Real-world event sequence data, such as activity logs, eye-tracking data, simulation data, and electronic health records, often share characteristics such as a large alphabet of events, fragmentation, noise, and high complexity which makes them difficult to analyze in their raw form. Because of this, simplification and preprocessing through various data transformations are commonly required before the data can be effectively visualized and analyzed. Existing methods for such data transformation are either manually applied and rely heavily on user expertise, or use algorithmic approaches to apply bulk operations which can imply the loss of potentially important information without users being aware. To bridge this gap, we propose a visual analytics approach that aims to successively increase the quality of noisy event sequences by supporting an interactive, context-aware application of data transformations. This is achieved by providing cues concerning the potential loss of information that transformation operations may imply and allowing users to explore, and visually assess their impact on the data. Therefore, a central feature of the approach is that users can tune the data transformation process so that important identified data characteristics are preserved. We motivate the proposed approach in the domain of air traffic control and illustrate it through a usage example, using event sequences derived by merging eye-tracking and simulator data from a human-in-the-loop simulation experiment with 14 air traffic controllers.