DAMA_DMBOK_数据管理知识体系3.0

## 一、什么是DAMA数据管理DAMA数据管理（Data Management Association）是一种用于提高数据管理效率的系统。

它是一种数据管理技术，它涵盖了数据库管理、数据仓库管理、数据挖掘、数据模型管理等多个方面。

它的目的是通过统一的数据管理模型，提高数据管理水平，提高数据管理效率，实现数据管理的有效性和可靠性。

## 二、DAMA数据管理的主要内容1、数据库管理：数据库管理是DAMA数据管理的核心内容，它包括数据库设计、数据库实施、数据库维护、数据库优化等内容。

它的目的是使数据库可靠、可用、可控，以满足企业的业务需求。

2、数据仓库管理：数据仓库管理是DAMA数据管理的重要组成部分，它涉及数据仓库的设计、数据仓库的实施、数据仓库的维护等内容。

它的目的是使数据仓库能够有效地支持企业的业务，以提高企业的数据管理效率。

3、数据挖掘：数据挖掘是DAMA数据管理的重要组成部分，它涉及数据挖掘技术的应用，以及数据挖掘的结果的分析和应用。

它的目的是通过数据挖掘技术，从数据中挖掘出有价值的信息，以满足企业的业务需求。

4、数据模型管理：数据模型管理是DAMA数据管理的重要组成部分，它涉及数据模型的设计、数据模型的实施、数据模型的维护等内容。

它的目的是使数据模型能够更好地满足企业的业务需求，以提高企业的数据管理效率。

## 三、DAMA数据管理的应用DAMA数据管理的应用可以提高企业的数据管理效率，有效提升企业的经济效益。

1、提高企业的数据管理效率：DAMA数据管理技术可以提高企业的数据管理效率，使企业能够更好地管理数据，从而提高企业的经济效益。

2、提高企业的决策质量：DAMA数据管理技术可以提高企业的决策质量，使企业能够基于有效的数据，做出更加准确的决策，从而提高企业的经济效益。

3、提高企业的运营效率：DAMA数据管理技术可以提高企业的运营效率，使企业能够更好地管理数据，从而提高企业的经济效益。

## 四、DAMA数据管理的未来发展随着社会经济的发展，企业对数据管理的要求也越来越高，DAMA数据管理也将发挥更大的作用。

dama数据管理知识体系数据管理是个关键性的元素，对于任何公司或机构而言，如何管理他们的数据是一个重要的问题。

随着科技的发展，数据已经成为我们社会的一种基石，我们依赖它来支持和促进我们的运作。

因此，管理数据的方式和标准也可以被作为一个重要的项目来参考。

DAMA数据管理知识体系是一种针对专业数据管理人员的专用知识体系，旨在帮助它们更好地掌握有关数据学习、数据应用等环节的知识。

这种体系是由国际数据管理协会成立的，自1993年以来，DAMA 知识体系已由许多认证的DAMA数据管理专家维护和扩展。

DAMA知识体系的一大特点是它把数据管理和数据应用分开。

它将数据管理拆分成多个子领域，每个子领域都由一组四个属性：数据管理的规范化、数据的质量管理、数据的存储和检索、以及数据的治理。

它还包括数据库技术、数据仓库技术和数据挖掘，以及基于云和分布式计算技术的数据应用等方面的知识。

此外，DAMA知识体系还涵盖了关于数据管理的一些最佳实践，包括数据策略和架构设计、数据分析方法、数据库设计、数据安全和隐私保护、以及组织内部的数据流程管理。

它还有一部分是关于业务数据和结构化数据之间的关系的，以及如何实现两者的有效整合。

DAMA识体系目前已经支持了多种方式的数据管理，包括传统的文件系统数据管理和数据库管理，以及新兴的大数据技术和云计算技术，比如Hadoop和谷歌云技术等，使得DAMA知识体系可以适应当前和未来的技术发展。

总的来说，DAMA数据管理知识体系是一个适用于数据管理人员的宝贵资源，它不仅能帮助专业人员更好地掌握数据管理的技术，而且也能为数据管理的未来发展提供参考和建议。

这样一个系统性的知识体系有助于数据管理人员更有效地收集、处理和分析数据，并为企业和机构找到更好的数据管理解决方案。

dama 数据管理知识体系指南数据管理是一项重要的工作，涉及到数据的采集、存储、处理、分析、应用等多个环节。

为了更好地进行数据管理，需要掌握一定的数据管理知识体系。

本文将介绍 dama 数据管理知识体系指南，帮助读者了解数据管理的基本概念、流程、工具和技术等方面的知识。

一、数据管理基本概念1. 数据：指记录事实、事项或概念的符号化描述，是信息的物理表现形式。

2. 数据管理：是指对数据进行规划、组织、存储、处理、维护、使用和评价的过程，以实现数据的有效管理和利用。

3. 数据库：是指按照一定的数据模型组织、存储和管理数据的系统。

4. 数据仓库：是指将不同的数据源集成到一个统一的数据存储库中，以支持企业决策和分析等应用需求。

5. 数据挖掘：是指从大量数据中发现有用的信息和知识的过程。

6. 数据治理：是指对数据进行规范、管理和控制的过程，以确保数据的质量和合规性。

二、数据管理流程1. 数据采集：是指从不同的数据源获取数据，并进行初步的清洗和处理。

2. 数据存储：是指将数据存储到数据库或数据仓库中，并进行数据建模和设计。

3. 数据处理：是指对数据进行加工、转换、清洗、整合等处理，以满足数据分析和应用的需求。

4. 数据分析：是指对数据进行统计分析、数据挖掘、机器学习等方法，以发现数据背后的规律和趋势。

5. 数据应用：是指将数据应用到具体的业务场景中，以支持决策、优化业务流程等应用需求。

6. 数据维护：是指对数据进行监控、维护和修复，以确保数据的质量和安全性。

三、数据管理工具1. 数据库管理系统（DBMS）：是用于管理和操作数据库的软件系统，常见的有 Oracle、MySQL、SQL Server 等。

2. 数据仓库工具：是用于构建和管理数据仓库的软件系统，常见的有 Teradata、IBM InfoSphere 等。

3. 数据可视化工具：是用于将数据可视化展示的软件系统，常见的有 Tableau、QlikView、Power BI 等。

DAMA数据管理知识体系指南打印版一、序言1. 数据管理的重要性在当今信息化社会中，数据管理扮演着极为重要的角色。

随着大数据、人工智能等技术的发展，数据量呈指数级增长，对数据管理的需求也日益增强。

良好的数据管理能够提高数据的质量、安全性和可用性，对企业的决策和运营起到至关重要的作用。

2. DAMA数据管理知识体系指南DAMA国际（Data Management Association）是一个致力于推动数据管理相关知识和标准的国际组织，其制定的数据管理知识体系指南被广泛应用于企业数据管理实践中。

为了方便广大数据管理从业者学习和参考，DAMA数据管理知识体系指南的打印版应运而生。

二、全面介绍数据管理知识1. 数据管理概述数据管理的定义、范围和重要性。

2. 数据管理的方法与工具数据管理的基本方法、常用工具和技术。

3. 数据管理的组织与策略数据管理的组织架构、策略规划和治理模式。

4. 数据架构与建模数据架构设计原则、建模方法和工具。

5. 数据质量与安全数据质量控制、安全保障和合规管理。

三、实用指南与案例分析1. 数据管理实践指南数据管理实践的流程、方法和技巧。

2. 数据管理案例分析实际案例分析，总结成功的数据管理实践经验。

3. 数据管理工具推荐介绍适用于数据管理的工具和软件，包括数据建模工具、数据质量检测工具、数据安全工具等。

四、附录与参考资料1. 数据管理相关标准国内外数据管理相关标准的介绍和比较。

2. 数据管理常用工具手册数据管理常用工具的使用手册和操作指南。

3. 数据管理学习资源数据管理相关的学习全球信息站、书籍和期刊推荐。

五、结语DAMA数据管理知识体系指南的出版，将为从业者提供一本系统、权威、实用的数据管理参考书籍。

希望广大数据管理从业者能够通过学习和实践，不断提升自身的数据管理能力，为企业的发展贡献力量。

以上是一份DAMA数据管理知识体系指南打印版的大致内容提纲，希望能够对您有所帮助。

感谢您的关注与支持。

数据治理方面的标准数据治理是指通过在组织内建立数据的管理、保护和控制策略，确保数据的质量、可靠性、一致性和可用性，以支持组织的战略目标和业务需求。

在现代信息化的背景下，数据治理已经成为各个企业和机构重要的管理领域之一。

为了实施数据治理，一些标准和规范已经被提出并得到广泛应用。

下面将介绍一些常见的数据治理标准。

1. DAMA-DMBOK：DAMA-DMBOK是数据管理协会（Data Management Association）制定的一套数据管理知识体系，它包含了数据治理的相关概念、原则、方法和技术。

DAMA-DMBOK定义了数据治理的核心要素，包括数据策略、数据架构、数据质量、数据集成、数据安全等，并提供了相应的最佳实践。

2. COBIT：COBIT（Control Objectives for Information and Related Technology）是一个由国际信息系统审计和控制协会（ISACA）开发的框架，用于管理和监控企业的信息技术。

COBIT包含了一系列的控制目标和实施指南，可应用于数据治理中的风险评估、合规性管理等方面。

3. ISO 8000：ISO 8000是国际标准化组织（ISO）为数据质量管理制定的一项国际标准。

ISO 8000定义了数据质量的要求和度量方法，包括数据完整性、一致性、准确性等方面，帮助组织建立和维护高质量的数据资产。

4. GDPR：GDPR（General Data Protection Regulation）是欧洲联盟于2018年实施的一项数据保护法规。

GDPR规定了个人数据的合法收集、处理和保护要求，对数据主体的权利给予了更加严格的保护。

对于组织来说，遵守GDPR标准是数据治理的关键一环，需要建立相应的数据安全管理措施和流程。

5. ITIL：ITIL（Information Technology Infrastructure Library）是一个由英国政府制定的一套IT服务管理最佳实践框架。

第12章元数据管理-DAMA-DMBOK：数据管理知识体系（第⼆版）第⼗⼆章元数据管理1.简介元数据的最常见定义，“关于数据的数据”，很容易引起误解。

可以归类为元数据的信息种类繁多。

元数据包括有关技术和业务流程，数据规则和约束以及逻辑和物理数据结构的信息。

它描述了数据本⾝（例如，数据库，数据元素，数据模型），数据表⽰的概念（例如，业务流程，应⽤程序系统，软件代码，技术基础结构）以及数据和概念之间的连接（关系）。

元数据可帮助组织了解其数据，系统和⼯作流程。

它可以进⾏数据质量评估，并且是数据库和其他应⽤程序管理的组成部分。

它有助于处理，维护，集成，保护，审核和管理其他数据。

要了解元数据在数据管理中的重要作⽤，请想象⼀个⼤型图书馆，其中有成千上万的书籍和杂志，但没有卡⽚⽬录。

没有卡⽚⽬录，读者甚⾄可能不知道如何开始寻找特定的书甚⾄特定的主题。

卡⽚⽬录不仅提供必要的信息（图书馆拥有的书籍和材料以及在何处被搁置），还使读者可以使⽤不同的起点（主题区域，作者或标题）来查找材料。

没有⽬录，很难甚⾄不可能找到⼀本书。

没有元数据的组织就像没有卡⽚⽬录的图书馆。

元数据对于数据管理和数据使⽤都是必不可少的（请参阅DAMA-DMBOK中对元数据的多个引⽤）。

所有⼤型组织都会产⽣和使⽤⼤量数据。

在整个组织中，不同的个⼈将具有不同级别的数据知识，但是没有⼀个⼈会了解有关数据的所有知识。

此信息必须记录在案，否则组织可能会失去有关⾃⾝的宝贵知识。

元数据提供了捕获和管理有关数据的组织知识的主要⽅法。

但是，元数据管理不仅是知识管理⽅⾯的挑战，⽽且还存在许多挑战。

这也是风险管理的必要。

元数据对于确保组织可以识别私有数据或敏感数据以及为⾃⼰的利益管理数据⽣命周期以及满⾜合规性要求并使风险最⼩化是必不可少的。

没有可靠的元数据，组织将不知道它拥有什么数据，数据代表什么，它起源于何处，它如何在系统中移动，谁可以访问它，或者对⾼质量数据意味着什么。

dama数据管理知识体系指南(原书第2版)Data Management（数据管理）是企业决策和业务运营中能够改善运行效率、产出质量和标准一致性的一个重要工具，在近几年来也受到了越来越多企业的关注。

DAMA数据管理知识体系指南（原书第2版）定义了DAMA-DMBOK（数据管理知识体系指南），为读者提供了一条完整、有序的路线图，以帮助读者掌握数据管理的基本知识和实践技能。

一、数据管理的基础知识：1.数据概念：定义什么是数据，以及数据科学家研究的数据概念。

2.数据模型：研究数据模型，包括：关系数据模型、实体模型、结构化文本模型等等。

3.数据库：研究数据库，包括使用的关系管理系统和文档管理系统，以及指定的数据库之间的操作方法。

4.数据算法：研究常用的数据算法，包括：排序、聚类、搜索、回归分析等等。

二、数据管理的实践技能：1.数据收集和数据处理：讲解如何从各种不同数据源收集、处理数据，使之符合数据库和数据模型。

2.数据构建和数据迁移：讲解如何构建数据库，以及如何从旧系统迁移到新系统。

3.数据分析：数据分析的有效性取决于算法的正确使用，这章讲解了使用正确算法的步骤。

4.数据可视化：讲解数据可视化的基本概念，以及使用工具和技术进行数据可视化的步骤。

三、数据管理的安全和技术：1.数据安全：介绍如何制定数据安全政策，控制访问权限，保护数据不被滥用和泄露。

2.数据标准与质量：探究如何确保数据的准确性、及时性，满足企业标准。

3.数据技术：介绍数据管理领域常用技术，包括有关正则表达式、XML、Big Data等等。

四、数据管理的治理方法：1.数据治理：介绍数据治理的概念，以及如何使用数据治理来改善数据可视化和数据质量。

2.数据策略：介绍如何在企业设计、推行数据策略，以及充分利用进行数据管理中意外发现的数据。

3.数据生态和社区协作：告诉读者如何构建数据生态，培育数据管理社区协作，以及提升数据价值。

以上就是DAMA数据管理知识体系指南（原书第2版）的内容概要，希望能帮助读者深入了解数据管理的基本知识和实践技能，并且能从数据管理的安全和技术，以及数据管理的治理方法中，获得更多的数据洞见，从而推动企业的发展。

DAMADMBOK数据管理知识体系DAMA-DMBOK 数据管理知识体系职能框架版本：V3.0.2目录1.简介 (3)1.1.数据管理专业 (3)1.2.数据管理知识体系（DMBOK） (4)1.3.DAMA数据管理辞典 (5)1.4.为什么需要此职能框架？ (5)1.5.为什么会有2.0版? (5)1.6.为什么会有 3.0版? (7)2.概述 (8)2.1.数据管理职能 (8)2.2.环境元素 (11)3.DAMA-DMBOK职能纲要 (13)文档简介本文档针对DAMA数据管理知识体系（ DMBOK）职能框架的第3.0.2版进行描述，该框架是由DAMA国际提供的，用于协助对本专业的最佳实践方法进行规范化的工作。

Deborah HendersonDAMA国际教育服务副主席DAMA基金会主席Mark MosleyDMBOK编辑修订历史版本日期作者描述1.0 2006.3.27 Mark Mosley 由芝加哥分会给DMBOK 委员会提交的建议书原始草稿。

1.1 2006.4.17 Mark Mosley 改写后的草稿，作为DMBOK委员会提供给DAMA国际/基金会的建议版本。

1.2 2006.5.3 Mark Mosley 2006年丹佛会议由DAMA 国际使用后进行修改。

1.3 2006.6.12 Deborah部分内容修订。

Henderson2.0 2007.4.5 Mark Mosley 部分内容修订，以反映2007年马萨诸塞州波士顿DAMA国际会议上所做的部分修改。

2.1 2007.11.5 Mark Mosley 部分修订，反映对DMBOK术语的使用。

3.0 2008.5.5 Mark Mosley 对第9章和第10章进行结构调整。

3.0.1 2008.6.25 Mark Mosley 修改为详细的活动纲要。

3.0.2 2008.9.10 Mark Mosley 对DW/BI管理的活动纲要进行微调。

DAMA-DMBOK Functional FrameworkVersion 3.02Mark MosleySeptember 10, 2008Table of ContentsTable of Contents (1)About This Document (1)Revision History (1)1. Introduction (2)1.1. The Data Management Profession (2)1.2. The DAMA DMBOK Guide (3)1.3. The DAMA Dictionary of Data Management (4)1.4. Why a Framework? (5)1.5. Why Version 2? (5)1.6. Why Version 3? (6)2. Overview (8)2.1. Data Management Functions (9)2.2. Environmental Elements (11)3. DAMA-DMBOK Functional Outline (15)About This DocumentThis document describes Version 3.02 of the DAMA-DMBOK Functional Framework, provided by DAMA International to help formalize the best practices of our profession. Deborah HendersonVP Education Services, DAMA InternationalPresident, DAMA FoundationMark MosleyDMBOK EditorRevision HistoryVersion Date Author Description1.0 March 27, 2006 Mark Mosley The original draft of a proposal by the Chicagochapter to the DMBOK committee.1.1 April 17, 2006 Mark Mosley Reworded draft to serve as a proposal from theDMBOK committee to DAMA I/F.1.2 May 3, 2006 Mark Mosley Revision after DAMA International adoption at the2006 Symposium in Denver, CO1.3 June 12, 2006 DeborahHendersonMiscellaneous revisions2.0 April 4, 2007 Mark Mosley Revisions reflecting changes made after DAMAInternational 2007 Symposium in Boston, MA2.1 November 5, 2007 Mark Mosley Revisions reflecting use of the term DMBOK3.0 May 5, 2008 Mark Mosley Restructured functions – from 9 to 103.0.1 June 25,2008 Mark Mosley Changes to detailed activities outline3.0.2 September10,2008 Mark Mosley Minor corrections and revision to the activity outlinefor DW/BI Management1. Introduction1.1. The Data Management ProfessionIn the Information Age, the Data Management function is vital to every organization. Whether known as Data Management, Data Resource Management or Enterprise Information Management, organizations increasingly recognize that the data they possess is a valuable resource. Like any valuable asset, they also recognize their data assets must be managed. Businesses, governments and other organizations are more effective when they use their data assets more effectively. The data management function seeks to effectively control and leverage data assets.Data management is a shared responsibility between the business data stewards serving as trustees of enterprise data assets and technical data stewards serving as the expert custodians and curators for these assets. Governance of the data management function coordinates this collaboration between IT and the enterprise.Within IT, Data Management is an emerging profession. Data management concepts and supporting technology have evolved quickly over the last thirty years.Creating a formal, certified, recognized and respected data management profession is not an easy task. The current environment is a confusing combination of terms, methods, tools, opinion and hype. To mature the data management profession, we need professional standards:•Standard terms and definitions•Standard functions, processes and practices•Standard roles and responsibilities•Standard deliverables and metricsThese standards and best practices will help data management professionals perform more effectively. Moreover, they will also help us communicate with our teammates, managers and executives. Executives in particular need to fully understand and value data management so they can fully support, fund and staff the data management function. DAMA, the Data Management Association, is the world’s premiere professional organization for data management professionals. DAMA International and the DAMA International Foundation are developing standards for the profession.1.2. The DAMA DMBOK GuideDAMA International is developing a new book, The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide). A DAMA-DMBOK Editorial Board has been formed as a working committee to guide development of the DAMA-DMBOK Guide and related publications, including The DAMA Dictionary of Data Management. The Editorial Board includes participation from local DAMA chapter members. DAMA members have volunteered to contribute and review drafts.The entire body of knowledge about data management is quite large and constantly growing. The DAMA-DMBOK Guide will provide a definitive introduction to data management. It will present a standard industry view of data management functions, terminology and best practices, without detailing specific methods and techniques. The DAMA-DMBOK Guide will not attempt to be a complete authority on any specific data management function, but will point readers to widely recognized publications, articles and websites for further reading. The DAMA DMBOK Guide will introduce valid alternative views and industry accepted approaches where clear differences of opinion exist.The goals of the DAMA-DMBOK Guide are:1.To build consensus for a generally applicable view of data management functions.2.To provide standard definitions for commonly used data management functions,deliverables, roles and other terminology.3.To identify guiding principles for data management.4.To overview commonly accepted good practices, widely adopted methods andtechniques, and significant alternative approaches, without reference to specifictechnology vendors or their products.5.To briefly identify common organizational and cultural issues.6.To clarify the scope and boundaries of data management.7.To guide readers to additional resources for further understanding.Audiences for the DAMA-DMBOK Guide include:•Certified and aspiring data management professionals.•Other IT professionals working with data management professionals.•Business data stewards at all levels.•Executives with an interest in managing data as an enterprise asset.•Knowledge workers developing an appreciation of data as an enterprise asset. •Consultants conducting assessments of client data management functions and helping to implement and improve data management at these clients.•Educators responsible for developing and delivering a data management curriculum. •Researchers in the field of data management.DAMA foresees several potential uses of the DAMA-DMBOK Guide, including: •Informing a diverse audience about the nature and importance of data management. •Helping build consensus within the data management community.•Helping data stewards and data professionals understand their responsibilities. •Provide the basis for assessments of data management effectiveness and maturity. •Guiding efforts to implement and improve data management functions.•Pointing readers to additional sources of knowledge about data management. •Guiding the development and delivery of data management curriculum content for higher education.•Suggesting areas of further research in the field of data management.•Helping data management professionals prepare for Certified Data Management Professional (CDMP) exams.•Assist organizations in their enterprise data strategyThe reference models for the DAMA-DMBOK Guide are the Project Management Body of Knowledge (PMBOK) 1 document published by the Project Management Institute, and the Software Engineering Body of Knowledge (SWEBOK)2 document published by the IEEE.DAMA believes the DAMA-DMBOK Guide will be well received by managers, executives and the higher education community DAMA expects to publish the DAMA-DMBOK Guide in 2009.1.3. The DAMA Dictionary of Data ManagementThe DAMA Dictionary of Data Management is a companion volume to the DAMA-DMBOK Guide, available for purchase as a CD-ROM. Originally developed as an extensive Glossary for the DAMA-DMBOK Guide, DAMA is publishing it separately due to its size and business value. Definitions for terms found in the Dictionary are consistent with their usage in the DAMA-DMBOK Guide.1 Trademark of the Project Management Institute2 Trademark of the IEEE1.4. Why a Framework?The DAMA-DMBOK Functional Framework described here exists to:•Provide a cohesive structure for organizing the Data Management Body of Knowledge (DAMA-DMBOK Guide) document.•Define standard terms and definitions for data management processes, roles and deliverables cited consistently throughout the DAMA-DMBOK Guide.•Guide assessments of an organization’s data management function and data strategy, and suggest and guide initiatives to implement and improve datamanagement.1.5. Why Version 2?Version 1.0 of the DAMA-DMBOK Functional Framework was approved by the DAMA Executive Board in April 2006, and posted on the website in July 2006. Over 3000 people from around the world have since downloaded the Framework paper. During that time, changes have been made to the framework in the course of developing the DAMA-DMBOK Guide. In Version 2, the following data management functions have been renamed:•Data Stewardship & Governance has been renamed to simply Data Governance, in recognition of data stewardship activities and responsibilities across all ninefunctions•Data Architecture & Design has been renamed to Data Architecture, Analysis & Design, to more fully describe the data modeling and specification activities.•Database Administration has been renamed to Database Management, to more clearly separate the function from the database administrator role, given otherroles participate in the Database Management function.•Data Quality Improvement has been renamed to Data Quality Management, reflecting the broader scope of the activities within the function.•Data Warehousing & Business Intelligence has been renamed to Data Warehousing & Business Intelligence Management, to more clearly indicate thefunction includes the development and support activities that enable businessintelligence and does not include the business intelligence activities performed by knowledge workers.•Unstructured Data Management has been renamed to Document, Record & Content Management, in recognition that data residing outside of databases maystill exist within some degree of structure.•Metadata Management has been renamed to Meta Data Management, in respect to trademark restrictions.The presentation sequence of these nine functions has been changed as well:•Data Quality Management was moved before Reference & Master Data Management, recognizing Reference & Master Data Management as a specialized form of Data Quality Management.•Reference & Master Data Management was moved before Data Warehousing & Business Intelligence Management, recognizing its focus on data integration foroperational databases which then provide the sources for data warehousing.•Meta Data Management was moved to the ninth position, reflecting its foundational role in providing the infrastructure for other data managementfunctions.The following environmental elements have also been renamed:•Goals & Objectives has been renamed to Goals & Principles, recognizing1)The DAMA-DMBOK Guide will offer directional goals for eachfunction but not specific objectives. Objectives reflect the currentmeasurable targets of a particular organization.2)Principles have been included with Goals, moving up from Practices toan earlier presentation position•Process & Activities has been renamed to just Activities, recognizing that functions and activities are two types of processes.•Principles & Practices has been renamed to Practices & Techniques, reflecting the grouping of Principles with Goals.•Organizational & Cultural Issues has been renamed to Organization & Culture.In addition, more detailed activities, deliverables and roles have been identified. These details should prove useful to managers, data stewards and data management professionals while the DAMA-DMBOK Guide is under development.Finally, much of the longer introduction to the first version of the paper has been removed. While appropriate in the paper’s original form as a proposal to the DAMA Executive Board, the longer introduction is no longer necessary.1.6. Why Version 3?Over the 12 months since April 2007, contributors and early reviewers to the DAMA-DMBOK Guide have raised additional issues.1.The scope of the Data Architecture, Analysis and Design function was very large,and its corresponding chapter in the DAMA-DMBOK Guide was also becomingvery large. In fact, Data Planning and Architecture was considered a separatefunction from Data Development. Data modeling techniques were used in bothfunctions.2. Data Development as a function was split artificially between Data Architecture,Analysis and Design and the Database Implementation activities within Database Management. It was unclear where the linkage was to the software developmentlifecycle (SDLC).3.Data Development (implementation) activities were seen as a separate functionfrom other production operational database management activities.4.Documents & Records Management was seen as a separate function fromDocument Content Management, more parallel to (production) DatabaseOperations Management activities in its support across the data lifecycle (fromacquisition to purge) for unstructured data stores. Document Content Management by itself was more focused on enabling user access, in parallel with BusinessIntelligence Management.5.The term Database Management was still being interpreted by many earlyreviewers to be a synonym for Database Administration, implying the full scopeof responsibilities of Database Administrators.6.Presentation of the Data Quality Management function seemed to fit better as theconcluding function since it monitored the quality of all data, including meta data, reference and master data.These issues became more obvious during the editing of contributor text on these topics. Version 3 resolves these issues by restructuring the DAMA-DMBOK Functional Framework from 9 functions to 10 functions:• A new Data Development function has been defined, removing the SDLC project delivery activities from the old Data Architecture, Analysis and Design functionand the old Database Management function.•The remaining activities from the old Data Architecture, Analysis and Design function have been renamed as Data Architecture Management.•The remaining production operational planning, control and support activities from the old Database Management function have been renamed as DatabaseOperations Management.• A new Documents & Records Management function has been defined, removing related activities from the scope of the old Documents, Records and ContentManagement function. The Documents & Records Management function includes responsibility for paper and electronic files across the data lifecycle, fromacquisition to purging, in parallel with Database Operations Management.•The name and the scope of the old Document, Records and Content Management function have now been simplified to just Document Content Management.•Data Quality Management is now presented as the concluding function.2. OverviewThe DAMA-DMBOK Functional Framework Version 3 identifies 10 major Data Management Functions, each described through 7 Environmental Elements. The matrix below graphically presents DAMA-DMBOK Functional Framework:Figure 1. The DAMA-DMBOK Functional Framework, Version 3The matrix is just a useful way to picture the entire framework.Each function will be addressed by a chapter in the DAMA-DMBOK Guide, and each of these chapters will discuss each of the seven elements. The extent of each discussion will vary by chapter, as appropriate to the issues involved. Each chapter will follow a consistent structure, including:•A brief Introduction to the function, including definitions of key terms, acontext diagram for the function, and a list of the business goals of the function.•A description of Concepts and Activities, including associated deliverables,responsible roles and organizations, best practices, common procedures andtechniques and supporting technology. In some chapters there is a separateConcepts and Activities section for each sub-function, and these sections arenamed for each sub-function.•A Summary including a list restating guiding principles, a table recapping theactivities, deliverables and responsibilities of the function, and a briefdiscussion of organizational and cultural issues•A selective list of books and articles suggested as Recommended Reading.2.1. Data Management FunctionsThe 10 Data Management Functions are:•Data Governance – planning, supervision and control over data management and use •Data Architecture Management – as an integral part of the enterprise architecture •Data Development – analysis, design, building, testing, deployment and maintenance •Database Operations Management – support for structured physical data assets •Data Security Management – ensuring privacy, confidentiality and appropriate access •Reference & Master Data Management – managing golden versions and replicas •Data Warehousing & Business Intelligence Management – enabling access to decision support data for reporting and analysis•Document & Content Management – storing, protecting, indexing and enabling access to data found in unstructured sources (electronic files and physical records) •Meta Data Management – integrating, controlling and delivering meta data•Data Quality Management – defining, monitoring and improving data qualityFigure 2. The 10 Data Management FunctionsThe diagram above presents the 10 functions alone, implying a sequence of presentation, beginning in the center, then to the 12:00 position and then moving around the circle clockwise from the 12:00 position to the 11:00 position. The diagram below gives some general sense of the scope of each function.Figure 3. Data Management Functions – Scope SummarySee Section 3 for detailed lists of the activities performed within each function.Each function is briefly defined below:•Data Governance – The exercise of authority, control and shared decision-making (planning, monitoring and enforcement) over the management of data assets. Data Governance is high-level planning and control over data management.•Data Architecture Management – The development and maintenance of enterprise data architecture, within the context of all enterprise architecture, and itsconnection with the application system solutions and projects that implemententerprise architecture.•Data Development – The data-focused activities within the system development lifecycle (SDLC), including data modeling and data requirements analysis,design, implementation and maintenance of databases data-related solutioncomponents.•Database Operations Management – Planning, control and support for structured data assets across the data lifecycle, from creation and acquisition througharchival and purge.•Data Security Management – Planning, implementation and control activities to ensure privacy and confidentiality and to prevent unauthorized and inappropriate data access, creation or change.•Reference & Master Data Management – Planning, implementation and control activities to ensure consistency of contextual data values with a “golden version”of these data values.•Data Warehousing & Business Intelligence Management – Planning, implementation and control processes to provide decision support data andsupport knowledge workers engaged in reporting, query and analysis.•Document & Content Management – Planning, implementation and control activities to store, protect and access data found within electronic files andphysical records (including text, graphics, image, audio, video) •Meta Data Management – Planning, implementation and control activities to enable easy access to high quality, integrated meta data.•Data Quality Management – Planning, implementation and control activities that apply quality management techniques to measure, assess, improve and ensure the fitness of data for use.Each of these functions is decomposed below into activities. In a few cases, activities are grouped into sub-functions. While functions and sub-functions are named with noun phrases, activities are named with verb phrases.2.2. Environmental ElementsThe 7 Environmental Elements provide a logical and consistent way to describe each function. The elements provide a structure for:•Consistent presentation in each DAMA-DMBOK Guide chapter.•Organizing assessment questions, findings and recommendations.•Guiding strategic planning for each function.The idea of Environmental Elements is not a new one. A commonly referenced structure identifies three elements: Process, Technology and People. We believe there is too much to cover under the category of Process (Processes, Deliverables, Principles, Methods & Techniques) and People (Roles & Responsibilities, Organizational & Cultural Issues).The Framework identifies the following 7 elements:Figure 4. The 7 Environmental ElementsThe diagram above presents the 7 elements, implying a sequence of presentation, beginning in the center and then moving clockwise around the circle from the 1:00 position to the 12:00 position. The diagram below gives a general sense of the scope of each Environmental Element.Figure 5. Environmental Elements – Scope SummaryOur structure provides for lists of basic elements (Goals & Principles, Activities, Deliverables, Roles & Responsibilities,) before addressing and elaborating on less structured topics (Practices and Procedures, Technology, Organization & Culture).Figure 6. Basic and Supporting Environmental ElementsThe basic Environmental Elements are:•Goals & Principles – The directional business goals of each function and the fundamental principles that guide performance of each function.•Activities – Each function is further decomposed into lower level activities. Some activities are grouped into sub-functions. Activities can be further decomposedinto tasks and steps.•Deliverables – The information and physical databases and documents created as interim and final outputs of each function. Some are considered essential, someare generally recommended, and others are optional depending on circumstances.•Roles and Responsibilities – The business and IT roles involved in performing and supervising the function and the specific responsibilities of each role in thatfunction. Many roles will participate in multiple functions.The supporting Environmental Elements are:•Practices & Procedures -- Common and popular methods and techniques used to perform the processes and produce the deliverables. May also include commonconventions, best practice recommendations and alternative approaches without elaboration.•Technology -- Categories of supporting technology (primarily software tools), standards and protocols, product selection criteria and common learning curves.In accordance with DAMA policies, specific vendors or products should not be mentioned.•Organization and Culture -- These issues might include:o Management Metrics – measures of size, effort, time, cost, quality, effectiveness, productivity, success and business valueo Critical Success Factorso Reporting Structureso Contracting Strategieso Budgeting and Related Resource Allocation Issueso Teamwork and Group Dynamicso Authority & Empowermento Shared Values & Beliefso Expectations & Attitudeso Personal Style & Preference Differenceso Cultural Rites, Rituals and Symbolso Organizational Heritageo Change Management Recommendations3. DAMA-DMBOK Functional OutlineThere are 10 functions and 102 activities. Where there are more than 12 activities within a function, activities have been grouped under two or more sub-functions. Three functions have sub-functions, with 8 sub-functions overall (2, 4 and 2). Functions and sub-functions are named with noun phrases, while activities are named with verb phrases.Each activity is categorized as belonging to one of four Activity Groups:•Planning Activities (P) Activities that set the strategic and tactical coursemanagement activities. Planning fordataotherperformed on a recurring basis.beactivitiesmay•Control Activities (C) Supervisory activities performed on an on-goingbasis.•Development Activities (D) Activities undertaken within projects andrecognized as part of the systems developmentcreating data deliverables through(SDLC),lifecycleanalysis, design, building, testing and deployment. •Operational Activities (O) Service and support activities performed on anbasis.on-goingThe outline below is subject to change as each chapter is drafted, but we expect changes to be relatively minor adjustments at this point.1.Data Governance1.1.Data Management Planning1.1.1.Identify Strategic Enterprise Data Needs (P)1.1.2.Develop & Maintain the Data Strategy (P)1.1.3.Establish the Data Management Professional Organizations (P)1.1.4.Identify & Appoint Data Stewards (P)1.1.5.Establish Data Governance & Stewardship Organizations (P)1.1.6.Develop, Review & Approve Data Policies, Standards and Procedures (P)1.1.7.Review & Approve Data Architecture (P)1.1.8.Plan and Sponsor Data Management Projects & Services (P)1.1.9.Estimate Data Asset Value & Associated Data Management Costs (P)1.2.Data Management Supervision & Control1.2.1.Supervise the Data Management Professional Staff & Organizations (C)1.2.2.Coordinate Data Governance Activities (C)1.2.3.Manage & Resolve Data Related Issues (C)1.2.4.Monitor & Ensure Regulatory Compliance (C)1.2.5.Monitor Conformance with Data Policies, Standards and Architecture (C)1.2.6.Oversee Data Management Projects & Services (C)municate & Promote the Value of Data Assets (C)2.Data Architecture Management2.1.Develop & Maintain the Enterprise Data Model (P)2.2.Analyze & Align With Other Business Models (P)2.3.Define & Maintain the Data Technology Architecture (P) (same as 4.2.2)2.4.Define & Maintain the Data Integration Architecture (P) (same as 6.2)2.5.Define & Maintain the DW / BI Architecture (P) (same as 7.1.2)2.6.Define & Maintain Enterprise Taxonomies (P) (same as 8.2)2.7.Define & Maintain the Meta Data Architecture (P) (same as 9.2)3.Data Development3.1.Data Modeling, Analysis & Design3.1.1.Analyze Information Requirements (D)3.1.2.Develop & Maintain Conceptual Data Models (D)3.1.3.Develop & Maintain Logical Data Models (D)3.1.4.Develop & Maintain Physical Data Models (D)3.2.Detailed Data Design3.2.1.Design Physical Databases (D)3.2.2.Design Related Data Structures (D)3.2.3.Design Information Products (D)3.2.4.Design Data Access Services (D)3.3.Data Model & Design Quality Management3.3.1.Develop Data Modeling & Database Design Standards (P)3.3.2.Review Data Model & Database Design Quality (C)3.3.3.Manage Data Model Versioning and Integration (C)3.4.Data Implementation3.4.1.Create & Maintain Development & Test Databases (D)3.4.2.Create & Maintain Test Data (D)3.4.3.Migrate & Convert Data3.4.4.Build & Test Information Products ((D)3.4.5.Build & Test Data Access Services (D)3.4.6.Build & Test Data Integration Services (D)3.4.7.Validate Information Requirements (D)3.4.8.Prepare for Data Deployment (D)4.Database Operations Management4.1.Database Support4.1.1.Implement & Maintain Database Environments (C)4.1.2.Implement & Control Database Changes (C)4.1.3.Acquire Externally Sourced Data (O)4.1.4.Plan for Data Recovery (P)4.1.5.Backup & Recover Data (O)4.1.6.Set Database Performance Service Levels (P)4.1.7.Monitor & Tune Database Performance (O)4.1.8.Plan for Data Retention (P)4.1.9.Archive, Retrieve and Purge Data (O)4.1.10.Manage Specialized Databases (O)。

dama数据管理知识体系指南模型评估概述及解释说明1. 引言1.1 概述在当今信息爆炸的时代，数据管理的重要性日益凸显。

无论是企业还是个人，都面临着海量数据的处理和管理任务。

为了高效地利用这些数据资源，建立一个系统化的数据管理知识体系变得至关重要。

本文旨在介绍数据管理知识体系指南，并重点探讨其中的模型评估部分。

1.2 文章结构本文共分为5个部分，除引言外，主要内容包括：第二部分介绍了数据管理知识体系的概念、重要性以及基本原则；第三部分概述了模型评估的定义、步骤和常用指标；第四部分详细解释了不同模型评估方法，包括基于交叉验证、基于训练集和测试集划分以及基于留一法；最后一部分对全文进行总结，并回顾展望了数据管理知识体系指南和模型评估的未来发展方向。

1.3 目的本文旨在通过对数据管理知识体系指南中模型评估部分的深入探讨，帮助读者更好地理解并应用相关概念与方法。

通过学习本文内容，读者将能够掌握模型评估的基本概念、步骤和常用指标，并了解不同的评估方法及其适用场景。

此外，文章还将对数据管理知识体系指南和模型评估进行回顾与展望，帮助读者把握未来数据管理发展的趋势。

以上是“1. 引言”部分的内容，希望对您撰写长文有所帮助。

如需继续撰写其他部分，请告知。

2. 数据管理知识体系指南：2.1 什么是数据管理：数据管理是指对组织或个人的数据资产进行有效管理和维护的一系列活动和策略。

它涉及到数据的收集、存储、处理、传输和使用等方面，旨在提高数据的质量、可靠性和安全性，以支持业务决策和运营需求。

2.2 数据管理的重要性：数据管理对于任何组织或个人来说都非常重要。

首先，良好的数据管理可以确保数据的准确性和完整性，避免了因为脏数据而导致的错误决策或行动。

其次，有效的数据管理可以提升工作效率和生产力，通过合理规划、整理和分类数据，使得信息可以更快地被获取到并用于各种目的。

此外，合规性与监管要求也是必须满足的内容。

最后，有效的数据管理还能帮助组织发现潜在机会，并帮助进行战略规划。