Winner of the CDQ Good Practice Award 2021: Evonik!

With their "Journey to a Touchless First-Time-Right Data Life Cycle Process", Evonik won the Good Practice Award 2021. Read here all the details about the winner, the finalists, and their submissions.
Good Practice Award Winner & Finalists

Data Strategy

A Data Strategy defines how a company will manage and use its data to generate value. We explain how to build a strategy and why it is essential for your business.
Data Strategy

E-Book: The Data Value Formula

Today, access to valuable data is a superpower. But just having a lot of data is not enough. In our recent e-book, our experts explain how to turn data into business value using a simple model – the Data Value Formula.
The Data Value Formula

Recent Publications

Our various publications create added value for you and your company. Scientifically based models and methods for efficient management of your data.
Request publications for free

Managing Enterprise Analytics Platforms

Successful management of enterprise analytics platforms requires scalable data onboarding and analytics delivery processes with little human intervention but strong governance.
Managing Enterprise Analytics Platforms

Using Machine Learning for Improving Data Quality

Increased data volumes put companies under pressure to systematically manage and control their data assets. Find out how Artificial Intelligence (AI) and Machine Learning (ML) techniques can support you in your data management activities.
Machine Learning

Data Excellence Model (DXM)

The digital transformation of businesses has brought about a fundamental change in many industries. The new Data Excellence Model offers support and guidance for practitioners.
Data Excellence Model

Open Data App Store

The Competence Center Corporate Data Quality (CC CDQ), together with the University of Lausanne, launched a project to provide an “Open Data App Store” that supports businesses in finding, integrating and using open data.
Open Data App Store

Data Catalog Reference Model and Market Study published

The Competence Center Corporate Data Quality (CC CDQ), in collaboration with researchers from Fraunhofer ISST and many data management experts, developed a data catalog reference model and a market study.
Data Catalogs

We are hiring: PhD students

We invite applications for our PhD Positions (100%) at the University of Lausanne/HEC in the Competence Center Corporate Data Quality. You will contribute to the design and implementation of a Data App Store or Machine Learning interrogations in business contexts. Employment rate is 100%!
To our PhD job offers

Competence Center Corporate Data Quality (CC CDQ)

Bridging the gap between theory and practice by bringing together data management experts from companies and academia

The Competence Center Corporate Data Quality (CC CDQ) is a data management research consortium and expert community addressing the challenges resulting from digitalization and data-driven strategies. The core objective of the CC CDQ is to transfer innovative concepts and scientific research results in the domain of data management to everyday business practice in order to support companies in managing data as an asset.

Corporate partners are more than 15 renowned companies from different industries that consider data management critical for the success of their business.
Researchers come from leading academic institutions – among them the Faculty of Business and Economics (HEC – University of Lausanne), the Institute of Information Management (IWI – University of St. Gallen), and the Institute of Accounting, Control and Auditing (ACA – University of St. Gallen).

The CC CDQ is headed by Prof. Dr. Christine Legner (HEC – University of Lausanne) and operated by CDQ AG

Based on latest scientific and practical insights, the CC CDQ develops methods, architectures, reference models, and prototypes needed for efficient implementation of data management. As an expert community, the CC CDQ hosts workshops and focus groups in which knowledge and experiences are shared, best practices are presented, and approaches and solutions are discussed.