【24h】

Data Quality Management Maturity Model: A Case Study in BPS-Statistics of Kaur Regency, Bengkulu Province, 2017

机译:数据质量管理成熟度模型:以BPS统计为例,班古鲁省考尔县,2017年

获取原文

摘要

Data are widely used in an organization not only for operation but also for strategic level use. Poor data quality can have negative impact for an organization such as poor decision making and planning. Therefore, data quality management becomes an issue growing today not only to the academic but also professional communities. Based on this issue, this paper presents and analyzes a case study developed in a governmental agency, BPS-Statistics of Kaur Regency. For analysis, a data quality maturity model is used to measure the implementation of data quality management in the organization. The results show that for the dimension of ‘Data quality expectations’ is at a maturity of 4.25. ‘Data quality protocol’ is at a maturity of 3.50. ‘Policies’ reaches a maturity of 3.67. ‘Data quality protocol’ and ‘Data standard’ are at a maturity of 4.42. ‘Data governance’ is at a maturity of 3.00. ‘Technology’ is at a maturity 3.17. ‘Performance management’ is at a maturity of 3.33. However, this also implies that implementing these particular dimensions will lead to a direct increase in overall maturity.
机译:数据在组织中不仅用于运营,而且还用于战略级别。不良的数据质量会对组织产生负面影响,例如不良的决策制定和计划。因此,数据质量管理已成为当今不仅是学术界而且是专业团体都日益关注的问题。基于这个问题,本文介绍并分析了在政府机构Kaur Regency的BPS-Statistics中开发的案例研究。为了进行分析,数据质量成熟度模型用于衡量组织中数据质量管理的实施情况。结果表明,“数据质量期望”维度的成熟度为4.25。 “数据质量协议”的到期日为3.50。 “政策”的到期日为3.67。 “数据质量协议”和“数据标准”的到期日为4.42。 “数据治理”的期限为3.00。 “技术”成熟度为3.17。 “绩效管理”的到期日为3.33。但是,这也意味着实施这些特定维度将导致整体成熟度的直接提高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号