Now showing 1 - 10 of 28
  • Publication
    Principles for Knowledge Creation in Collaborative Design Science Research
    (Association for Information Systems, 2012-12-18) ; ;
    Joey, F. George
    Design 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.
  • Publication
    Turning information and data quality into sustainable business value
    (IWI-HSG; BEI; SAP, 2013-03-01) ; ; ;
    Danner, Gerd
    Data 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.
  • Publication
    Business and Data Management Capabilities for the Digital Economy : White Paper
    (Own publication, 2015-05-01) ;
    Otto, Boris
    ;
    Gizanis, Dimitrios
    Buzzwords like big data, the Internet of Things, mobile computing, or Industry 4.0 all build on the conviction that the importance of data and information will keep growing both for businesses and for society as a whole. Data management departments need to revise their existing architectures and processes to get ready for the new require-ments, for example regarding data availability, data integration, and data credibility. The report builds on insights collected from the CC CDQ workshops and bilateral pro-jects taking place in 2014. It aims at providing data managers of medium and large enterprises from all industries with useful background information and practical guid-ance for their journey towards the digital economy. More precisely, the report - contributes to a common understanding of the major technological, economic and social drivers behind the evolution of the "digital economy", - specifies the implications the digital economy has on data management re-quirements, - shows how companies react to these new requirements in five short exemplary cases, - presents a business and data management capability framework for companies operating in the digital economy, and - describes a possible roadmap for data managers to follow on their company's journey towards digitization.
  • Publication
    Linking Service- and Capability-Driven Design - Towards a Framework for Designing Digital Businesses
    (Universitätsverlag Ilmenau, 2016-03-09) ;
    Leveling, Jens
    ;
    Otto, Boris
    ;
    Nissen, Volker
    ;
    Stelzer, Dirk
    ;
    Straßburger, Steffen
    ;
    Fischer, Daniel
    The digitization of the economy and society requires enterprises from all industries to revisit their business models and prepare their organizations for the digital age. The design of "smart" products and services, the involvement of prosumers, and the intensifying interconnection of supply chains are signs of this transformation. Each of these scenarios builds on improved availability and interchangeability of data. In order to successfully transform their business and be able to develop valuable new services, companies require methodological help. To address this need, this paper proposes a service-capability design framework for digital businesses. The framework is developed theoretically based on the literature and earlier research and consists of a meta-model and a high-level reference model. The framework is retroactively applied to a real-world digital use case to demonstrate its validity.
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  • Publication
    Gestaltung der Datenversorgungskette: Referenzprozessmodell und Anwendungsbeispiel
    (Institut für Wirtschaftsinformatik, Universität St. Gallen, 2012-01-01)
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  • Publication
    Controlling Customer Master Data Quality: Findings from a Case Study
    Data 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.
  • Publication
    Corporate Data Quality : Prerequisite for Successful Business Models
    (epubli, 2015)
    Otto, Boris
    ;
    Data 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/