| Data are important in our daily lives. Individuals, businesses, and organizations usually make decisions based upon data. The quality of the data affects the quality of the decisions. Researchers and organizations are devoting themselves to identifying data quality dimensions, measuring data quality, and controlling and continuously improving data quality. However, among the data quality research network, there is little research regarding managing both data quality and business resources, a phenomenon which gets more critical as the decision makers decide to achieve higher data quality. This research utilizes four data quality dimensions, accuracy, completeness, consistency, and timeliness, to gain a more comprehensive understanding of data quality. This research first presents a mixed binary integer programming (mixed BIP) optimization model for achieving the highest data quality in an information system with limited resources. This model also includes nonlinear costs that represent the fact that costs of improving data quality increase as the number of errors decreases. Furthermore, this research presents a multi-period MIP model considering due-time limitation, system dynamics, and conflict of local optimums. A periodical review procedure of the multi-period model is suggested. In addition, two sub-models are presented that demonstrate the impact on data quality status when no improvements are performed over multiple periods. |
