From the data quality criteria identified, the data analyst must now design the data execution plan.
识别出数据质量标准之后，数据分析师就必须设计执行计划。
With this information, the data analyst can determine which data elements should be checked for data quality.
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。
For this case, the data analyst must establish the policies governing "survivorship" (which record survives).
对于这种情况，数据分析必须建立控制“存活”(哪些记录保留下来)的策略。
The data analyst should participate in this process to ensure high-level data quality requirements are included.
数据分析师应该参与这个过程，确保其中包含高水平的数据质量需求。
There are several possibilities as to what the "target system" is for the data analyst working in an SOA project.
对于SOA项目中的数据分析工作，“目标系统”有以下这些可能性。
Often a data analyst or data producer will produce new data values from raw data and include these in a data file;
通常数据分析者或是数据制作者会由原始资料中整理出一些新的数值数据，并且将这些新的数值数据放在数据文件中；
In such cases, the data analyst must build representative data samples from existing test data or sample the production data.
在这样的情况下，数据分析师必须从现有的测试数据或生产数据生成有代表性的数据样本。
The data analyst must, as part of the execution plan, determine against which data sets the investigation tests can be run and who has access to those data sets.
在执行计划中，数据分析师必须确定运行测试所用的数据集，还要确定谁对这些数据集有访问权。
The SAS software is the largest integrated applied information system with the function of complete data access, data management, data analyst and data show.
SAS系统是大型集成的应用信息系统，拥有完备的数据访问、数据管理、数据分析和数据呈现功能。
WHO epidemiologist Dr Francesco Checci (second from left) and data analyst Jerome Chakauya (far right) check patient records at the Beatrice Road Infectious Diseases Clinic in Harare.
世卫组织流行病学家FrancescoChecci博士（左起第2人）和数据分析人员JeromeChakauya （最右侧）检查哈拉雷Beatrice Road传染病医院的患者病历。
The size of source models, the number and type of attributes, and the complexity of defining the transformation can be time-consuming for the data analyst, architect, and implementer.
源模型的大小、属性的数量和类型、以及定义转换的复杂性，对于分析员、架构师和实现者来说，可能很费时间。
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.
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。
Once again, in this case, the data analyst may have to investigate not only the structure of the target but also any data already resident there to ensure the compatibility of the incoming data.
同样，在这种情况下，数据分析可能不仅需要调查目标的结构，还需要调查任何已有的数据，以确保进入的数据的兼容性。
Once again, in this case, the data analyst may have to investigate not only the structure of the target, but also any data already resident there to ensure the compatibility of the incoming data.
同样，在这种情况下，数据分析可能不仅需要调查目标的结构，还需要调查任何已有的数据，以确保进入的数据的兼容性。
Once again, in this case, the data analyst may have to investigate not only the structure of the target, but also any data already resident there to ensure the compatibility of the incoming data.
同样，在这种情况下，数据分析可能不仅需要调查目标的结构，还需要调查任何已有的数据，以确保进入的数据的兼容性。
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