On the surface, capturing cycle time and throughput metrics seems easy in a Kanban system or tool. For accurate forecasting and decision-making using this data, we better be sure it is captured accurately and free of contaminated samples. For example, the cycle time or throughput rate for a project team working nights and weekends may not be the best data for forecasting the next project. Another choice we have to make is how we handle large and small outlier samples (extreme high or low). These extreme values may influence a forecast in a positive or negative direction, but which way?