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Latest uploaded documents
| Title | Type, Company presentation, Break-out sessions, Co-Innovation | Upload date | |
|---|---|---|---|
| CC CDQ Research Briefing - AI-ready data | White paper | 10 March 2026 | CC CDQ Research Briefing - AI-ready data.pdf |
| (06) CC CDQ WS 92 Co-Innovation_Agentic AI and GenAI in data management_Konrad Schulte_UNIL_Frederik Moeller_TU Braunschweig | CDQ presentation | 9 March 2026 | (06) CC CDQ WS 92 Co-Innovation Agentic AI and GenAI in data management Konrad Schulte UNIL Frederik Moeller TU Braunschweig.pdf |
| (07) CC CDQ WS 92 Co-Innovation_Unstructured Data and Semantic Layer_Tilman Friedrich_Christine Legner | CDQ presentation | 9 March 2026 | (07) CC CDQ WS 92 Co-Innovation Unstructured Data and Semantic Layer Tilman Friedrich Christine Legner.pdf |
| (08) CC CDQ WS 92 Best practice exchange Data governance Elizabeth Teracino UNIL Richard Lehmann CDQ | CDQ presentation | 9 March 2026 | (08) CC CDQ WS 92 Best practice exchange Data governance Elizabeth Teracino UNIL Richard Lehmann CDQ.pdf |
| (04) CC CDQ WS 92 BoS Data literay Elizabeth Teracino UNIL Richard Lehmann CDQ | CDQ presentation | 9 March 2026 | (04) CC CDQ WS 92 BoS Data literay Elizabeth Teracino UNIL Richard Lehmann CDQ.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 92 Introduction_Christine Legner_Richard Lehmann_Frederik Moeller | 4 March 2026 | (01) CC CDQ WS 92 Introduction Christine Legner Richard Lehmann Frederik Moeller.pdf |
| (02) CC CDQ WS 92 Designing the future data organization_Rainer Hofmann_EnBW | 4 March 2026 | (02) CC CDQ WS 92 Designing the future data organization Rainer Hofmann EnBW.pdf |
| (03) CC CDQ WS 92 the federation strikes back-from data mesh to data domains_Marco Tonnarelli_Willem_van_den_Heuvel_JADS | 5 March 2026 | (03) CC CDQ WS 92 the federation strikes back-from data mesh to data domains Marco Tonnarelli Willem van den Heuvel JADS.pdf |
Webinars & CC-Videos
CC Research Topics
Data products have the potential to increase data access & reuse, improve governance and control on data and ensure rapid delivery of insights across firms. Therefore, this co-innovation aims to lay the foundation by clarifying the concept of data product, along with its different types and example. Furthermore, we provide a reusable template to support data product design and an end-to-end approach to holistically manage data products. Currently, we are expanding our view from managing single products to portfolio of data products and while doing so, exploring the topic of data product valuation and data product sharing across multiple scenarios.
At the heart of digital transformation is the potential of AI to redefine data management. Therefore, this co-innovation aims to identify the specific impact of AI on current data management practices. Furthermore, when training their own customized AI models, companies often face two problems: they do not have the vast amounts of data necessary to create a competitive AI model and the available data is of inferior quality (heavily lowering a model’s performance). To address the latter problem, we will explore state-of-the-art (AI) techniques to achieve high data quality. We will approach the problem of insufficient data by testing new collaborative approaches for AI projects.
Increasing emphasis on sustainability alongside with stricter regulations and shifting consumer preferences put mounting pressure on enterprises and their largely ad-hoc sustainability activities. This co-innovations group’s research activities focus on developing a scalable approach to address the increasing number of sustainability scenarios. For this goal, the group extensively works on identifying and documenting typical sustainability scenarios, understanding the underlying data requirements, formulating common definitions for sustainability-related data objects, and developing the necessary data management capabilities.
Data quality practices have traditionally focused onto master data. However, with the advent of new technologies, devices and online platforms, these practices need to be extended into other types of data such as observational data, media data and analytical data. In this Co-Innovation group, we 'revisit' data quality in this broaden context and extend to CDQ body of knowledge.