Dataset characterizing tailoring techniques, challenges, impacts and metrics in Large-Scale agile development

Large-Scale Agile Development refers to the coordination of Agile practices across multiple teams, departments and globally distributed sites. While Agile is the software development industry standard, Large-Scale Enterprises frequently struggle with one-size-fits-all adoption and require specific tailoring to fit their complex organizational structures. Consequently, Agile Techniques Tailoring, the adaptation of specific frameworks like Scrum, Kanban or eXtreme Programming to fit complex structures and organizational culture, has become critical for Large-Scale Enterprises seeking to improve flexibility and time-to-market. This tailoring approach has been extended over time to scaled frameworks such as Scaled Agile Framework, Disciplined Agile Delivery, Large-Scale Scrum or the Spotify Model. This dataset provides a comprehensive characterization of evidence generated through a Systematic Mapping Study which covers publications between 2015 and 2025, following a rigorous five-phase screening process based on inclusion and exclusion criteria. The dataset includes a Characterization Scheme containing 36 attributes and a full inventory of the primary study list used for data extraction, including challenges, positive/negative impact techniques, the specific metrics used to evaluate success and the approaches used to deliver value. The dataset allows researchers and practitioners to navigate empirical evidence regarding Agile transformation, identify research gaps and compare the effectiveness of different Agile techniques.