Toward total data quality management (TDQM)
Information technology in action
Process innovation: reengineering work through information technology
Process innovation: reengineering work through information technology
Data quality and systems theory
Communications of the ACM
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
Virtual teams: a review of current literature and directions for future research
ACM SIGMIS Database
Data quality assessment from the user's perspective
Proceedings of the 2004 international workshop on Information quality in information systems
An approach for incorporating quality-based cost---benefit analysis in data warehouse design
Information Systems Frontiers
One Size Does Not Fit All---A Contingency Approach to Data Governance
Journal of Data and Information Quality (JDIQ)
A Procedure to Develop Metrics for Currency and its Application in CRM
Journal of Data and Information Quality (JDIQ)
Overview and Framework for Data and Information Quality Research
Journal of Data and Information Quality (JDIQ)
Towards a maturity model for corporate data quality management
Proceedings of the 2009 ACM symposium on Applied Computing
Methodologies for data quality assessment and improvement
ACM Computing Surveys (CSUR)
Why not one big database? Principles for data ownership
Decision Support Systems
Journey to Data Quality
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Journal of Data and Information Quality (JDIQ)
Business Process Management: Concepts, Languages, Architectures
Business Process Management: Concepts, Languages, Architectures
An overview of business intelligence technology
Communications of the ACM
System development quality control
MIS Quarterly
Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomy
Decision Support Systems
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The collection, representation, and effective use of organizational data are important to a firm because these activities facilitate the increasingly important analysis needed for business operations and business analytics. Poor data quality can be a major cause for damages or losses of organizational processes. The many tasks that individuals perform within an organization are linked and normally require access to shared data. These linkages are often documented as process flow diagrams that connect the data inputs and outputs of individuals. However, in such a connected setting, the differences among individuals in terms of their preferences for data attributes such as timeliness, accuracy, and others, can cause data quality problems. For example, individuals at the head of a process flow could bear all of the costs of capturing high quality data but not receive all of the benefits, even though the rest of the organization benefits from their diligence. Consequently, these individuals, in absence of any managerial intervention, might not invest enough in data quality. This research analyzes this problem and proposes a set of solutions to this, and similar, organizational data quality problems. The solutions focus on principles of employee empowerment, decentralization, and mechanisms to measure and reward individuals for their data quality efforts.