How BMW Group breaks down knowledge silos with Amazon Quick Sight

This post was co-written with Ruben Simon, Fabian Söllner, and Miriam Deml of BMW Group.

The Cloud Data Hub (CDH), developed by BMW Group in partnership with Amazon Web Services (AWS), stands as a centralized platform that has revolutionized how the company manages its vast data ecosystem. The CDH currently governs over 19 PB of data across 6,400 data assets and 2,000 data use cases. Before its implementation, BMW Group’s data was trapped in isolated data silos, scattered across departments in disconnected systems that hindered access and collaboration. The CDH broke down these silos, creating a unified data environment where every member of the organization—from data scientists to business users—can tap into data to create insights.

While breaking down data silos has transformed BMW Group into a more data-driven organization, the journey toward true data democratization revealed a new challenge. Despite the CDH making raw data accessible, the valuable data products generated from it—BI dashboards, AI and machine learning (ML) models, and analytical reports—remained trapped in isolated environments. This meant that critical insights often failed to reach decision-makers across departments, and analytical work was sometimes duplicated.

Recognizing the new frontier of knowledge silos, BMW Group has expanded its vision for the CDH beyond data sharing to knowledge sharing. In this blog post, we show you how promoting Amazon Quick Sight dashboards to discoverable, shareable data products within the CDH creates a unified knowledge layer that amplifies the value of data-driven insights. This next phase in BMW Group’s data journey ensures that actionable intelligence flows as freely as the data that powers it, creating additional returns on investment into a central data platform.

How knowledge is created from raw data in the CDH

Within BMW Group’s CDH, raw data undergoes a remarkable transformation—evolving from isolated data points into knowledge assets that drive innovation across the enterprise.

As shown in the preceding figure, it starts with data discovery. Data consumers search the CDH global business data catalog for data assets relevant to their use case. To dive deeper into the data assets, they explore them directly in the CDH Data Portal running Amazon Athena SQL queries on a limited preview of the data. After they are confident that the data asset is needed for their use case, they can request access to the full data asset. Data access is controlled by AWS Lake Formation at the column and row level. Next, to prepare the data for visualization and analysis in Quick Sight, users use the extract, transform, and load (ETL) capabilities available in AWS Glue Studio. At the visualization frontier, Quick Sight serves as the critical bridge between complex analysis and business action. Its interactive dashboards democratize insights across organizational hierarchies, enabling decision-makers to explore data relationships without technical barriers.

This process of data-to-knowledge manifests in several high-impact initiatives that demonstrate how the CDH creates value for BMW Group. In the autonomous driving division, engineers analyze terabytes of real-world driving data streamed from BMW Group’s connected vehicle fleet. These massive datasets—once unmanageable—now reveal subtle patterns in driver behavior and environmental conditions that continuously refine BMW Group’s autonomous systems. Connectivity Health State dashboards—built with Quick Sight—monitor vehicles. They aid development by assessing connectivity in new hardware and software, detecting operational anomalies, and speeding up incident analysis. Meanwhile, on the automotive factory floors, production managers access intuitive Quick Sight dashboards that translate complex manufacturing metrics into visual insights, enabling real-time quality adjustments that have measurably improved production efficiency. The company’s Cloud Efficiency Analytics program represents another success story, where the AWS Cost and Usage data becomes actionable intelligence that is used to optimize cloud resource allocation.

How Quick Sight dashboards become data products in the CDH

Given the pivotal role of Quick Sight in transforming raw data into actionable insights at BMW Group, the CDH team designated Quick Sight dashboards as data products. The goal is to make Quick Sight dashboards discoverable and accessible by everyone in the organization while enforcing the strict governance processes that CDH implements for its other data products. This helps ensure that insights are not only generated within Quick Sight but also securely shared across the organization in compliance with BMW’s data governance framework.

The CDH team started by enhancing the experience for dashboard users. Users can now browse their created dashboards directly within the CDH Data Portal and access them with a single click, as shown in the following figure.

Within this view, authors can enrich dashboards with additional metadata—such as a business summary and detailed documentation—making them discoverable in the centralized dashboard catalog, as shown in the following figure.

With this critical feature, CDH users can search for dashboards created by any other CDH user from a single, centralized location, breaking down knowledge silos. Whether users are seeking insights on a specific topic or exploring new datasets, they can quickly locate relevant dashboards, as shown in the following figure.

Access to these dashboards is managed with a balance of openness and security. Dashboards without confidential information are classified as public—and are immediately accessible by anyone with access to the CDH data portal, eliminating unnecessary barriers to insight sharing. For dashboards containing sensitive data, such as confidential (private) or personally identifiable information (restricted), access is controlled by the dashboard owner. Users can request access to these restricted dashboards, with approval or denials managed by the dashboard owners. This process maintains compliance and security while fostering a collaborative environment.

Finally, by using Quick Sight embedded analytics, the CDH team seamlessly embedded Quick Sight dashboards directly into the Data Portal. Through this seamless integration, users can analyze and interact with data without switching platforms. The streamlined approach reduces complexity and makes dashboards accessible to all users, regardless of their technical expertise, allowing them to engage with insights effectively within a familiar interface.

Conclusion

BMW Group’s Cloud Data Hub journey exemplifies a strategic evolution in enterprise data value creation. What began as an initiative to consolidate fragmented data across the organization is now evolving into a comprehensive knowledge management ecosystem. By implementing the CDH on AWS, BMW Group first conquered the challenge of data accessibility, creating a unified environment in which information flows freely across traditional departmental boundaries. The subsequent integration of Quick Sight marked a pivotal second phase, transforming accessible data into actionable visual intelligence that now drives decisions across all organizational levels. This success is quantified by the remarkable 197.9% growth in Quick Sight adoption among CDH users within just 1 year of integration.

Looking ahead, BMW Group continues to push the boundaries of knowledge democratization by integrating artificial intelligence into the Cloud Data Hub ecosystem. Through strategic initiatives focused on enhanced metadata intelligence, AI-assisted dashboard creation, and natural language interfaces, the company is poised to further eliminate knowledge silos and accelerate data-driven decision-making across the organization.

For organizations inspired by BMW Group’s journey, AWS now offers Amazon SageMaker Unified Studio, which implements many of the same foundational concepts that BMW pioneered with their CDH. SageMaker Unified Studio provides a unified environment for data discovery, preparation, and analytics—with Quick Sight fully integrated to enable dashboard sharing as data products. Companies can use this managed service to achieve similar knowledge democratization outcomes without the extensive custom development that BMW Group undertook as an early innovator in this space.

About the authors

Florian Seidel is a Global Solutions Architect specializing in the automotive sector at AWS. He guides strategic customers in harnessing the full potential of cloud technologies to drive innovation in the automotive industry. With a passion for analytics, machine learning, AI, and resilient distributed systems, Florian helps transform cutting-edge concepts into practical solutions. When not architecting cloud strategies, he enjoys cooking for family and friends and experimenting with electronic music production.

Ruben Simon is a seasoned Product Manager for BMW’s Cloud Data Hub, the company’s largest data platform. He is passionate about driving digital transformation in data, analytics, and AI, and thrives on collaborating with international teams. Outside the office, Ruben cherishes family time and has a keen interest in continual learning.

Fabian Söllner is a Software Engineer within BMW’s Cloud Data Hub team. He is dedicated to empowering his colleagues across the organization by delivering cutting-edge Business Intelligence and Artificial Intelligence solutions. Beyond his professional pursuits, Fabian adeptly balances his career with a passion for various sports, particularly road cycling and triathlon showcasing his drive for self-improvement both personal and professional.

Miriam Deml is a Software Engineer at BMW’s Cloud Data Hub, where she helps build a scalable data platform and enables teams across the company through innovative data solutions. She enjoys tackling complex challenges, particularly those involving AI enablement. Outside of work, Miriam finds balance and energy through baking and bouldering, whether she’s perfecting a batch of macarons or reaching the top of a new climbing route.

Aishwarya Lakshmi Krishnan is a Senior Customer Solutions Manager with AWS Automotive. She is passionate about solving business problems using generative AI and cloud based technologies.