Data Governance to Improve Data Quality for Statistical Process Control (SPC): A Case Study PT. XYZ

Pratiwi, Mulyani; Ruldeviyani, Yova

doi:10.1145/3451471.3451488

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…To develop this strategy, a maturity assessment of BDPJN's data quality management was done using Loshin's Data Quality Management Maturity Model (DQM3) to determine the lacking characteristics. The results were then mapped based on DQM activities in DAMA-DMBOK as the data quality management strategy, which is the best theory for improving data quality [10,[13][14][15][16][17][18][19][20] . This study is expected to produce recommendations that BDPJN management units can use to maintain and improve BDPJN data quality.…”

Section: Introductionmentioning

confidence: 99%

Data Quality Management Strategy To Improve Remote Sensing Data Quality: A Case Study On National Remote Sensing Data Bank

Payani

Mayanda

Adiresta

et al. 2023

JTS

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National Remote Sensing Data Bank (Bank Data Penginderaan Jauh Nasional; BDPJN) is the operational implementer in the field of remote sensing to meet the needs of national data on remote sensing images. Remote sensing images are essential in the Indonesian government for various needs, such as in regional (spatial planning, city boundaries), disaster (forest fires, floods, landslides, earthquakes, volcanic eruptions), plantation, natural resources, and environment sectors. Quality management of BDPJN's satellite images becomes challenging due to the increasing number of products owned and users annually. For this reason, a data quality management strategy is needed to guarantee and improve the quality of BDPJN data. To develop such a strategy, an assessment of the maturity of BDPJN's data quality management was conducted from the aspect of data processing by implementing Loshin's Data Quality Management Maturity Model (DQM3) to find out the characteristics that were lacking. The results were then mapped based on Data Quality Management (DQM) activities in DAMA-DMBOK as a recommendation for data quality management strategies. This study applies quantitative research where data collection was done by distributing questionnaires to 24 respondents who are data stewards of a medium, high, and very high-resolution mosaic images. Based on the assessment, BDPJN is in the maturity level of Defined to Managed. The recommendations are 21 DQM activities that can be carried out to improve BDPJN data quality.

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