Organisations striving to improve data quality must consider appointing data stewards, according to Gartner. 按照Gartner的说法， 企业要提高数据质量必须考虑任命数据管家。

The success of data stewardship requires organisations to move toward a culture that views data as a competitive asset rather than a necessary evil and define clear goals for data-quality improvement.  成功的数据管理工作，需要企业养成一种文化，查看竞争资产资产数据就是灭顶之灾， 定义清晰的数据质量提升目标。

A recent European Gartner BI survey of more than 600 business intelligence (BI) users found that more than 35 percent identified data quality as a top-three BI problem facing their organisation in the next 12-18 months, making it the second biggest challenge overall. 一份当前的欧洲Gartner 对超过600家BI用户的BI调查，发现超过35%的被调查者表示，在过去的12-18月中鉴别数据质量是他们所面对的Top3的BI问题，实现它是第二个最大的挑战。

"Data quality is a business issue, not an IT matter, and it requires the business to take responsibility and drive improvements," said Andreas Bitterer, research vice president at Gartner. "Appointing data quality stewards help organisations achieve data quality improvement goals. Such individuals should be considered subject-matter experts for their departments and act as trustees of data, rather than owners of it. They will ensure that quality is maintained to make the data support business processes."  “数据质量是企业的问题，非IT的问题。 他需要企业去承担责任及驱使其提高质量。” Andreas Bitterer （Gartner Research Vice President）说。”任命数据管家帮助企业完成数据质量提高的目标。 某些企业也可以考虑他们的部门选择专业的专家进行数据的托管，胜于数据的所有者自己处理。他们可以保证维护的数据能够更好的支持商业流程。”

Each major business function have data stewards, including sales, marketing, service, production, finance, HR and IT. "Successful and effective data stewards reside in the business, are visible, respected and influential - they must have the vision to understand the importance of data quality to the overall business objectives, as well as the impact of quality issues on downstream business processes," said Bitterer. 每个主要的商业序列都需要数据管家，包括销售，市场，服务，生产，财务，人力资源和IT等序列。“成功和有效的企业数据管家， 是显著的，受尊敬的并有影响力的 - 他们必须理解数据质量在整个完成商业目标中的重要性，并且要清楚质量问题在商业流程中影响。” Bitterer 说。

"Many data stewardship programmes have resulted in little or no improvement because the organisation selected the wrong individuals as stewards or because those individuals were not organised and managed in a way that ensures success."  “很多数据管理工作成果很小或没有任何提升，或是因为组织选择了不合适的个人，或是这些个体在确保成功之路上没有被组织和管理。”

Gartner cites the example of a marketing specialist from the company’s marketing department acting as the data steward in the data quality improvement programme by keeping marketing data complete, correct, consistent, honest and not redundant. In this role, they would have responsibility for ensuring marketing-relevant information adheres to the corporate data quality standards.    Gartner引用一个列子， 来自企业市场部门的市场专员在数据质量提升程序中，担当数据管家保证市场数据的完整性，准确性，唯一性，真实性和不冗余。在这个规则中，他们具有保证市场相关信息紧跟公司数据质量标准的职责。

Successful stewards will be placed closest to the point of data capture and maintenance, are intimately knowledgeable about the data and its use in a business context. They have also a stake in improving quality. As such, they are empowered to make business process changes and apply resources to address quality issues. Furthermore, they can influence how their peers execute business processes to achieve further improvements.  成功的管家将紧随数据获取和维护等关键点， 具有相密切的数据知识并运用在整个商业中。他们同样有提高数据质量的秘诀。同样的，他们被授权去进行商业流程的变革以及应用资源去标识质量问题。此外， 他们可以影响流程的执行者去完成未来的改进。

According to Gartner, the governance duties of stewards are to: 基于Gartner的结果，数据管家的管辖区域为：

• Ensure the consistency and accuracy of data as it flows from one application to the next.  确保数据的一致和准确性从一个应用到下一个应用。

• Implement governance tasks and achieve data quality metrics pertaining to the accuracy and completeness of information in their domain. 执行管理任务，完成自己区域内的数据质量矩阵的准确性和完整性 。

• Be responsible for the elements that support data sharing and master data management objectives (such as official product hierarchies, valuation models, customer segmentation profiles and preferred suppliers).  支持数据共享和主数据管理目标的职责。（例如职位产品层次，评价模型，客户细分轮廓和首选厂商）

• Support ongoing profiling activities and identify issues with source systems (such as calculation routines and missing values).支持正在进行的轮廓活动和标识源系统的问题（例如计算程序和遗漏值）

• Create or update document taxonomies and actively participate in the semantic reconciliation of data models. 创建和更新文档分类和活跃共享， 在数据的语义调和中。