Home
#95 CC CDQ Workshop + CDQ Good Practice Award- Berlin
November 25-26 2026
• • •
Latest uploaded documents
| Title | Type, Company presentation, Break-out sessions, Co-Innovation | Upload date | |
|---|---|---|---|
| (01) CC CDQ WS 93 Introduction_Christine Legner_Richard Lehmann_Frederik Moeller | CDQ presentation | 11 June 2026 | (01) CC CDQ WS 93 Introduction Christine Legner Richard Lehmann Frederik Moeller.pdf |
| (02) CC CDQ WS 93 The AI-native CDQ_Sina Wulfmeyer_Unique | Company presentation | 11 June 2026 | (02) CC CDQ WS 93 The AI-native CDQ Sina Wulfmeyer Unique.pdf |
| (03) CC CDQ WS 93 Beyond the Balance Sheet_Monetizing Master Data at SAP_Angelika Parker_Christoph Rauchmann_SAP | Company presentation | 11 June 2026 | (03) CC CDQ WS 93 Beyond the Balance Sheet Monetizing Master Data at SAP Angelika Parker Christoph Rauchmann SAP.pdf |
| (06) CC CDQ WS 93 Industrial-grade AI and Industrial Data – a Gordian Knot_Boris Scharinger_Siemens | Company presentation | 11 June 2026 | (06) CC CDQ WS 93 Industrial-grade AI and Industrial Data – a Gordian Knot Boris Scharinger Siemens.pdf |
| CC CDQ Research Briefing - AI-ready data | White paper | 10 March 2026 | CC CDQ Research Briefing - AI-ready data.pdf |
Latest workshop
| Title | Upload date | |
|---|---|---|
| (01) CC CDQ WS 93 Introduction_Christine Legner_Richard Lehmann_Frederik Moeller | 11 June 2026 | (01) CC CDQ WS 93 Introduction Christine Legner Richard Lehmann Frederik Moeller.pdf |
| (02) CC CDQ WS 93 The AI-native CDQ_Sina Wulfmeyer_Unique | 11 June 2026 | (02) CC CDQ WS 93 The AI-native CDQ Sina Wulfmeyer Unique.pdf |
| (03) CC CDQ WS 93 Beyond the Balance Sheet_Monetizing Master Data at SAP_Angelika Parker_Christoph Rauchmann_SAP | 11 June 2026 | (03) CC CDQ WS 93 Beyond the Balance Sheet Monetizing Master Data at SAP Angelika Parker Christoph Rauchmann SAP.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.