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Data driven decision support

The aim of this special issue is to present current research on data driven decision support. Recently, many discussions about data driven approaches can be found in both the scientific aswell as in the practitioner literature. Under the umbrella of the term “big data”, the ability to extract, prepare, store and analyse very large, diverse, near or realtime data sets is expected to change our views on data and the way how business operates [1]. According to the EU Data Market study, the number of data workers could almost double by the year 2020, the overall data market could get to 111 billion Euro in 2020 and the economic impacts producedby the adoptionof data-driven innovations could rise to 4.7% of GDP in 2020 [3]. The major driver for this is the ability to process more aswell asmore diverse data in near or even real timewhich enables to process data not yet analysed or even to collect more data. The latter is heavily boosted by the growing availability of cheap sensors and increased connectivity. As a result, as much as 90% of the data have been created in the last couple of years [2]. This together creates many new application areas in the context of data driven technologies. The analysis of data is intertwined with the idea of decision support [4]. Thereby, the aim of decision support is to provide the decision maker with a better information basis for decision, not to automate the decision process itself [5]. Decision support and decision support systems are not new. Such systems are designed to help decision makers in utilizing data and models to solve problems to improve the decision quality [6]. But the way we approach data and how we process data changed a lot. Statistics is more and more complemented by artificial intelligence to discover unknown relationships [7]. Such algorithms are especially suited to analyse time series data which are very relevant for decision support in organiza-