数据管理的组织方面

Wilfried Lemahieu, S. V. Broucke, B. Baesens
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摘要

在本章中,您将学习:•识别数据管理的基本概念;•了解目录、元数据、数据质量和数据治理的作用和重要性;•识别数据库建模和管理中的关键角色;•理解信息架构师、数据库设计师、数据所有者、数据管理员、数据库管理员和数据科学家之间的区别。Sober意识到其整个商业模式的成功依赖于数据,并且希望确保以最佳方式管理数据。该公司正在研究如何组织适当的数据管理,并想知道相应的职位概况。挑战是双重的。一方面,Sober希望拥有合适的数据管理团队,以确保最佳的数据质量。另一方面,索伯只有有限的预算来组建这支球队。在本章中,我们将深入探讨数据管理的组织方面。我们首先详细阐述数据管理,并回顾目录和元数据的重要作用,这两者都在第1章中介绍过。然后我们讨论元数据建模,它本质上遵循与我们在第3章中描述的类似的数据库设计过程。数据质量在其重要性和潜在维度方面也得到了广泛的讨论。接下来,我们将引入数据治理作为一种企业文化来保障数据质量。最后,我们回顾了数据建模和管理中的各种角色,如信息架构师、数据库设计师、数据所有者、数据管家、数据库管理员和数据科学家。数据管理数据管理需要对数据和相应的数据定义或元数据进行适当的管理。它旨在确保(元)数据具有良好的质量,从而成为有效和高效管理决策的关键资源。在接下来的小节中,我们首先回顾目录、元数据的角色及其建模。接下来是关于数据质量和数据治理的讨论。目录和元数据的作用良好的元数据管理的重要性不容低估。在过去,这一点经常被忽视,在需要更新或维护应用程序时导致严重的问题。在基于文件的数据管理方法中,元数据分别存储在每个应用程序中,从而产生了在第1章中讨论的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Organizational Aspects of Data Management
Chapter Objectives In this chapter, you will learn to: • identify the basic concepts of data management; • understand the role and importance of catalogs, metadata, data quality, and data governance; • identify key roles in database modeling and management; • understand the differences between an information architect, database designer, data owner, data steward, database administrator, and data scientist. Opening Scenario Sober realizes that the success of its entire business model depends on data, and it wants to make sure the data are managed in an optimal way. The company is looking at how to organize proper data management and wondering about the corresponding job profiles to hire. The challenge is twofold. On the one hand, Sober wants to have the right data management team to ensure optimal data quality. On the other hand, Sober only has a limited budget to build that team. In this chapter, we zoom into the organizational aspects of data management. We begin by elaborating on data management and review the essential role of catalogs and metadata, both of which were introduced in Chapter 1. We then discuss metadata modeling, which essentially follows a similar database design process to that which we described in Chapter 3. Data quality is also extensively covered in terms of both its importance and its underlying dimensions. Next, we introduce data governance as a corporate culture to safeguard data quality. We conclude by reviewing various roles in data modeling and management, such as information architect, database designer, data owner, data steward, database administrator, and data scientist. Data Management Data management entails the proper management of data and the corresponding data definitions or metadata. It aims at ensuring that (meta-)data are of good quality and thus a key resource for effective and efficient managerial decision-making. In the following subsections we first review catalogs, the role of metadata, and the modeling thereof. This is followed by a discussion on data quality and data governance. Catalogs and the Role of Metadata The importance of good metadata management cannot be understated. In the past this was often neglected, resulting in significant problems when applications needed to be updated or maintained. In the file-based approach to data management, the metadata were stored in each application separately, creating the issues discussed in Chapter 1.
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