Approaches of data analysis in the context of Business Intelligence solutions are presented, when the data is scarce with respect to the needs of performing an analysis. Several scenarios are presented: usage of an initial dataset obtained from primary data as a reference for the quality of the results, enriching the dataset through decoration with derived attributes and enriching the dataset with external data. Each type of dataset decoration is used to improve the quality of the analysis' results. After being subject to improvement using the presented methods, the improved dataset contains a large number of attributes regarding a subject. As some attributes refer to sensitive information or imply sensitive information about the subject, therefore dataset storage needs to prevent unwanted analysis that could reveal such information. A method for dataset partitioning is presented with respect to the predictive capacity of a set of attributes over a sensitive attribute. The proposed partitioning includes also means to hide the link between the real subject and stored data.