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Since supply chains have become complex and tracking a product's journey, from raw materials to the end of it's life has become more difficult. Consumers are demanding greater transparency about the materials origins and environmental impact of the products they buy. These new requirements, togeher with European Commission Green Deal strategy, lead to the concept of digital product passport (DPP). DPP could be seen as an instrument to boost circularity, however the DPP architecture and governance model still undefined and unclear. Data Governance in the context of the DPP acts as the backbone for ensuring accurate and reliable data within these passports or data models, leading to flawless traceability. This article approaches the DPPs and it's governance challenges, explaining how they function as digital repositories for a product's life cycle information and the concept of Data Governance. By understanding how these two concepts work together, we will explore a short use case within the footwear industry to show how DPP governance architecture might work in a distributed environment.

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Industry 4.0 promotes a paradigm shift in the orchestration, oversight, and optimization of value chains across product and service life cycles. For instance, leveraging large-scale data from sensors and devices, coupled with Machine Learning techniques can enhance decision-making and facilitate various improvements in industrial settings, including predictive maintenance. However, ensuring data quality remains a significant challenge. Malfunctions in sensors or external factors such as electromagnetic interference have the potential to compromise data accuracy, thereby undermining confidence in related systems. Neglecting data quality not only compromises system outputs but also contributes to the proliferation of bad data, such as data duplication, inconsistencies, or inaccuracies. To consider these problems is crucial to fully explore the potential of data in Industry 4.0. This paper introduces an extensible system designed to ingest, organize, and monitor data generated by various sources, focusing on industrial settings. This system can serve as a foundation for enhancing intelligent processes and optimizing operations in smart manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Nowadays the concept of digitalization and Industry 4.0 is more and more important, and organizations must improve and adapt their processes and systems in order to keep up to date with the latest paradigm. In this context, there are multiple developed Maturity Models (MMs) to help companies on the processes of evaluating their digital maturity and defining a roadmap to achieve their full potential. However, this is a subject in constant evolution and most of the available MMs don't fill all the needs that a company might have in its transformation process. Thus, European Digital Innovation Hubs (EDIH) arose to support companies on the process of responding to digital challenges and becoming more competitive. Supported by the European Commission and the Digital Transformation Accelerator, they use tools to measure the digital maturity progress of their customers. This paper analyzes several MMs publicly available and compares them to the guidelines provided to the EDIH.