Metabolomic data, usually from mass spectrometry or nuclear magnetic resonance spectroscopy, is highly complex and information rich. With continuing advances in metabolite detection technologies, the volume and complexity of data is ever increasing. Computational analysis of metabolomic data, from raw data processing to biological interpretation, is therefore fundamental to realizing the impact of metabolomics on a diverse array of application fields, e.g., environmental toxicity, industrial biotechnology, and biomedicine. This Dagstuhl Seminar extends the Computational Metabolomics series to examine how we can enhance the utility and interpretation of metabolomics data.