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Data Quality Improvement Strategy for the Certification of Telecommunication Tools and Equipment: Case Study at an Indonesia Government Institution

机译:电信工具和设备认证的数据质量改进策略:印度尼西亚一家政府机构的案例研究

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The problem based on the findings of the Supreme Audit Agency (BPK) on licensing performance checks at the Ministry of Communication and Information Technology (KOMINFO) is that the certification data of telecommunication tools and equipment in the e-certification information system database is incomplete, inaccurate, and invalid in supporting equipment and equipment certification services telecommunication. Based on these conditions, the level of maturity of data quality management is measured. The measurement of the level of maturity of data quality management is carried out using the Modelo Alarcos de Mejora de Datos (MAMD) 2.0 framework. The result of measurement data quality maturity level is level 1, while the expected level is level 2. The strategy to improve the quality of certification data for telecommunication tools and equipment is prepared based on an analysis of the causes of data problems, gaps in the current condition of quality data management and expectations, and regulations related to data governance. The recommendations produced are grouped into eight data discipline points that need to be implemented by the Directorate of Standardization agreeing to reach the desired level of data quality management maturity. Eight points of data discipline are data requirements management, technology infrastructure management, configuration management, historical data management, data security management, data quality control and control, data life cycle management, as well as standard resolutions, policies, and procedures.
机译:根据最高审计署(BPK)关于通信和信息技术部(KOMINFO)许可证绩效检查的调查结果,电子认证信息系统数据库中电信工具和设备的认证数据不完整、不准确,在电信配套设备和设备认证服务中无效。基于这些条件,可以衡量数据质量管理的成熟度。使用Modelo Alarcos de Mejora de Datos(MAMD)2.0框架对数据质量管理的成熟度进行测量。测量数据质量成熟度等级的结果为1级,而预期等级为2级。提高电信工具和设备认证数据质量的战略是在分析数据问题的原因、质量数据管理现状的差距和期望以及与数据治理有关的法规的基础上制定的。产生的建议分为八个数据规程点,需要由标准化委员会实施,以达到所需的数据质量管理成熟度水平。数据学科的八点是数据需求管理、技术基础设施管理、配置管理、历史数据管理、数据安全管理、数据质量控制和控制、数据生命周期管理,以及标准解决方案、策略和程序。

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