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"Big data," a consequence of the "omics" technologies and its analysis by machine learning, have changed the climate of thought in biomedical sciences, shifting the demography of expertise and culminating in a new role: "data scientist." While historically the inquiry on the nature of organisms started with theories (logical reasoning) but no data, we now live in an era of data but no theory. A tacit assumption of modern data analytics is that correlations and clusters in the data constitute knowledge. Through support of technology and data collection, funding agencies promoted this attitude, while neglecting hypothesis-driven inquiry and theory. Data is, of course, an indispensable ingredient of knowledge, but it cannot be the endpoint of inquiry. This article provides key concepts for a fruitful discussion, examines the dualism between data and theory, and proposes how they synergize. Data scientists must learn to appreciate theory, but if the most value is to be extracted from data, theorists should not dismiss brute-force empirical pattern recognition in data. The patterns could motivate the erection of new theories, much as Kepler's law represented a formal "summary" of astronomic data on which Newton's laws could be tested.