Why the Data Train Needs Semantic Rails

In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today's chaotic information universe, how one would understand which data sets can be meaningfully integrated, and how to communicate the results to humans and machines alike. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights work best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation. Equally important, however, are questions of how to publish data effectively and break up data silos, how to retrieve data, how to enable the exploration of unfamiliar data sets from different domains, how to access provenance information, how to determine whether data sets can be meaningfully reused and integrated, how to prevent data from being misunderstood, how to combine data with processing services and workflows on the fly, and finally how to make data readable and understandable by machines and humans.