这个维把数据质量元素的业务需求整合在一起，包括数据质量元素的标准化、匹配和链接等。
This dimension incorporates the business requirements for standardization and matching or linkage of data quality elements.
掌握了这些信息，分析师就可以判断在进行数据质量分析时应该检查的数据元素。
With this information, the data analyst can determine which data elements should be checked for data quality.
不需要寻找与数据质量相关的所有列、字段和元素，只需要找到影响信息结构和信息理解的那些元素。
Not all defined columns, fields, and elements are relevant to data quality, only those that affect the structure and understanding of information.
在识别出的数据元素上应用数据质量度量，以评估当前的标准化和匹配支持，将这些结果转换为业务术语。
Apply data quality measures to identified data elements to assess current standardization and matching support and translate these results to business terminology.
对数据元素应用公认的业务定义和规则，就可以防止不一致数据质量问题。
Applying a commonly agreed business definition and rules to the data elements provides insurance against inconsistent data quality issues.
数据分析师要与业务领域专家和应用程序专家协作，评估哪些数据元素对于数据质量分析比较有意义。
The data analyst works with business domain experts and application experts to assess which elements are more meaningful than others in order to identify how data quality will be analyzed.
将每个数据元素从数据源的数据模型映射到新的集中数据模型的业务规则，包括数据质量和可接受的值要求的列表。
Business rules mapping for each data element from a data source's data models to the new centralized data model, including data quality and the list of acceptable value requirements.
将每个数据元素从数据源的数据模型映射到新的集中数据模型的业务规则，包括数据质量和可接受的值要求的列表。
Business rules mapping for each data element from a data source's data models to the new centralized data model, including data quality and the list of acceptable value requirements.
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