Guidelines for setting organizational policies for data quality

From a process perspective, the tasks that individuals carry out within an organization are linked. These linkages are often documented as process flow diagrams that connect the data inputs and outputs of individuals. In such a connected setting, the differences among individuals in preference for data attributes such as timeliness, accuracy, etc., can cause data quality problems. For example, individuals at the head of a process flow may bear all the cost of capturing high quality data but may not receive all of the benefits although the rest of the organization benefits from their diligence. Consequently, these individuals, in absence of any managerial intervention, may not invest enough in data quality. In this research, solutions to this and similar organization data quality problems are proposed. The solutions focus on principles of reengineering, employee empowerment, decentralization of computing, and mechanisms to measure and reward individuals for their data quality efforts.