Recent technological advances in the detection of genomic structural variation have revolutionized the field of medical genetics. Genome-wide screening for copy-number variants in routine molecular diagnostics unveiled the presence of an unforeseen amount of structural variation in the genome. Owing to the massive amount of patients analyzed, the analysis of the resulting data became exponentially more complex. Simultaneously, novel insights in the impact of structural variation on the phenotype forced the re-evaluation of the pathogenicity of copy-number variations in more complex inheritance models. As a consequence, the challenge of today's genetics shifted from the mere detection of structural variation to the correct annotation and interpretation of the data. Various databases and data mining tools are available to help in the interpretation of the data, but making decisions on the pathogeniticy of the variation is still challenging. This review provides an overview of current laboratory techniques to detect structural variation, options to analyze and annotate data from genome-wide methods and caveats to take into account in interpretation of results.