Organizations collect a vast amount of data of different types, from various sources, and through different channels. Primarily, these data are used by these organizations to facilitate their core business processes. However, today we witness a growing tendency to use these data for other purposes than that they are collected for. To this end, the data from one information system are combined with those of other information systems. Subsequently, the combined data are analyzed with advanced data analytics tools. Although there is a strong and practical need to apply such findings of data analytics to improve, among others, organizations’ (social) services, it is often not straightforward how to apply these findings in practice. This is due to many challenges arising from legal, ethical, and data quality concerns. In this chapter, we discuss the main reasons that hamper the application of data analytics findings, particularly pertaining to data collection processes and data analysis processes (like data mining and statistics). These reasons include inadequate transformations of statistical truths to individual cases, chances to fall into the trap of system realities, and required efforts to deal with the evolving semantics of data over time. The latter is due to the fact that our (social) environment is subjected to constant changes. We discuss two strategies to harvest data analytics findings in a responsible way. By means of some real-life examples in the field of social services we illustrate the applications of the strategies in practice. Furthermore, we argue that the findings from data-driven analytics may augment real-world ecosystems if they are applied with caution and responsibly.