Abstract

Good data quality is crucial for any data-driven system’s effective and safe operation. For critical safety systems, the significance of data quality is even higher since incorrect or low-quality data may cause fatal faults. However, there are challenges in identifying and managing data quality. In particular, there is no accepted process to define and continuously test data quality concerning what is necessary for operating the system. This lack is problematic because even safety-critical systems become increasingly dependent on data. Here, we propose a Candidate Framework for Data Quality Assessment and Maintenance (CaFDaQAM) to systematically manage data quality and related requirements based on design science research. The framework is constructed based on an advanced driver assistance system (ADAS) case study. The study is based on empirical data from a literature review, focus groups, and design workshops. The proposed framework consists of four components: a Data Quality Workflow, a List of Data Quality Challenges, a List of Data Quality Attributes, and Solution Candidates. Together, the components act as tools for data quality assessment and maintenance. The candidate framework and its components were validated in a focus group.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.