质量数据管理

数据管理

由于数据清洗（Data Cleaning）工具通常简单地被称为数据质量（Data Quality）工具，因此很多人认为数据质量管理，就是修改数据中的错误、是对错误数据和垃圾数据进行清理。这个理解是片面的，其实数据清洗只是数据质量管理中的一步。数据质量管理（DQM），不仅包含了对数据质量的改善，同时还包含了对组织的改善。针对数据的改善和管理，主要包括数据分析、数据评估、数据清洗、数据监控、错误预警等内容；针对组织的改善和管理，主要包括确立组织数据质量改进目标、评估组织流程、制定组织流程改善计划、制定组织监督审核机制、实施改进、评估改善效果等多个环节。
任何改善都是建立在评估的基础上，知道问题在哪才能实施改进。通常数据质量评估和管理评估需通过以下几个维度衡量。
评估
Data managementSince data cleaning tools are often simply referred to as data quality tools, many people think of data quality management as correcting errors in data and cleaning up erroneous and junk data. This understanding is one-sided. In fact, data cleaning is only a step in data quality management. Data Quality Management (DQM) not only includes the improvement of data quality, but also the improvement of the organization. For data improvement and management, it mainly includes data analysis, data evaluation, data cleaning, data monitoring, error warning, etc. For organizational improvement and management, it mainly includes establishing organizational data quality improvement goals, evaluating organizational processes, and formulating organizational process improvement Plan, formulate organizational supervision and review mechanism, implement improvement, evaluate improvement effect and other links.Any improvement is based on evaluation, knowing where the problem is before implementing the improvement. Usually data quality assessment and management assessment need to be measured through the following dimensions.Evaluate