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CC CDQ web session: AI for data management - data quality rule mining
September 18, 2025
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Latest uploaded documents
| Title | Type, Company presentation, Break-out sessions, Co-Innovation | Upload date | |
|---|---|---|---|
| CC CDQ Research Briefing AI-enabled data management | E-book & White paper | 31 July 2025 | CC CDQ Research Briefing AI-enabled data management.pdf |
| The Data Product Canvas - Designing Data Products for Sustained Value From Enterprise Data - ISJ_2025_Redwan Hasan_Bastian Finkel_Christine Legner | Scientific publication | 23 July 2025 | The Data Product Canvas - Designing Data Products for Sustained Value From Enterprise Data - ISJ 2025 Redwan Hasan Bastian Finkel Christine Legner.pdf |
| CC CDQ Data Products Canvas Worksheet | E-book & White paper | 24 June 2025 | CC CDQ Data Product Canvas worksheet.pdf |
| CC CDQ Data Products Canvas | E-book & White paper | 24 June 2025 | CC CDQ Data Product Canvas.pdf |
| (10) CC CDQ WS 89 Practice exchange data products and data mesh_Redwan Hasan_Elizabeth Teracino_UNIL | Co-Innovation | 13 June 2025 | (10) CC CDQ WS 89 Practice exchange data products and data mesh Redwan Hasan Elizabeth Teracino UNIL.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 89 Introduction_Christine Legner_Tobias Pentek_Richard Lehmann | 5 June 2025 | (01) CC CDQ WS 89 Introduction Christine Legner Tobias Pentek Richard Lehmann.pdf |
| (02) CC CDQ WS 89 Data Governance at Nestlé_Sebastien Leroux-Nestle | 5 June 2025 | (02) CC CDQ WS 89 Data Governance@Nestlé Sebastien Leroux Nestle.pdf |
| (03) CC CDQ WS 89 Industrial Data Sharing and Data Spaces in Europe_Frederik Möller_TU Braunschweig | 10 June 2025 | (03) CC CDQ WS 89 Industrial Data Sharing and Data Spaces in Europe Frederik Möller TU Braunschweig.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.