Spend leakage in a cloud estate manifests in many forms and requires a careful analysis of various metrics. We present a suite of features to detect such behavior and provide recommendations for optimization. We apply the proposed solution in practice by presenting a real-world case-study. Through this case-study we demonstrate how many cases of spend leakage go unnoticed by simple handles to manage cloud estate, and how the solutions proposed in the paper can enable the business with better foresight and control on the cloud costs.

Organizations across the globe require a reliable anomaly detection solution that allows for continuous quality control. Considering the scale and complexity of infrastructure, the most common methods include setting a blanket threshold by using knowledge of the experts or by applying simple statistical measures, which results in an alarm deluge. In this paper, we propose an approach to derive optimal thresholds by analyzing both the temporal and spatial properties of metrics related to entities. Additionally, our solution also self-tunes and self-learns to accommodate the tacit knowledge of experts and domains constraints. We demonstrate the effectiveness of our solution through a series of experiments and a real-world case study.

Spend leakage in the procure-to-pay process is one of the prominent challenges that organizations across the globe face. Given the complex dynamics of the procurement process, spend analysis heavily relies on the tacit knowledge of experts. In this paper, we address the problem of detecting maverick spends using a data driven approach. We present approaches to model the behavior dynamics of the procurement process, detect mavericks, and recommend alternate procurement options. We demonstrate the effectiveness of this solution through a real-world case-study.