This talk presents a principled methodology for partitioning item-level data into homogeneous time-series, with the objective of maximizing monitoring coverage and improving the detection of anomalies and drifts. We discuss the theoretical underpinnings of clustering algorithms for this task and describe practical algorithms enabling efficient search for optimal partitioning. We exemplify our approach with a real-world application in large-scale monitoring environments from the online payment domain.