Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on data governance by transferring concepts from IT governance and organizational theory to the previously largely ignored field of data governance. The article presents the first results of a community action research project on data governance comprising six international companies from various industries. It outlines a data governance model that consists of three components (data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The data governance model documents data quality roles and their type of interaction with DQM activities. In addition, the article describes a data governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on data governance. Based on these findings, companies can structure their specific data governance model.
This memorandum was originally published in German in the Zeitschrift für betriebswirtschaftliche Forschung (zfbf), Volume 62, pp. 662-672 and is translated and reprinted here with the kind permission of Fachverlag der Verlagsgruppe Handelsblatt GmbH. The authors would like to acknowledge the generous assistance of EJIS Senior Associate Editor Nicholas Romano who helped with the translation from the German.
The quality of master data has become an issue of increasing prominence in companies. One reason for that is the growing number of regulatory and legal provisions companies need to comply with. Another reason is the growing importance of information systems supporting decision-making, requiring master data that is up-to-date, accurate and complete. While improving and maintaining master data quality is an organizational task that cannot be encountered by simply implementing a suitable software system, system support is mandatory in order to be able to meet challenges efficiently and make for good results. This paper describes the design process toward a functional reference model for master data quality management (MDQM). The model design process spanned several iterations comprising multiple design and evaluation cycles, including the model's application in a participative case study at consumer goods manufacturer Beiersdorf. Practitioners may use the reference model as an instrument for the analysis, design and implementation of a company's MDQM system landscape. Moreover, the reference model facilitates evaluation of software systems and supports company-internal and external communication. From a scientific perspective, the reference model is a design artifact; hence it represents a theory for designing information systems in the area of MDQM.
Business and Information Systems Engineering (BISE) is at a turning point: The ubiquity of information technology (IT) that we experience today in all areas of life leads to a fundamental shift in the BISE landscape and demands the individual user and his or her needs to be put at the center of all investigations. The increasing linkage of human and machine makes it necessary to adjust the perspective on value-chains, processes, methods and structures in BISE. Building on three core themes, the paper at hand discusses this complex socio-technological phenomenon and introduces the new field of 'user, use & utility research' .
Design-oriented IS research aims at delivering results which are of scientific rigor and of practical relevance at the same time. Recently, a number of guidelines have emerged helping researchers to do design-oriented IS research. However, these guidelines lack of supporting the researcher in gaining access to and capturing knowledge from the practitioner community. This paper proposes a method for Consortium Research, a multilateral form of collaborative research in which practitioners grant researchers access to their knowledge, collaborate in the specification of solutions, test artifacts in their business environments, and finance the research activities.
PurposeThe purpose of this paper is to conceptualize data quality (DQ) in the context of business process management and to propose a DQ oriented approach for business process modeling. The approach is based on key concepts and metrics from the data quality management domain and supports decision‐making in process re‐design projects on the basis of process models.Design/methodology/approachThe paper applies a design oriented research approach, in the course of which a modeling method is developed as a design artifact. To do so, method engineering is used as a design technique. The artifact is theoretically founded and incorporates DQ considerations into process re‐design. Furthermore, the paper uses a case study to evaluate the suggested approach.FindingsThe paper shows that the DQ oriented process modeling approach facilitates and improves managerial decision‐making in the context of process re‐design. Data quality is considered as a success factor for business processes and is conceptualized using a rule‐based approach.Research limitations/implicationsThe paper presents design research and a case study. More research is needed to triangulate the findings and to allow generalizability of the results.Practical implicationsThe paper supports decision‐makers in enterprises in taking a DQ perspective in business process re‐design initiatives.Originality/valueThe paper reports on integrating DQ considerations into business process management in general and into process modeling in particular, in order to provide more comprehensive decision‐making support in process re‐design projects. The paper represents one of the first contributions to literature regarding a contemporary phenomenon of high practical and scientific relevance.
for their valuable comments on the drafts, to A. Glaus and M. Saupe for the orthographie, grammatical and stylistic quality control, and to S. Tummer and E. Österle for the graphie design. Again, I am specially thankful for the work of Irene Cameron who translated the book from German into English.
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