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CDQ Academy M3: Data Architecture, Life-Cycle and Applications
April 23-25, 2024
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Latest uploaded documents
| Title | Type, CDQ Award presentation, Company presentation, E-books & whitepaper | Upload date | |
|---|---|---|---|
| DQ Tool Work Report_v1.0 | CDQ presentation | 18 March 2024 | DQ Tool Work Report v1.0.pdf |
| (09) CC CDQ WS 84 Co-Innovation_AI_for_Data_Management_Konrad Schulte_Christine Legner_UNIL | CDQ presentation | 13 March 2024 | (09) CC CDQ WS 84 Co-Innovation AI for Data Management Konrad Schulte Christine Legner UNIL.pdf |
| (05) CC CDQ WS 84 BoS AI_Governance_Konrad Schulte_Christine Legner_Hippolyte Lefebvre_UNIL | Break-out Session | 12 March 2024 | (05) CC CDQ WS 84 BoS AI Governance Konrad Schulte Christine Legner Hippolyte Lefebvre UNIL.pdf |
| CC CDQ_Research Briefing - Data Literacy | E-book & White paper | 12 March 2024 | CC CDQ Research Briefing Data Literacy.pdf |
| (06) CC CDQ WS 84 BoS Data Lifecycle_Markus Eurich_UNIL | Break-out session | 11 March 2024 | (06) CC CDQ WS 84 BoS Data Lifecycle Markus Eurich UNIL.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 84 Introduction_Christine Legner_Unil_Tobias Pentek_CDQ | 6 March 2024 | (01) CC CDQ WS 84 Introduction Christine Legner Unil Tobias Pentek CDQ.pdf |
| (02) CC CDQ WS 84 Data Management at Boehringer Ingelheim_Martin Treder_Frank Sommerer | 6 March 2024 | (02) CC CDQ WS 84 Data Management at Boehringer Ingelheim Martin Treder Frank Sommerer.pdf |
| (03) CC CDQ WS 84 Building data management capabilities for sustainability_Ridwan Bhuiyan_Zalando | 6 March 2024 | (03) CC CDQ WS 84 Building data management capabilities for sustainability Ridwan Bhuiyan Zalando.pdf |
Webinars & CC-Videos
CC Research Topics
Due to the increasing amount of data, organizations are faced with the need to deliver novel analytics to meet data consumers’ business requirements. To do so, complex data pipelines are being built and data is being prepared and analyzed in a siloed manner – leading to poor data quality, high cost and slow analytics delivery. In order to deal with these challenges, data products can be developed to facilitate the reuse of data by bringing together data sources into a singular view, empowering data consumers through data sharing, streamlining governance by clarifying ownership and reducing analytics’ time-to-market. Furthermore, data products can lay foundation to multiple enterprise-level objectives such as data monetization and data democratization as well as play a key role in implementing new concepts such as data mesh.
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.
Data Management for Sustainability
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.