{"title":"改进数据质量管理的策略:以印度尼西亚政府组织主数据为例","authors":"Rizqa Nulhusna, Nur Fajar Taufiq, Y. Ruldeviyani","doi":"10.1109/ISITDI55734.2022.9944466","DOIUrl":null,"url":null,"abstract":"Data is important for organizations to support their operational and decisional activities. Organizations need to ensure that their data is high quality and appropriate for use. This study was conducted at a Government Organization in Indonesia that is currently focusing on a reform agenda in information technology and databases. The organization established a dedicated data management unit and executed data updating programs to support data quality management (DQM). The purpose of this study is to recommend the strategy to improve the organization's DQM, especially on master data. Therefore, it is important to assess the DQM Maturity Level to determine their current state and build up recommendations upon that. This study assessed the DQM maturity level on the organization's master data using the Data Quality Framework by D. Loshin. Overall, the maturity level of DQM on the organization's master data is at level 3 (defined). Recommendations for improving DQM in the organization based on DQM activities in DMBOK are adopting or developing a data quality framework to guide DQM strategy, managing data quality rules related to data quality dimensions, ensuring data quality publication, establishing data quality SLAs and developing dashboard and reporting applications for data users.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strategy to Improve Data Quality Management: A Case Study of Master Data at Government Organization in Indonesia\",\"authors\":\"Rizqa Nulhusna, Nur Fajar Taufiq, Y. Ruldeviyani\",\"doi\":\"10.1109/ISITDI55734.2022.9944466\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data is important for organizations to support their operational and decisional activities. Organizations need to ensure that their data is high quality and appropriate for use. This study was conducted at a Government Organization in Indonesia that is currently focusing on a reform agenda in information technology and databases. The organization established a dedicated data management unit and executed data updating programs to support data quality management (DQM). The purpose of this study is to recommend the strategy to improve the organization's DQM, especially on master data. Therefore, it is important to assess the DQM Maturity Level to determine their current state and build up recommendations upon that. This study assessed the DQM maturity level on the organization's master data using the Data Quality Framework by D. Loshin. Overall, the maturity level of DQM on the organization's master data is at level 3 (defined). Recommendations for improving DQM in the organization based on DQM activities in DMBOK are adopting or developing a data quality framework to guide DQM strategy, managing data quality rules related to data quality dimensions, ensuring data quality publication, establishing data quality SLAs and developing dashboard and reporting applications for data users.\",\"PeriodicalId\":312644,\"journal\":{\"name\":\"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISITDI55734.2022.9944466\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISITDI55734.2022.9944466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Strategy to Improve Data Quality Management: A Case Study of Master Data at Government Organization in Indonesia
Data is important for organizations to support their operational and decisional activities. Organizations need to ensure that their data is high quality and appropriate for use. This study was conducted at a Government Organization in Indonesia that is currently focusing on a reform agenda in information technology and databases. The organization established a dedicated data management unit and executed data updating programs to support data quality management (DQM). The purpose of this study is to recommend the strategy to improve the organization's DQM, especially on master data. Therefore, it is important to assess the DQM Maturity Level to determine their current state and build up recommendations upon that. This study assessed the DQM maturity level on the organization's master data using the Data Quality Framework by D. Loshin. Overall, the maturity level of DQM on the organization's master data is at level 3 (defined). Recommendations for improving DQM in the organization based on DQM activities in DMBOK are adopting or developing a data quality framework to guide DQM strategy, managing data quality rules related to data quality dimensions, ensuring data quality publication, establishing data quality SLAs and developing dashboard and reporting applications for data users.