A CONTINGENCY APPROACH TO DATA GOVERNANCE (Research Paper) Kristin Wende, Boris Otto University of St. Gallen, Switzerland kristin.wende@unisg.ch , boris.otto@unisg.ch Abstract: Enterprises need data quality management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have assigned accountabilities for DQM mostly to IT departments. They have thereby ignored the organizational issues that are critical to the success of DQM. With data governance, however, companies implement corporate-wide accountabilities for DQM that encompass professionals from business and IT. This paper outlines a data governance model comprised of three components that build a matrix comparable to an RACI chart: data quality roles, decision areas, and responsibilities. The data governance model documents the data quality roles and their type of interaction with DQM activities. In addition, the paper identifies contingency factors that impact the model configuration. Companies can structure their company-specific data governance model based on these findings. Key Words: Data governance, IT governance, data quality management, data governance model, contingency theory INTRODUCTION Companies are forced to continuously adapt their business models. Global presence requires harmonized business processes across different continents, customers ask for individualized products, and service offerings must be industrialized [cp. 1]. These factors certainly impact the business process architecture and the IT strategy of organizations. Ultimately, however, data of high quality are a prerequisite for fulfilling these changing business requirements and for achieving enterprise agility objectives [2]. In addition to such strategic factors, some operational domains directly rely on high-quality corporate data, such as business networking [3-5], customer management, [6-8], decision-making and business intelligence [9, 10], and regulatory compliance [11]. Data quality management (DQM) focuses on the collection, organization, storage, processing, and presentation of high-quality data. In addition, there are organizational issues that must be addressed, such as maintaining sponsorship, managing expectation, avoiding scope creep, and handling political issues [12-15]. Despite the organizational and business relevance of DQM, however, responsibility for improving data quality and managing corporate data is often assigned to IT departments [11]. Also, many companies try to cope with data quality (DQ) issues by simply implementing data management or data warehouse systems. Surveys on data warehousing failures reveal that organizational rather than technical issues are more critical to their success [16]. Integrated DQM is required in order to address both organizational and technical perspectives. Successful data quality programs identify the organizational processes behind data quality [17]. With data governance, companies implement corporate-wide accountabilities for data quality management that encompass professionals from both business and IT. Data governance defines roles and assigns