In the CDISC era, biotechnology and pharmaceutical companies are paying increasing attention to how analysis dataset specifications are documented and accuracy of datasets that are generated.  It has always been a desirable practice to record the details about analysis datasets, including the structure of the dataset, the source of data variables, the logic of derivations, and methods of special data handling.  For FDA submissions that include analysis data model (ADaM) datasets, the analysis data specifications must be included in submission documentation.  The use of independent programming is increasingly a gold standard validation method.  In this paper, we describe techniques for leveraging analysis data specifications to automate processes in producing analysis datasets, quality control of the data by independent programming validation, and generating Data Definitions (define.xml) content.  The result of this process is increased confidence in the quality of the data and the reliability of the documentation.