-Service Permissions

Integration of all components into a user-friendly workflow for access requests via a user provisioning system

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Real-Time Architecture

Streaming architecture with Debezium, Kafka, Azure Event Hub, and Snowflake for real-time data availability and efficient reporting

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Regulatory Compliance

Definition of functional and technical roles, along with automated access policies in Snowflake

Siemens Financial Services: The Financial Data Platform of the Future

Results & Successes

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Automated data integration: With the b.telligent metaFactory, over 20 source systems were efficiently integrated into the new Financial Data Platform.

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High scalability & performance: The combination of Microsoft Azure and Snowflake enables real-time analyses and parallel developments thanks to sandbox architecture.

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Regulatory security: Automated authorization management ensures compliance according to the need-to-know principle.

Thanks to the consistent automation of data integration through metadata and a well-thought-out release management process, the b.telligent team successfully integrated data from more than 20 source systems into the new SFS data platform within just 24 months. A key success factor was the chosen sandbox architecture, which minimized dependencies and interactions between development streams.

By investing in a tool for the automated migration of SAP HANA Calculation Views to standard SQL, existing reports in the presentation layer could continue to be used. Additionally, easier data access now provides the client with new opportunities in the fields of Data Science and Machine Learning.

While it was clear that replacing SAP HANA with the new platform would not immediately result in cost savings, the long-term benefits and future-proofing of the solution more than compensate for this. Moreover, the integration of all components into the central user provisioning system significantly accelerated the onboarding of new users.

The Tech Behind the Success

Microsoft

Innovation and integration, as well as interoperability, are key factors of the Microsoft product development. Learn more about our collaboration!

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Snowflake

b.telligent builds high-performance and scalable analytics solutions in the cloud with Snowflake. Learn more about our partnership now!

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Datavault Builder

Datavault Builder's efficient visual data integration solution focuses on standardization and collaboration between business and IT staff. This allows you to increase productivity and benefit from the fastest possible time-to-insights, with full auditability and governance without additional effort.

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Klaus-Dieter Schulze

Klaus-Dieter Schulze

Managing Director

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