Data Center management and operation have been a challenging element for IT infrastructure. We proposed a framework to address the key problems of workload prediction & balance; resource overhead & waste reduction; component/disk failure and performance degradation by combining data analytics and machine learning technologies. We will illustrate the analytics intelligence with a case study of disk failure prediction which has been one of the fundamental problem in distributed storage systems. The disk failure prediction uses SMART data collected from each disk which are then crunched through an analytics engine empowered by machine learning algorithms to predict disk failure with 90% of false positive rate and two weeks of lead time. We also believe this framework will serve as the stepping stone for data center modernization, automation, and orchestration
