| Title: | A framework for bridging perceived and actual quality through automation : strengthening data reliability and governance |
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| Authors: | ID Podobnikar, Tomaž (Author) |
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| Files: |  RAZ_Podobnikar_Tomaz_2025.pdf (2,66 MB)
MD5: CA704AC748CF1D16C32AA52129768672
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.25 - Other Component Parts |
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| Organization: | FIŠ - Faculty of Information Studies in Novo mesto
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| 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. |
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| Keywords: | quality assurance/quality control (QA/QC), continuous process improvement, spatial data quality, data steward, data governance, planned land use data, perceived vs. actual data quality, geospatial, data quality management (DQM), uncertainty management |
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| Publication date: | 01.01.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | Str. 1-19 |
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| PID: | 20.500.12556/ReVIS-11589  |
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| COBISS.SI-ID: | 232354307 |
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| UDC: | 004.6:005.336.3 |
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| ISSN on article: | 2310-287X |
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| DOI: | 10.20944/preprints202501.0932.v1 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 11. 4. 2025;
Posted Date: 13 January 2025;
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| Publication date in ReVIS: | 14.04.2025 |
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| Views: | 144 |
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| Downloads: | 2 |
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