With the ascendency of data and the rise of interest in analytics, organizations are becoming more interested in the use of data to make their business processes more intelligent and reactive. BI applications are a common way that organizations integrate analytics in their processes. However, it can be days, weeks or even months before a manual response is undertaken based on a human interpreting a report. Even when information technology supports automatic responses within an organization, it is often implemented in an ad hoc manner without following a systematic framework. In this thesis, we present a reactive performance monitoring (RPM) framework which aims at automating the link from the analytical (how well is the operational achieving the strategic) to the operational (the particular process steps implemented within an organization that determine its behavior) aspects of businesses to bypass the strategic (the high level and long term goals an organization is trying to achieve) as needed and reduce the latency between knowledge and action. Our RPM framework is composed of an architecture, a methodology, and a rule environment which permits the redaction of rules possessing relevant conditions and actions. In addition, we present an OLAP rule engine which is demonstrated to be effective in our framework where events are streamed in, reacted upon in real-time, and stored in an OLAP database. To develop and evaluate our framework, two case studies were undertaken. The first was done using IBM technologies implementing an application made to identify patients at high risk of cancer recurrence. The second was done using open source technologies. With this second implementation, we created an application that has the goal of informing women from at risk populations of the different stages of pregnancy on a weekly basis.