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Latest uploaded documents
| Title | Type, Company presentation, E-books & whitepaper, SAP MDG | Upload date | |
|---|---|---|---|
| (11) CC CDQ WS 87 Co-Innovation results_Data Products_Redwan Hasan_UNIL | Co-Innovation | 2 December 2024 | (11) CC CDQ WS 87 Co-Innovation results Data Products Redwan Hasan UNIL.pdf |
| (09) CC CDQ WS 87 BoS Business case for data management_Tobias Pentek_CDQ_Jingyang Wang_UNIL | Breakout session | 2 December 2024 | (09) CC CDQ WS 87 BoS Business case for data management Tobias Pentek CDQ Jingyang Wang UNIL.pdf |
| (07) CC CDQ WS 87 Co-Innovation results_AI for data management_Konrad Schulte_Christine Legner_UNIL | Co-Innovation | 2 December 2024 | (07) CC CDQ WS 87 Co-Innovation results AI for data management Konrad Schulte Christine Legner UNIL.pdf |
| (03) CC CDQ WS 87 Co-Innovation results_Data_and_Sustainability_Elizabeth Teracino_Jingang Wang_UNIL | Co-Innovation | 2 December 2024 | (03) CC CDQ WS 87 Co-Innovation results Data and Sustainability Elizabeth Teracino Jingang Wang UNIL.pdf |
| (02) CC CDQ WS 87 Data Strategy 2024_Christine Legner Tobias Pentek | CDQ presentation | 29 November 2024 | (02) CC CDQ WS 87 Data Strategy 2024 Christine Legner Tobias Pentek.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 86 Introduction_Christine Legner_UNIL | 26 September 2024 | (01) CC CDQ WS 86 Introduction Christine Legner UNIL.pdf |
| (02) CC CDQ WS 86 Tetra Pak enterprise data management journey_Balaji Rajamani_Tetra Pak | 25 September 2024 | (02) CC CDQ WS 86 Tetra Pak enterprise data management journey Balaji Rajamani Tetra Pak.pdf |
| (03) CC CDQ WS 86 Enabling your data democracy_the role of modern data governance_Hippolyte Lefebvre_UNIL | 26 September 2024 | (03) CC CDQ WS 86 Enabling your data democracy the role of modern data governance Hippolyte Lefebvre UNIL.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.