Quantitative user research, in special user (usability) testing, plays a central role in the field of data-informed design. Obviously, if the data is incomplete or inaccurate, important details might be overlooked and the real problems remain unknown. In the solution side, without a proper generation and selection of ideas the result might be costly and/or unnefective solutions, ruining the investment. Among possible causes for those problems, lack of resources (versus amount of data) and ineffective techniques are in the top of the list. This talk, presented at Interaction South America (ISA 2016) aimed to show a simple, objective way to 1) organize quantitative data generated from user testing and 2) generate and choose collaboratively the most viable solutions. It follows the divergence/convergence model and adapts the Double Diamond Diagram (UK Design Council, 2005) more especifically to user research. It also relies on the agile / lean mindset in order to keep things actionable and result-oriented. The result is an end-to-end framework that goes from issue to insight. It consists in four stages: 1) Collect and normalize data; 2) Prioritize issues in a multidimensional analysis. 3) Generate solutions collaboratively, taking into account that a solution can address multiple issues. 4) Select solutions in a multidimensional, ROI based analysis.