CC Corporate Data Quality (CC CDQ)
Type
applied research project
Start Date
November 1, 2006
End Date
December 31, 2016
Status
ongoing
Keywords
Corporate Data Quality
CDQ
Datenmanagement
Stammdatenmanagement
Datenmodellierung
überbetriebliches Datenmanagement
Datenarchitektur
Prozessarchitektur
Geschäftsarchitektur
Description
Zur Verbesserung der Konzerndatenqualität braucht es konkrete Lösungsansätze. Das Kompetenzzentrum Corporate Data Quality soll die Visibilität des Themas und das "Business Alignment" in Unternehmen stärken. Zu den Arbeitsergebnissen gehören Methoden und Referenzarchitekturen, die zur Umsetzung von Corporate Data Quality beitragen. Im Zentrum steht der Wissenstransfer des Stands der Forschung in die Unternehmen. Gemeinsam mit Praxispartnern werden Best Practices und Benchmarks entwickelt.
Weitere Informationen unter: http://cdq.iwi.unisg.ch/
Weitere Informationen unter: http://cdq.iwi.unisg.ch/
Leader contributor(s)
Partner(s)
ABB Ltd.
Astra Zeneca plc
Bayer HealthCare AG
Beiersdorf AG
DB Netze
Drägerwerk AG & Co. KGaA
Ericsson AB
Festo AG & Co. KG
Merck KGaA
Nestlé S.A.
Novartis Pharma AG
Osram GmbH
Robert Bosch GmbH
SAP AG
Schweizerische Bundesbahnen SBB
Schaeffler AG
Swisscom IT Services AG
ZF Friedrichshafen AG
Funder
Topic(s)
Business Case für Datenqualität
Anforderung durch neue Prozesse
rechtliche Anforderungen (Compliance)
Mess- und Berichtssysteme für Datenqualität
Rollen und Verantwortlichkeiten im Datenmanagement
Referenzprozesse im Datenmanagement
Kopplung von Datenmanagement- und Nutzungsprozessen
Prozess-Monitoring
Datensicherheit
Portal-/Workflow-Unterstützung für Datenmanagementprozesse
Systemarchitekturen
Datenmigration
Metadaten- und Informationsmodellierung
Gestaltungsprinzipien für Datenarchitekturen
Daten-Services
Überbetriebliches Datenmanagement
Standards
Public Processes
Method(s)
bilaterale Projekte
konsortiale Workshops
Fallstudien
Range
Institute/School
Range (De)
Institut/School
Division(s)
Eprints ID
68049
28 results
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Item type:Publication, Corporate Data Quality : Prerequisite for Successful Business ModelsData is the foundation of the digital economy. Industry 4.0 and digital services are producing so far unknown quantities of data and make new business models possible. Under these circumstances, data quality has become the critical factor for success. This book presents a holistic approach for data quality management and presents ten case studies about this issue. It is intended for practitioners dealing with data quality management and data governance as well as for scientists. The book was written at the Competence Center Corporate Data Quality (CC CDQ) in close cooperation between researchers from the University of St. Gallen and Fraunhofer IML as well as many representatives from more than 20 major corporations. The book is available in English and German as Open Access on http://www.cdq-book.org/Type:book - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Toward a Taxonomy of the Data Resource in the Networked Industry(BVL International, 2014-06-04); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Researcher-Practitioner Collaboration and Knowledge Transfer: Results from an Online Survey(University of St. Gallen, Institute of Information Management, 2012-07-01); Researcher-practitioner collaboration has been receiving much attention in the de-bate about relevant Information Systems (IS) research. Successful collaboration be-tween researchers and practitioners requires the transfer of knowledge, both from research to practice and vice-versa. The working report presents the results of an online survey on researcher-practitioner collaboration among design science researchers. It does not aim at inter-preting the results, but rather forms as their documentation. The results can then be taken up in further research activities.Type:working paperIssue:BE HSG / CC CDQ / 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Reference Process Model for Master Data Management(Universität Leipzig, 2013-02-27); ; ; ; Franczyk, BogdanThe management of master data (MDM) plays an important role for companies in responding to a number of business drivers such as regulatory compliance and efficient reporting. With the understanding of MDM's impact on the business drivers companies are today in the process of organizing MDM on corporate level. While managing master data is an organizational task that cannot be encountered by simply implementing a software system, business processes are necessary to meet the challenges efficiently. This paper describes the design process of a reference process model for MDM. The model design process spanned several iterations comprising multiple design and evaluation cycles, including the model's application in three participative case studies. Practitioners may use the reference model as an instrument for the analysis and design of MDM processes. From a scientific perspective, the reference model is a design artifact that represents an abstraction of processes in the field of MDM.Type:conference paperVolume:1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Controlling Customer Master Data Quality: Findings from a Case StudyData quality management plays a critical role in all kinds of organizations. Data is one of the most important criteria for strategic business decisions within organizations and the foundation for the execution of business processes. For the assessment of a company's data quality, to ensure the process execution and to monitor the effectiveness of data quality initiatives, data quality has to be monitored and controlled. This can be achieved by implementing a comprehensive controlling system for data quality. The implementation of such a system has been realized in only a few organizations. This paper presents a single case study describing the implementation of a comprehensive data quality controlling system. The study focuses on controlling activities defined in the fields of business management.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Principles for Knowledge Creation in Collaborative Design Science ResearchDesign Science Research (DSR) advances the scientific knowledge base while at the same time leading to research results of practical utility. Several guidelines for DSR have been proposed to support researchers in their work. Collaborative forms of DSR require that knowledge be created across the boundaries of the research community and the practitioners community. Only little research, though, has been undertaken so far investigating the topic of knowledge creation in collaborative DSR settings. Answers to fundamental questions are still missing: What knowledge creation processes are used? What problems may occur during researcher-practitioner collaboration? This paper addresses the gap in literature by taking a knowledge creation perspective on DSR. Based on a literature review and findings from the field it proposes a set of principles for knowledge creation in collaborative DSR.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Turning information and data quality into sustainable business valueData and information1 of high quality is not just a hygiene factor for business, but has turned into an asset for competitive advantage. In line with this, data must be carefully managed, thoughtfully governed, strategically used, and sensibly controlled. Excellent organizations recognize the importance of timely, accurate, and reliable data and accordingly treat data as an asset the same way they treat all other corporate assets (such as employees, patents, or manufacturing equipment, for example). The opposite, however, is also true; enterprises using only ad hoc data management practices find that important information gets locked in silos, reports are untrustworthy or practically useless, and vital processes depending on data often run incorrectly. Today's companies are establishing enterprise-wide data quality management as a corporate function in order to ensure smooth business operations provisioned withthe right data at the right time at a sufficient quality level. To support enterprises in their efforts, the Framework for Corporate Data Quality Management (CDQM) describes structures and activities that need to be built up and implemented for efficient and effective management of enterprise-wide data. The Framework has been published as a standard for master data and data quality management by the Competence Center Corporate Data Quality (CC CDQ) of the University of St. Gallen and the European Foundation of Quality Management (EFQM, see http://www.efqm.org). The Framework focuses on raising awareness of the topic and on giving guidance for establishing CDQM in organizations. What the Framework does not do, however, is providing guidelines or recommendations as to how corporate data quality management is supposed to be implemented from a technical point of view.This white paper aims at filling this gap, as it describes how the Framework for CDQM can be implemented using solutions and products which are part of SAP Solutions for Information Management. The white paper addresses both experienced practitioners (who need to expand their skills regarding SAP's Information Management domain) and practitioners who are new to managing, governing, and optimizing the use of data that has an impact on enterprise operations. This white paper can be used in several ways:- as a reference regarding practices and methods for establishing corporatewide data quality management,- as a guide to quickly identify specific products of SAP's Information Management portfolio and how these products support the implementation of the Framework for CDQM,- as a reference regarding a common terminology to be used by business and IT professionals.Type:working paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gestaltung der Datenversorgungskette: Referenzprozessmodell und AnwendungsbeispielType:working paperIssue:BE HSG / CC CDQ / 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication,
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