Statistical data represent one of the most important sources of information both for humans and organizations alike. However, accessing, querying and correlating statistical data demand a great deal of effort, especially in situations that involve different organizations. Therefore, solutions to facilitate the manipulation and integration of large statistical databases add value to this scenario. In this dissertation we propose a framework that allows statistical data to be efficiently processed and represented as RDF triples. Based on the DataCube Vocabulary, W3C s triplification standard, the proposed solution makes it easy to query, analyze, and reuse statistical data in RDF format. The reverse process, RDF for Excel, is also supported, so as to offer a solution for the integration and use of RDF data in spreadsheets.