Data reusability has become a distinct characteristic of scientific, commercial, and administrative practices nowadays. However, an unlimited and careless reuse of data may lead to privacy breaches and unfair impacts on individuals and vulnerable groups. Data content adaption is a key aspect of preserving data privacy and fairness. Often, such content adaption affects data utility adversely. Further, the interaction between privacy protection and fairness protection can be subject to making trade-offs because mitigating privacy risks may adversely affect detecting unfairness and vice versa. Therefore, there is a need for research on understanding the interactions between data utility, privacy and fairness. To this end, in this contribution, we use concepts from causal reasoning and argue for adopting an integrated view on data content adaption for data driven decision support systems. This asks for considering the operation context wholistically. By means of two cases, we illustrate that, in some situations, local data content adaption may lead to low data quality and utility. An integrated wholistic approach, however, may result in reuse of the original data (i.e., without content adaption, thus in higher data utilization) without adversely affecting privacy and fairness. We discuss some implications of this approach and sketch a few directions for future research.