Finding a consensus within a distributed collaborative decision making process, which contains a diverse set of data is difficult, especially as most currently used systems cannot qualify their findings by including very disparate data values, without introducing bias. Whether it is numerical values, or opinions gathered from participants, determining the best overall consensus can be improved by the incorporation of a system that can take into account the entire range of data available, improving decision making, and limiting the time required for conflict resolution and team consensus building. Technology developed at the University of Nebraska addresses the issue of consensus building by applying a mathematically sound, effective and trustworthy system to evaluate imprecisely expressed or imprecisely measured information from multiple sources. The system can be used where group team members are geographically separated and/or have diverse opinions and a sound consensus is needed. Even imprecise polling data can be analyzed, leading to a better understanding of public consensus on important social and economic issues. This technology stands to improve the decision making for anyone that relies upon the evaluation of a wide ranging collection of data.