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As industrial giants adapt to a data-centric future, Audi is pioneering a transformation that integrates data governance directly into the heart of its production systems. Ahead of Big Data & AI World Frankfurt, taking place on 4–5 June 2025 as part of Tech Show Frankfurt, Anna Vogt, Data Governance Production & Logistics, AUDI AG, shares how her team is embedding data governance and delivery capabilities to unlock AI’s full potential at scale.
In this interview, Anna explores how Audi built a virtual data delivery organisation, what good governance looks like in complex production environments, and why enabling data access and accountability across teams is essential for driving long-term value. All opinions shared are her own.
– – – – – –What motivated you to bring your data governance expertise to Tech Show Frankfurt this year?
I am thrilled to be here at Tech Show Frankfurt to share Audi’s journey in transforming our business organisation towards a data organisation, particularly within the production process data domain. The automotive industry is at a pivotal point where data and AI are becoming integral to our operations.
My motivation stems from the desire to share how data governance can be implanted in practice in such a complex environment. By sharing our experiences and insights, I hope to inspire other organisations to embrace data-driven transformations and leverage the value of data to stay competitive.
What does good governance look like in complex industrial environments like production and logistics?
Good data governance in complex industrial environments like production and logistics involves establishing clear data ownership, ensuring data quality, and providing value-oriented and managed data products.
At Audi, we have created a virtual data delivery organisation that aligns with our data governance framework. This includes defining roles such as Data Domain Managers, Data Owners and Stewards within the business functions to oversee data integrity and compliance. However, effective data governance is based on the continuous improvement of the IT architecture in order to achieve data-centric data provision. And lastly, it also means fostering a culture where data is treated as a strategic and valuable asset.
How do you ensure that governance doesn’t slow down innovation, especially where AI insights could drive efficiency?
Data governance processes and tools actually provide significant advantages for BI and AI use cases by enhancing transparency over data and streamlining the data shopping process. At Audi, we have implemented a data catalogue allowing our teams to identify and access the data they need. Moreover, our governance tools support the seamless release and delivery of data for the use cases. By having well-defined processes in place, we can ensure that data is consistent and compliant with regulations, without creating bottlenecks.
How are you tackling the challenge of data ownership, access, and accountability in cross-functional AI teams?
Tackling the challenge of data ownership and access requires a collaborative approach between the business functions responsible for the data and IT functions, enabling the application architecture and the technical data access. By fostering data ownership and providing the necessary training and resources, we empower our teams to manage data responsibly and effectively.
What practical steps can companies take to prepare for a more AI-augmented future?
To prepare for a more AI-augmented future, a robust data infrastructure and a data-driven culture where data is valued for decision-making are central/main pillars. Developing talent through training programs is essential to building a skilled workforce capable of managing AI technologies.
Implementing strong data governance frameworks ensures data quality, security, and compliance, which are crucial for AI projects. Based on a strong data governance framework and data infrastructure, I recommend prioritising data demands based on value-oriented AI initiatives and critical business processes in order to create and scale impact.
What can attendees expect to learn at your session at Big Data & AI World Frankfurt?
At my session at Big Data & AI World Frankfurt, attendees can expect to learn about Audi’s transformation journey towards a data organisation and gain insights into the Data Domain Production Process. I will discuss how we established our data domains, their focus and operating mode with the main clusters of data governance and data delivery. Additionally, I will share practical examples and best practices that can be applied to various industries, providing a comprehensive understanding of how to navigate the data-driven future.
Anna’s session, “Audi’s Transformation to Data Domains & Insights into the Data Domain Production Process”, takes place on 4 June 2025, 13:50–14:15.
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