Currently, the strategic use of Big Data for decision analytics is a challenge of utmost relevance for numerous leading enterprises. Following a clear management commitment, most companies have prepared themselves with a considerable IT infrastructure to meet the challenges of data-driven decisions. However, there is often a serious discrepancy between the technical possibilities for intelligent use of Big Data and its actual application for immediately available use cases. This gap can very often be contributed to the lack of organizational development. In order to tap the full potential in the long term, it is not enough to rely on the know-how of data scientists in the short term. In the future, engineers will have to act as data scientists themselves. Hence, a mindset change and corresponding qualification measures are essential. In this talk, we present the iterative and incremental User-centered Smart Data approach for effective decision analytics. This innovative Design Thinking based methodology involves users and their needs right from the start and rapidly develops highly scalable data analyses in an agile manner and conforming to the respective IT policies. The resulting speedboats in form of prototypes enable sustainable solutions accepted by central IT departments. Customized training concepts provide the required backwind for the necessary mindset change of the engineers. Concrete case studies show how the existing gap between IT and specialist departments is closed and data based and algorithmically sound decision-making processes can be established efficiently. For example, for a premium automobile OEM, the application of User-centered Smart Data facilitated the optimization of the operating strategy of hybrid drives to maximize recuperation performance.