Category: Enterprise governance and control

This second post of a two-part series that details how Volkswagen Autoeuropa, a Volkswagen Group plant, together with AWS, built a data solution with a robust governance framework using Amazon DataZone to become a data-driven factory. Part 1 of this series focused on the customer challenges, overall solution architecture and solution features, and how they helped Volkswagen Autoeuropa overcome their challenges. This post dives into the technical details, highlighting the robust data governance framework that enables ease of access to quality data using Amazon DataZone.

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance.

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with […]