Data quality: Setting organizational policies

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.