提高数据质量。
Data quality analysis for an SOA project.
SOA项目的数据质量分析。
Case study: An example of data quality service.
案例学习：数据质量服务实例。
Can they state a dollar cost for a data quality?
这些标准描述了数据质量的成本了吗？
Design the data quality analysis execution plan.
设计数据质量分析执行计划。
An SOA project approach to data quality analysis.
为SOA项目执行数据质量分析的方法。
When it comes to SAs, data quality is a key issue.
就SA 而言，数据质量是个重要问题。
Data quality analysis provides this understanding.
数据质量分析就能提供这样的理解。
This article discusses causes of poor data quality.
本文讨论造成数据质量差的原因。
Table 2: Technical data quality dimensions - domains.
表2：技术数据质量维-域。
Table 3: Technical data quality dimensions - entities.
表3：技术数据质量维-实体。
We use data quality tools to profile and cleanse bad data.
我们使用数据质量工具来配置和清洗错误数据。
Consolidate queries to improve data quality and performance.
整合查询以提升数据质量和性能。
Does the business data quality meet the business 'requirements?
业务数据质量是否满足了业务需求？
Data Integration Challenges: Semantic Meaning and Data quality.
数据集成挑战：语义和数据的品质。
Data quality analysis could have helped to avoid these problems.
数据质量分析可以帮助避免这些问题。
Conducting the data quality analysis within the project framework.
在项目框架内进行数据质量分析。
A measurable business result of CCMS is the data quality improvements.
CCMS的一个可测定的业务结果是数据质量的改进。
Table 4: Business Process Data Quality Dimensions – Integrated sources
表4:业务流程数据质量维 –集成的源
Data quality is one of the important issues in identifying data sources.
数据质量是识别数据源中的重要问题之一。
If you want to boost data quality, how will you know if you've made progress?
如果想要提高数据质量，那么如何能够知道是否已经取得了进步？
Both of these data quality dimensions influence SOA service and process design.
这两个数据质量维影响SOA服务和过程设计。
There are many benefits to both data quality and performance with this approach.
使用此方法对数据质量和性能都有很多好处。
The detailed data quality analysis pattern will be covered in Part 7 of this series.
本系列的第7部分将详细讨论数据质量分析模式。
Can the customer guarantee the data quality of the corrected data fields in the future?
客户是否可以保证将来已纠正数据字段的数据质量呢？
The results of the data quality analysis may also feed back into the canonical data model.
数据质量分析的结果还可以反馈到规范化数据模型。
The data cleansing service receives data with an undetermined level of data quality as input.
数据清理服务接受数据质量为未确定的数据作为输入。
Addressing every potential data quality exposure in the solution is often an overwhelming task.
解决解决方案中的所有数据质量问题往往是很艰巨的任务。
Apply data quality metrics to data from identified data stores to ascertain data quality levels.
将数据质量度量指标应用到来自已识别的数据存储的数据上，以确定数据质量级别。
The proposed solution is to conduct a data quality assessment during service analysis and design.
建议的解决方案是在服务分析和设计期间进行数据质量评估。
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