Reproducibility in Data Analysis research has long been a significant concern, particularly in the areas of Bioinformatics and Computational Biology. Towards the aim of developing reproducible and reusable processes, Data Analysis management tools can help giving structure and coherence to complex data flows. Nonetheless, improved software quality comes at the cost of additional design and planning effort, which may become impractical in rapidly changing development environments. I propose that an adjustment of focus from processes to data in the management of Bioinformatic pipelines may help improving reproducibility with minimal impact on preexisting development practices.