Abstract
The quality of data analysis and modelling is dependent on its inputs and statistical analysis is of limited value with inappropriate data. To address this issue, a framework for assessing data quality using the example of airport surface safety, i.e. runway / taxiway safety is proposed. The nature of airport surface safety is such that there is a need to account for data from a number of stakeholders, who may possess databases differing in quality, and aggregate this data for subsequent analysis to provide robust safety assessment and mitigation. To address the data quality problem arising from the aggregation of multi-organisational databases, this research proposes a framework for the validation of external data quality based on the underlying data collection and investigation processes. Multi-Criteria Decision Analysis (MCDA) is applied to derive quantitative weights for twelve safety databases based on the quality of the underlying organisational data collection and investigation processes. These weights combined with an internal data quality validation and an indication of the reporting level of an organisation can give a robust indication of the quality of a database.
About the speaker
Sabine Wilke is a PhD student at the CTS, Imperial College London. As a Lloyd’s Register Educational Trust scholar she conducts her research in Transport Risk Management. Her research is primarily focused on the investigation of problems of airport surface safety, in particular runway and taxiway safety. She is developing a holistic model of airport surface safety that integrates the viewpoints of all relevant aviation stakeholders. Sabine holds an MSc (with Distinction) in Supply Engineering and Logistics and a Diploma in Business Administration.