Data analysis is an interdisciplinary science. Traditionally its development has been driven by the areas of application, but nowadays its development is also stimulated by the ever-changing possibilities promised by progress in computer technology. Huge data sets and non-numerical data, such as text data, image data, and metadata, present both challenges and opportunities for modern data analysts. These in turn lead to new types of problems and require the development of new types of models. Intelligent data analysis also requires that one take proper advantage of the largely complementary abilities of humans and computers. Interactive graphics, an important tool for modern intelligent data analysis, nicely illustrates this: the production of such graphics, and the ability to manipulate them in real time, requires advanced computational facilities; but the ability to interpret them requires the capacity to synthesise possessed only by the human eye and mind. Intelligent data analysis also requires one to have a proper strategy for analysis. Analysis without strategy is surely one of the hallmarks of unintelligent data analysis. Likewise, a key to intelligent data analysis is the ability to recognise what is important in a problem—what counts and what doesn't count.