The increasing availability of new data sources, based on low-cost, non-probabilistic samples derived from unplanned statistical processes, such as administrative records and other forms of big data, combined with rising nonresponse rates, which have made traditional surveys increasingly expensive and potentially less representative, has recently stimulated statistical research in the field of multisource statistical processes. In this context, quality evaluation plays a crucial role in guiding decisions about the role played by the different data sources, whether they serve as primary or auxiliary inputs. The assessment of quality focuses primarily on the bias of estimates, rather than their variability, due to the selection and measurement errors that typically affect these types of data. This paper highlights the main characteristics of statistical processes that rely on either probabilistic or non-probabilistic samples as primary data sources, and describes several multisource processes currently implemented by Istat in the official statistical production.