A model-based approach for developing data cleansing solutions

The data extracted from electronic archives is a valuable asset; however, the issue of the (poor) data quality should be addressed before performing data analysis and decision-making activities. Poor data quality is frequently cleansed using business rules derived from domain knowledge. Unfortunately, the process of designing and implementing cleansing activities based on business rules requires a relevant effort. In this article, we illustrate a model-based approach useful to perform inconsistency identification and corrective interventions, thus simplifying the process of developing cleansing activities. The article shows how the cleansing activities required to perform a sensitivity analysis can be easily developed using the proposed model-based approach. The sensitivity analysis provides insights on how the cleansing activities can affect the results of indicators computation. The approach has been successfully used on a database describing the working histories of an Italian area population. A model formalizing how data should evolve over time (i.e., a data consistency model) in such domain was created (by means of formal methods) and used to perform the cleansing and sensitivity analysis activities.