Risk Assessment of Error-Prone Personal Information in Data Quality Tools

In order to assist companies dealing with data preparation problems, different approaches are developed to handle the dirty data. However, these firms are not able to predict the final outcome from the customer data, before running all the business process. This gives rise to an extra cost for the company at the end, if the data is highly corrupted. Therefore, in this paper; we propose a framework to estimate the propagation of the error through a data quality tool. Since data quality tools are a variation of the workflows, we have based our modeling on workflow schema. The reliability and the risk propagation parameters are introduced for the sequence, parallel split and conditional type of control properties that can be seen in a data quality tool. At the end, a business model is introduced and an experiment with the proposed model is given as a proof-of-concept.