Many IT systems use Configuration Management Databases (CMDBs) to keep track of which hardware and software is installed as well as any problems that occur over time. Thus, over time, CMDBs collect large amounts of valuable data that can be used for decision support. This project proposes mining historic data from a CMDB to detect common co-changes that can be used to support change impact analysis. We show that using co-changes helps predict change sets with rates as high as 70% recall and 89% precision. Additionally, we propose using data from other repositories such as scheduling information (e.g., backup processes, build processes, etc.) in conjunction with the data in the CMDB to provide support for root cause analysis. Our work on identifying which data from the different repositories can contribute to a better change impact analysis and root cause analysis framework won the best paper award at the 19th Centre of Advanced Studies Conference (CASCON).