Data Quality Sensitivity Analysis on Aggregate Indicators

Decision making activities stress data and information quality requirements. The quality of data sources is frequently very poor, therefore a cleansing process is required before using such data for decision making processes. When alternative (and more trusted) data sources are not available data can be cleansed only using business rules derived from domain knowledge. Business rules focus on fixing inconsistencies, but an inconsistency can be cleansed in different ways (i.e. the correction can be not deterministic), therefore the choice on how to cleanse data can (even strongly) affect the aggregate values computed for decision making purposes. The paper proposes a methodology exploiting Finite State Systems to quantitatively estimate how computed variables and indicators might be affected by the uncertainty related to low data quality, independently from the data cleansing methodology used. The methodology has been implemented and tested on a real case scenario providing effective results.