This paper discusses issues related to data-driven decision support systems in event-driven enterprises and proposes Discrete Wavelet Transformation (DWT) as a method to improve these systems. DWT is proposed as a method of data reduction that reduces the effects of excessive data, enabling better visualization and scalability, while preserving patterns, trends, and surprises in the data. A procedural model for using a data-driven decision support system that integrates DWT is presented. Finally, the procedural model is evaluated in experiments based on real event-driven data from a large telecommunications company.