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One Size Does Not Fit All – A Contingency Approach to Data Governance

abstract 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.
   
type journal paper
   
keywords Contingency Theory, Data Governance, Aufbauorganisation, Datenqualitätsmanagement
   
language English
kind of paper journal article
date of appearance 01-11-2009
journal ACM Journal of Data and Information Quality
volume of journal 1
number of issue 1
page(s) Article No. 4
review double-blind review
   
citation Weber, K., Otto, B., & Österle, H. (2009). One Size Does Not Fit All – A Contingency Approach to Data Governance. ACM Journal of Data and Information Quality, 1(1), Article No. 4.