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Bridging Perceived and Actual Data Quality: Automating the Framework for Governance Reliability

The discrepancy between perceived and actual data quality, shaped by stakeholders’ interpretations of technical specifications, poses significant challenges in governance, impacting decision-making and stakeholder trust. To address this, we introduce an automated data quality management (DQM) framework, implemented through the NRPvalid toolkit, as a standalone solution incorporating over 100 assessment tools. This framework strengthens data quality evaluation and stakeholder collaboration by systematically bridging subjective perceptions with objective quality metrics. Unlike traditional producer–user models, it accounts for complex, multi-stakeholder interactions to improve data governance. Applied to planned land use (PLU) data, the framework significantly reduces discrepancy, as quantified by error score metrics, and directly enhances building permit issuance by streamlining interactions among administrative units, municipalities, and investors. By evaluating, refining, and seamlessly integrating spatial data into the enterprise spatial information system, this scalable, automated solution supports constant data quality improvement. The DQM and its toolkit have been widely adopted, promoting transparent, reliable, and efficient geospatial data governance.

Ketersediaan
#
Perpustakaan BIG (Eksternal Harddisk) 550
455
Tersedia
Informasi Detail
Judul Seri
Geosciences
No. Panggil
550
Penerbit
Switzerland : MPDI.,
Deskripsi Fisik
25 hlm PDF, 6.139 KB
Bahasa
Inggris
ISBN/ISSN
2076-3263
Klasifikasi
550
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
online resource
Edisi
Vol.15, Issue 4, April 2025
Subjek
Geospatial
perceived and actual data quality
data quality management (DQM)
quality assurance
quuality control
spatial data quality
data quality standards
data governance
planned land use
automation
uncertainty management
Info Detail Spesifik
Geosciences
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

Lampiran Berkas
  • Bridging Perceived and Actual Data Quality: Automating the Framework for Governance Reliability
    The discrepancy between perceived and actual data quality, shaped by stakeholders’ interpretations of technical specifications, poses significant challenges in governance, impacting decision-making and stakeholder trust. To address this, we introduce an automated data quality management (DQM) framework, implemented through the NRPvalid toolkit, as a standalone solution incorporating over 100 assessment tools. This framework strengthens data quality evaluation and stakeholder collaboration by systematically bridging subjective perceptions with objective quality metrics. Unlike traditional producer–user models, it accounts for complex, multi-stakeholder interactions to improve data governance. Applied to planned land use (PLU) data, the framework significantly reduces discrepancy, as quantified by error score metrics, and directly enhances building permit issuance by streamlining interactions among administrative units, municipalities, and investors. By evaluating, refining, and seamlessly integrating spatial data into the enterprise spatial information system, this scalable, automated solution supports constant data quality improvement. The DQM and its toolkit have been widely adopted, promoting transparent, reliable, and efficient geospatial data governance.
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