整合查询以提升数据质量和性能。
Consolidate queries to improve data quality and performance.
这将影响总体响应时间和实现的数据质量。
This has an impact on the overall response time and the data quality of your implementation.
根据业务情况，描述特定数据质量改进的过程。
Formulate a correction process with business case justifications for a particular data quality improvement.
数据质量分析实践的焦点是数据质量级别的度量。
The central focus of the data quality analysis practice is the measurement of the data quality level. This measurement is across three core areas
选择合适的工具可以大大改进数据质量评估的效率。
The effectiveness of the data quality assessment can be greatly enhanced with the right tooling decision.
数据质量分析的结果还可以反馈到规范化数据模型。
The results of the data quality analysis may also feed back into the canonical data model.
此外还有其他有关数据质量和与假设因果关系等遁词。
There are other quibbles to do with the quality of the data and assumptions about cause and effect.
数据清理服务接受数据质量为未确定的数据作为输入。
The data cleansing service receives data with an undetermined level of data quality as input.
本系列的第7部分介绍数据质量分析的概念和详细方法。
Part 7 of this series introduces the concepts and detailed approach for conducting a data quality analysis.
遗留数据的结构往往没有文档说明，数据质量令人担忧。
The structure of the legacy data is often not documented and the data quality is poor.
当在数据库中输入不准确的数据时，数据质量就会受损。
Data quality can be corrupted when inaccurate data is entered into a database. For example, someone could create a record for "Ms."
这个基于项目的方法的中心点是数据质量分析的实际执行。
The center point for this project-based approach is the actual execution of the data quality analysis.
数据质量并非华而不实，它已经成为业务的关键操作要素。
Data quality no longer sits on the shelf as a luxury, nice-to-have item; instead, it has become a key operational element for businesses.
数据质量使用信息来在市场中获得突出优势的关键先决条件。
Data quality is a key prerequisite for using information to gain insightful advantages in the marketplace.
本文介绍SOA项目中数据质量分析执行的最重要的几个方面。
This article introduces the most significant aspects of data quality analysis execution in SOA projects.
创建描述数据质量水平的详细报告、图表和汇总，并提出建议。
Create detailed reports, charts and summaries that portray data quality levels, and present recommendations.
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？
If you want to boost data quality, how will you know if you've made progress?
创建详细的报告、图表和总结，以描绘数据质量级别和提供建议。
Create detailed reports, charts and summaries that portray data quality levels and present recommendations.
XML灵活的本质有时候让数据库专业人员对数据质量很担心。
The flexible nature of XML sometimes concerns database professionals who worry about data quality.
为与项目相关的每个业务术语和实体定义业务规则和数据质量需求。
Define business rules and data quality requirements for each business term and entity that is relevant to the project.
通过清晰地关注服务的核心需求，数据质量分析的价值将得以实现。
By clearly focusing on the core requirements of the services to introduce, the value of the data quality analysis will be realized.
数据质量分析在使用数据的上下文中对操作性数据的性质进行量化。
Data quality analysis quantifies the characteristics of operational data in the context of its intended use.
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。
The data analyst should participate in this process to ensure high-level data quality requirements are included.
由于对元数据管理能力需求的增长，元数据质量最近受到更多的关注。
Metadata quality has received increased attention lately due to the increasing demand for metadata management capabilities.
规则描述了干净数据应该如何出现，以及一个监测数据质量的强大工具。
Rules describe how clean data should appear, and are a powerful way to monitor the quality of your data.
如果一个复杂的表单包含错误的数据，对整个项目的数据质量有什么影响？
If a complex form has incorrect data, how does that impact the overall data quality of a project?
失业这样重要的因素也要被忽略掉，因为数据质量参差不齐，不具可比性。
The data on unemployment were too spotty to be comparable and so this important factor is discounted too.
内容质量经常被部分忽视，这是因为评估内容质量比评估数据质量更困难。
Content quality is often neglected partially because evaluating content quality is a much harder task than evaluating data quality.
由于存在不同的数据质量定义视图，不正确地使用数据会导致数据质量差。
By having disparate views on what the definition of data quality is, poor quality can be experienced through incorrect use of data.
使用这些映射识别在服务模型上下文中研究数据质量所需的映射和集成规则。
These mappings are used to identify the mapping and integration rules needed to explore the data quality characteristics within the context of the service model.
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