Home
Latest uploaded documents
| Title | Type, Company presentation, E-books & whitepaper, SAP MDG, Co-Innovation | Upload date | |
|---|---|---|---|
| Data_strategy_transforming_into_a_data-driven_enterprise_Christine Legner_Tobias Pentek | Scientific paper | 8 May 2025 | Data strategy transforming into a data-driven enterprise Christine Legner Tobias Pentek.pdf |
| 32nd CDQ SAP MDG WS April 2025 - Meeting Minutes | SAP MDG Focus Group | 14 April 2025 | 32nd CDQ SAP MDG WS April 2025 Meeting Minutes.pdf |
| CC CDQ Research Briefing - Data Product Management - Lifecycle Roles Responsibilities | E-book & White paper | 19 March 2025 | CC CDQ Research Briefing - Data Product Management - Lifecycle Roles Responsibilities.pdf |
| (07) CC CDQ WS 88 BoS Data Literacy_Hippolyte Lefebvre_Konrad Schulte_UNIL | Break-out session | 19 March 2025 | (07) CC CDQ WS 88 BoS Data Literacy Hippolyte Lefebvre Konrad Schulte UNIL.pdf |
| (10) CC CDQ WS 88 Practice exchange_Data Governance roll-out_Hippolyte Lefebvre_Elizabeth Teracino_UNIL_Zib Korendo_CDQ | Co-Innovation | 18 March 2025 | (10) CC CDQ WS 88 Practice exchange Data Governance roll-out Hippolyte Lefebvre Elizabeth Teracino UNIL Zib Korendo CDQ.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 88 Introduction_Christine Legner_Tobias Pentek_UNIL_CDQ | 12 March 2025 | (01) CC CDQ WS 88 Introduction Christine Legner Tobias Pentek UNIL CDQ.pdf |
| (02) CC CDQ WS 88 Master Data in a S4 HANA greenfield approach_Jens Peter Henriksen_Mateus Prado_Bayer | 12 March 2025 | (02) CC CDQ WS 88 Master Data in a S4 HANA greenfield approach Jens Peter Henriksen Mateus Prado Bayer.pdf |
| (03) CC CDQ WS 88 Towards enabling analytics & AI Active data management_Markus Rahm_Schaeffler | 12 March 2025 | (03) CC CDQ WS 88 Towards enabling analytics & AI Active data management Markus Rahm Schaeffler.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.