Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality Monitoring

Abstract

The volume of data to be analyzed has increased tremendously in recent years. In order to extract knowledge from this data, domain experts gain new insights with the help of graphical analysis tools for explorative analyses. Here, the reliability and trustworthiness of an exploratory analysis is determined by the quality of the underlying data. Existing approaches require manual testing to ensure data quality which is often neglected. This research aims to introduce a novel interactive approach for seamlessly integrating data quality considerations into the process of explorative data analysis conducted by domain experts. We derive requirements, conduct an extensive literature review, and develop an approach that efficiently combines stakeholders’ strengths, allowing unobtrusive data quality integration in interactive analysis. Our approach enhances trustworthiness due to unobtrusive monitoring of data quality within the context of explorative data analysis. Domain experts gain insights more reliably, bridging the gap between technical requirements and domain expertise. In conclusion, our research presents a promising solution for improving the reliability and trustworthiness of explorative data analysis, especially for domain experts who may lack technical knowledge. By seamlessly integrating data quality into the analytical process, we empower domain experts to extract valuable insights from the ever-increasing volume of data, thereby advancing the field of data-driven decision-making.