Ross Perez is a data Analyst at data visualization company Tableau.
来自数据可视化公司Tableau的数据分析师Ross Perez可以让你一目了然。
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。
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。
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。
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。
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。
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。
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 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。
After talking to the Business Analyst, the System Analyst realizes that this new offering requires a consistent means to access the organization's customer data.
与业务分析人员交流之后，系统分析人员认识到，这个新服务要求采用一致的方式访问组织的客户数据。
For example, a financial analyst would need to see financial data whereas a team member would not.
例如，一个财务分析师需要看财务数据，而其他团队成员是无法看到的。
For example, the analyst gathers roles, tasks, sequence information, resources, data, narratives, requirements, and so on using appropriate tools and uses them as input to construct the BP model.
例如，分析员使用适当的工具收集角色、任务、序列信息、资源、数据、叙述、需求，等等，并将它们作为构建BP模型的输入内容。
Understanding the data quality for this service requires that the analyst first understands how the service interface maps to the application API and then how that API accesses the underlying data.
要想了解这个服务的数据质量，分析师首先需要了解服务接口如何映射到应用程序api，然后了解API如何访问底层数据。
Tracking down a missing customer data update may require the Incident Analyst to check the diagnostics on the J2EE server and the existing back-end systems.
为了追踪丢失的客户数据更新，可能需要事故分析人员检查J2EE和现有后端系统上的诊断信息。
The analyst should also document any business rules that are applied to trade data, particularly data transformation or (crub) procedures, as well as data validation rules.
分析员还应该证明应用于交易数据的所有商业规则，特别是数据变换或数据过程，以及数据确认规则。
Data profiling is not magic — it helps bring data quality issues to light, but you still need a data or business analyst or subject matter expert to review results and draw appropriate conclusions.
数据概要分析并不神奇——它帮助揭示数据质量问题，但是仍然需要一个数据或业务分析师或者主题专家来对结果加以评价，并得出适当的结论。
This is accomplished by bringing together three primary capabilities: pervasive sensors, data management and analyst collaboration.
中心汇集了三个主要功能：普适传感器、数据管理和分析师协作。
All the rules and mappings of these components must be understood for the analyst to identify the correct source data and rules needed to analyze the data quality.
分析师必须了解这些组件的所有规则和映射，才能识别出分析数据质量所需的源数据和规则。
Rather, data mining is used to point out data records that deserve a closer look by a human analyst or expert who must then decide whether to take action or not.
相反，数据挖掘用于指出有待分析师或专家进一步分析的数据记录。然后，析师或专家以此为依据决定是否采取行动。
This scenario assumes two main IT roles are involved: business analyst performs business modeling, and data modeler carries out logical data modeling.
该场景需要涉及两个主要的IT角色：业务分析师执行业务建模，而数据建模师实现逻辑数据建模。
Thus, proper education and communication between data modeler and business analyst is essential.
因此，数据建模师和业务分析师之间需要进行恰当的培训和交流。
The Software Architect also works with the Business Analyst in understanding the data requirements of the current systems and their relationship to the IAA data model.
软件架构师还与业务分析人员一起理解当前系统的数据需求及它们到IAA 数据模型的关系。
As the analyst, the deeper you get into the data, the more you have to be careful that you are not confusing people with the minutiae which can do more harm than good.
作为分析师，对于数据挖掘越深越需要谨慎，你不能用细枝末节迷惑大众，因为这样弊大于利。
As the analyst, the deeper you get into the data, the more you have to be careful that you are not confusing people with the minutiae which can do more harm than good.
作为分析师，对于数据挖掘越深越需要谨慎，你不能用细枝末节迷惑大众，因为这样弊大于利。
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