Consolidate queries to improve data quality and performance.
整合查询以提升数据质量和性能。
Data Integration Challenges: Semantic Meaning and Data quality.
数据集成挑战：语义和数据的品质。
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？
The structure of the legacy data is often not documented and the data quality is poor.
遗留数据的结构往往没有文档说明，数据质量令人担忧。
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。
This has an impact on the overall response time and the data quality of your implementation.
这将影响总体响应时间和实现的数据质量。
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。
If a complex form has incorrect data, how does that impact the overall data quality of a project?
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？
The flexible nature of XML sometimes concerns database professionals who worry about data quality.
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。
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。
If there are any data quality problems, both you and your customer need to know as soon as possible.
如果存在数据质量问题，您和您的客户就都需要尽快知道。
The center point for this project-based approach is the actual execution of the data quality analysis.
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 scorecard keeps evolving, and users give their input on which data quality metrics they need most.
计分卡不断演变，用户输入他们最需要的数据质量标准。
This article introduces the most significant aspects of data quality analysis execution in SOA projects.
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。
Figure 1 shows some typical data quality problems that can exist and that can complicate service design.
图1给出一些典型的数据质量问题，这些问题可能导致服务设计复杂化。
Review data quality related issues raised by business and it users relevant to the project requirements.
寻找由与项目需求相关的业务用户和IT用户所引发的数据质量问题。
The effectiveness of the data quality assessment can be greatly enhanced with the right tooling decision.
选择合适的工具可以大大改进数据质量评估的效率。
Data quality is a key prerequisite for using information to gain insightful advantages in the marketplace.
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。
Create detailed reports, charts and summaries that portray data quality levels and present recommendations.
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。
Part 7 of this series introduces the concepts and detailed approach for conducting a data quality analysis.
本系列的第7部分介绍数据质量分析的概念和详细方法。
Formulate a correction process with business case justifications for a particular data quality improvement.
根据业务情况，描述特定数据质量改进的过程。
Create detailed reports, charts and summaries that portray data quality levels, and present recommendations.
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。
Data quality analysis quantifies the characteristics of operational data in the context of its intended use.
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。
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。
An IBM customer in the industrial sector implemented a data warehouse without employing data quality analysis.
工业领域的一家IBM客户实现了一个数据仓库，但是没有进行数据质量分析。
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。
Historically, data warehousing initiatives attempted to address data quality problems downstream from applications.
在历史上，数据仓库曾经试图解决应用程序产生的数据质量问题。
Checking the data quality is not a trivial job; it requires knowledge of both data modeling and the business domain.
检查数据质量并非是一项普通工作；它同时需要数据建模和商业领域的知识。
As each test is executed, results are produced, gathered, and incorporated into the broader data quality assessment.
随着每个测试的执行，都会产生和收集结果，并将结果并入到更广阔的数据质量评估中。
Define business rules and data quality requirements for each business term and entity that is relevant to the project.
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。
应用推荐