Data quality on KDD: A real-life scenario

The growing diffusion of IT-based services generates a lot of data useful for supporting the activities of firms, organisations, and state agencies. In such a context, data quality tasks are frequently addressed using cleansing routines, often framed in the wider context of ETL pro- cesses (Extraction, Transformation, and Loading). The design of these cleansing routines often relies on the experience of domain-experts, and this makes the evaluation of the quality level achieved a relevant concern to ensure the believability of the analysed results. In this paper we describe two model based techniques aimed at respec- Tively evaluating the consistency of a dataset and at identifying the cleansing alternatives. The techniques have been applied on a real-world dataset derived from the Italian labour market domain, which we made publicly available to the community.