Process innovation: reengineering work through information technology
Process innovation: reengineering work through information technology
A product perspective on total data quality management
Communications of the ACM
Quality information and knowledge
Quality information and knowledge
Diversity in information systems action research methods
European Journal of Information Systems
Improving data warehouse and business information quality: methods for reducing costs and increasing profits
Data Quality for the Information Age
Data Quality for the Information Age
Reconceptualizing the Context-Design Issue for the Information Systems Function
Organization Science
How Important is Data Quality for Evaluating the Impact of EDI on Global Supply Chains?
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Managing Information Quality
The concept of contingency beyond "It depends": illustrations from IS research stream
Information and Management
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 08
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
The PMP Exam: How to Pass On Your First Try (Test Prep series)
The PMP Exam: How to Pass On Your First Try (Test Prep series)
Optimized enterprise risk management
IBM Systems Journal
Data quality: Setting organizational policies
Decision Support Systems
Governing Information Security: Governance Domains and Decision Rights Allocation Patterns
Information Resources Management Journal
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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.