Bridging Perceived and Actual Data Quality: Automating the Framework for Governance Reliability
Year: 2025
Type: article
Abstract: Following spatial data capture, stakeholders often invest significant resources to meet technical specifications. This challenge arises largely from varying interpretations of established standards, resulting in data that fails to meet the requirements for ingestion into the enterprise geospatial ecosystem. A key issue lies in the discrepancy between perceived data quality – how stakeholders understand or interpret the performance of the data, which is aligned with technical specifications – and actual data quality, which reflects objective performance when properly measured. The proposed data quality management (DQM) framework addresses this discrepancy by focusing on key aspects of spatial data quality, with an automated program playing a central role in bridging this divide. The framework enhances stakeholder communication and significantly improves the reliability of data governance by providing a comprehensive evaluation of data quality. This evaluation with the outputs combining error presentation through statistics, georeferenced files, and visualization enables rapid interpretation and error resolution. When applied to planned land use (PLU) data, this solution improved efficiency, enhanced overall data quality, and ensured seamless integration into the enterprise Spatial information system. This resulted in a higher level of maturity in data quality management.
Source: Geosciences
Author: Tomaž Podobnikar
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,172
Funder: European Commission
Award: G3611179903
OpenAlex ID: W4408851713