DAMA/CDGA/CDGP认证之DMBOK2数据管理2200知识点（思维导图）
2021-10-14 19:06:00
1.21MB
DAMA CDGA CDGP(数据治理工程师/专家)考试认证资料汇总。
01 DAMA数据管理知识体系指南（1.0）
02 DAMA数据管理知识体系指南（2.0）
03 DAMA数据管理知识体系指南思维导图
2021 CDGA和CDGP认证考试热点问题解答
2021 DAMA数据管理专业认证CDMP介绍图文版
2021《CDGA数据治理工程师》和《CDGP数据治理专家》考试模块和内容大纲
2021-09-26 20:04:53
354.1MB
《计算机科学与技术学科前沿丛书•计算机科学与技术学科研究生系列教材:DAMA数据管理知识体系指南(中文版)》是关于数据管理知识体系的专业指南，是欧美数据管理从业者的必备书，是中国广大信息技术和数据管理从业人士有益的知识指南和工作参考书，同时可作为计算机、工商管理，信息管理等专业的研究生教材。通过阅读《计算机科学与技术学科前沿丛书•计算机科学与技术学科研究生系列教材:DAMA数据管理知识体系指南(中文版)》，数据管理从业者将有效提升数据管理知识和技能，此书亦是DAMA International推出的数据管理专业人士认证（CDMP）考试的培训及备考必备书目。
2021-09-13 09:22:37
45.24MB
1
DMOBOK是DAMA国际编著的一本有关数据管理的专注，比较权威
Dedicated to the memory ofPatricia Cupoli, MLS, MBA, CCP, CDMP(May25,1948-July28,2015for her lifelong commitment to the Data Management professionand her contributions to this publicationPublished byTechnics oQ Publications2 Lindsley roadBasking ridge, NJ 07920 USAhilps://www.technicspub.comSenior editorDeborah Henderson. CDMPEditor:Susan earley, CDMPProduction editorLaura Sebastian-Coleman, CDMP, IQCPBibliography researcherElena Sykora, DGSPCollaboration tool managerEva Smith cDMPCover design by Lorena MolinariAll rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronicor mechanical, including photocopying, recording or by any information storage and retrieval system, withoutwritten permission from the publisher, except for the inclusion of brief quotations in a review.The author and publisher have taken care in the preparation of this book, but make no expressed or impliedwarranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental orconsequential damages in connection with or arising out of the use of the information or programs contained hereinAll trade and product names are trademarks, registered trademarks or service marks of their respective companiesand are the property of their respective holders and should be treated as suchSecond editionFirst Printing 2017Copyright O 2017 DAMA InternationalISBN. Print ed9781634622349ISBN. PDF ed9781634622363IsBN. Server ed9781634622486iSBN, Enterprise ed9781634622479Library of congress control number2017941854ContentsPreface15Chapter 1: Data Management171. Introduction172. Essential Concepts182.1 Data2.2 Data and information202.3 Data as an Organizational asset202.4 Data Management principles212.5 Data Management Challenges232.6 Data Management Strategy3. Data Management frameworks333.1 Strategic Alignment Model3.2 The amsterdam Information model343,3 The DAMA-DmboK framework3.4 DMBOK Pyramid (Aiken)393.5 DAMA Data Management Framework evolved404, DAMA and the dobok435. Works Cited/ Recommended46Chapter 2: Data Handling ethics491 Introduction492. Business drivers3. Essential Concepts523.1 Ethical Principles for data3.2 Principles Behind Data Privacy Law3.3 Online data in an ethical context563.4 Risks of Unethical Data Handling Practices563. 5 Establishing an ethical data culture603.6 Data ethics and governance644. Works Cited/Recommended65Chapter 3 Data governance1. Introduction671.1 Business drivers701.2 Goals and Principles1.3 Essential Concepts722 Activities792.1 Define Data Governance for the organization792.2 Perform readiness assessment2. 3 Perform discovery and business alignment2.4 Develop Organizational Touch Points812.5 Develop Data Governance Strategy822.6 Define the DG Operating Framework822.7 Develop Goals, Principles, and policies832. 8 Underwrite Data Management projects842.9 Engage Change management852· DMBOK22.10 Engage in Issue Management862.11 Assess Regulatory Compliance Requirements872.12 Implement Data Governance882.13 Sponsor data Standards and procedures882. 14 Develop a Business Glossary902. 15 Coordinate with Architecture Groups902.16 Sponsor Data Asset valuation912.17 Embed data governance913. Tools and Techniques923.1 Online Presence/Websites923.2 Business Glossary923. 3 Workflow tools3. 4 Document Management Tools933.5 Data governance scorecards934. Implementation Guidelines934. 1 Organization and culture934.2 Adjustment and communication945. Metrics6. Works cited/ recommended95Chapter 4: Data Architecture971 Introduction971.1 Business drivers991.2 Data Architecture Outcomes and practices1001.3 Essential Concepts1012. Activities1092.1 Establish data architecture practice1102.2 Integrate with Enterprise Architecture1153. Tools1153.1 Data Modeling Tools1153.2 Asset Management Software1153.3 Graphical Design Applications4. Techniques1164. 1 Lifecycle projectionsl164.2 Diagramming Clarity1165 Implementation guidelines1175.1 Readiness assessment/ Risk Assessment1185.2 Organization and Cultural change1196. Data architecture governance1196.1 Metrics1207. Works Cited/ Recommended120Chapter 5: Data Modeling and Design1231 Introduction1231.1 Business drivers1251.2 Goals and Principles1251.3 Essential Concepts1262 Activities1522.1 Plan for Data modeling152CONTENTS·32,2 Build the data model1532.3 Review the data models1582,4 Maintain the data models1593. Tools3.1 Data Modeling tools1593.2 Lineage tools1593.3 Data Profiling Tools1603.4 Metadata Repositories1603.5 Data model patterns1603.6 Industry data models1604, Best practices16l4.1 Best Practices in Naming Conventions1614. 2 Best Practices in Database design16l5, Data model governance1625.1 Data Model and Design Quality Management1625.2 Data Modeling metrics1646. Works Cited/ Recommended166Chapter 6: Data Storage and operations1691. Introduction1691.1 Business drivers1711.2 Goals and Principles1711.3 Essential Concepts1722. Activities1932.1 Manage Database Technology1942.2 Manage databases1963. Tools2093. 1 Data Modeling tools2093.2 Database Monitoring tools2093.3 Database Management Tools2093.4 Developer Support Tools2094. Techniques2104. 1 Test in lower environments2104.2 Physical Naming Standards2104.3 Script Usage for All Changes2105 Implementation guidelines2105.1 Readiness Assessment/Risk Assessment2105.2 Organization and Cultural Change2116. Data Storage and operations governance6.1 Metric2126.2 Information Asset Tracking2136.3 Data Audits and data validation2137. Works Cited/Recommended214Chapter 7: Data Security2171. Introduction2171. 1 Business drivers1.2 Goals and Principles2221.3 Essential Concepts2234· DMBOK22. Activities2452.1 Identify Data Security Requirements2452.2 Define Data Security Policy2472.3 Define Data Security standards2483. Tools2563.1 Anti-Virus Software/ Security Software2563.2 Https2563.3 Identity Management Technology2573.4 Intrusion detection and prevention software2573.5 Firewalls(Prevention2573.6 Metadata Tracking2573.7 Data Masking/Encryption2584. Techniques2584.1 CRUD Matrix Usage2584.2 Immediate Security Patch Deployment2584.3 Data security attributes in metadata2584.4 Metrics2594.5 Security Needs in Project requirements2614.6 Efficient Search of Encrypted Data2624.7 Document sanitization625 Implementation guidelines5.1 Readiness Assessment/Risk Assessment2625.2 Organization and cultural change2635.3 Visibility into User Data Entitlement2635. 4 Data Security in an Outsourced World2645.5 Data security in Cloud environments2656. Data Security governance2656.1 Data Security and Enterprise architecture2657. Works Cited/Recommended266chapter 8: Data Integration and Interoperability2691. Introduction2691.1 Business drivers2701.2 Goals and principles2721.3 Essential Concepts2732. Data Integration Activities2862. 1 Plan and analyze2862.2 Design Data Integration Solutions2892.3 Develop data Integration Solutions2912.4 Implement and monitor2933. Tools2943.1 Data Transformation Engine/EtL Tool2943.2 Data virtualization server2943.3 Enterprise Service Bus2943.4 Business rules engine2953.5 Data and Process Modeling tools2953.6 Data Profiling tool2953.7 Metadata Repository2964. Techniques296CONTENTS·55 Implementation Guidelines2965.1 Readiness assessment/ Risk Assessment2965.2 Organization and Cultural change2976, Dll Governance2976.1 Data Sharing agreements2986.2 Dll and data lineage2986.3 Data Integration Metrics2997. Works Cited/recommended299Chapter 9: Document and Content Management3031 Introduction3031.1 Business drivers3051.2 Goals and principles3051.3 Essential Concepts3072. Activities3232. 1 Plan for lifecycle management3232.2 Manage the lifecycle3262. 3 Publish and deliver content3293. Tools3301 Enterprise Content Management Systems3302 Collaboration tools3333.3 Controlled vocabulary and metadata tools3333. 4 Standard markup and exchange formats3333.5 E-discovery Technology3364. Techniques3364. 1 Litigation response playbook3364. 2 Litigation Response Data Map3375 Implementation Guidelines3375.1 Readiness Assessment/ Risk Assessment3385.2 Organization and Cultural Change3396. Documents and content governance3406.1 Information governance frameworks3406.2 Proliferation of information3426.3 Govern for Quality Content3426.4 Metrics3437. Works Cited/ recommended344Chapter 10: Reference and Master Data3471 Introduction3471. 1 Business drivers3491.2 Goals and principles3491.3 Essential Concepts3502. Activities702.1 MDM ACtivities3712.2 Reference data activities3733. Tools and Techniques3754. Implementation guidelines3754.1 Adhere to master data architecture3764.2 Monitor data movement3766· DMBOK24.3 Manage Reference Data Change3764.4 Data Sharing Agreements3775. Organization and cultural change3786, Reference and master data governance3786.1 Metrics3797. Works Cited/Recommended379Chapter 11: Data Warehousing and Business Intelligence3811 Introduction3811.1 Business drivers3831.2 Goals and Principles3831.3 Essential Concepts3842. Activities3942.1 Understand requirements3942.2 Define and Maintain the DW/BI Architecture3952.3 Develop the data Warehouse and Data marts3962.4 Populate the data Warehouse3972.5 Implement the Business Intelligence Portfolio3982.6 Maintain data products3993. Tools4023.1 Metadata repository4023.2 Data Integration Tools4033. 3 Business Intelligence Tools Types4034. Techniques4074.1 Prototypes to Drive Requirements4074.2 Self-Service bi4084.3 Audit Data that can be queried4085 Implementation Guidelines4085.1 Readiness Assessment/Risk Assessment4085.2 Release Roadmap4095.3 Configuration Management4095.4 Organization and Cultural Change4106. DW/BI Governance4116.1 Enabling business acceptance4116.2 Customer/User Satisfaction4126. 3 Service Level Agreements416. 4 Reporting strategy6.5 Metrics442237. Works cited/ recommended414Chapter 12: Metadata Management4171 Introduction4171.1 Business drivers4201.2 Goals and Principles4201. 3 Essential Concepts422. Activities4342. 1 Define metadata Strategy4342.2 Understand Metadata Requirements4352.3 Define metadata architecture436
Dedicated to the memory ofPatricia Cupoli, MLS, MBA, CCP, CDMP(May25,1948-July28,2015for her lifelong commitment to the Data Management professionand her contributions to this publicationPublished byTechnics oQ Publications2 Lindsley roadBasking ridge, NJ 07920 USAhilps://www.technicspub.comSenior editorDeborah Henderson. CDMPEditor:Susan earley, CDMPProduction editorLaura Sebastian-Coleman, CDMP, IQCPBibliography researcherElena Sykora, DGSPCollaboration tool managerEva Smith cDMPCover design by Lorena MolinariAll rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronicor mechanical, including photocopying, recording or by any information storage and retrieval system, withoutwritten permission from the publisher, except for the inclusion of brief quotations in a review.The author and publisher have taken care in the preparation of this book, but make no expressed or impliedwarranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental orconsequential damages in connection with or arising out of the use of the information or programs contained hereinAll trade and product names are trademarks, registered trademarks or service marks of their respective companiesand are the property of their respective holders and should be treated as suchSecond editionFirst Printing 2017Copyright O 2017 DAMA InternationalISBN. Print ed9781634622349ISBN. PDF ed9781634622363IsBN. Server ed9781634622486iSBN, Enterprise ed9781634622479Library of congress control number2017941854ContentsPreface15Chapter 1: Data Management171. Introduction172. Essential Concepts182.1 Data2.2 Data and information202.3 Data as an Organizational asset202.4 Data Management principles212.5 Data Management Challenges232.6 Data Management Strategy3. Data Management frameworks333.1 Strategic Alignment Model3.2 The amsterdam Information model343,3 The DAMA-DmboK framework3.4 DMBOK Pyramid (Aiken)393.5 DAMA Data Management Framework evolved404, DAMA and the dobok435. Works Cited/ Recommended46Chapter 2: Data Handling ethics491 Introduction492. Business drivers3. Essential Concepts523.1 Ethical Principles for data3.2 Principles Behind Data Privacy Law3.3 Online data in an ethical context563.4 Risks of Unethical Data Handling Practices563. 5 Establishing an ethical data culture603.6 Data ethics and governance644. Works Cited/Recommended65Chapter 3 Data governance1. Introduction671.1 Business drivers701.2 Goals and Principles1.3 Essential Concepts722 Activities792.1 Define Data Governance for the organization792.2 Perform readiness assessment2. 3 Perform discovery and business alignment2.4 Develop Organizational Touch Points812.5 Develop Data Governance Strategy822.6 Define the DG Operating Framework822.7 Develop Goals, Principles, and policies832. 8 Underwrite Data Management projects842.9 Engage Change management852· DMBOK22.10 Engage in Issue Management862.11 Assess Regulatory Compliance Requirements872.12 Implement Data Governance882.13 Sponsor data Standards and procedures882. 14 Develop a Business Glossary902. 15 Coordinate with Architecture Groups902.16 Sponsor Data Asset valuation912.17 Embed data governance913. Tools and Techniques923.1 Online Presence/Websites923.2 Business Glossary923. 3 Workflow tools3. 4 Document Management Tools933.5 Data governance scorecards934. Implementation Guidelines934. 1 Organization and culture934.2 Adjustment and communication945. Metrics6. Works cited/ recommended95Chapter 4: Data Architecture971 Introduction971.1 Business drivers991.2 Data Architecture Outcomes and practices1001.3 Essential Concepts1012. Activities1092.1 Establish data architecture practice1102.2 Integrate with Enterprise Architecture1153. Tools1153.1 Data Modeling Tools1153.2 Asset Management Software1153.3 Graphical Design Applications4. Techniques1164. 1 Lifecycle projectionsl164.2 Diagramming Clarity1165 Implementation guidelines1175.1 Readiness assessment/ Risk Assessment1185.2 Organization and Cultural change1196. Data architecture governance1196.1 Metrics1207. Works Cited/ Recommended120Chapter 5: Data Modeling and Design1231 Introduction1231.1 Business drivers1251.2 Goals and Principles1251.3 Essential Concepts1262 Activities1522.1 Plan for Data modeling152CONTENTS·32,2 Build the data model1532.3 Review the data models1582,4 Maintain the data models1593. Tools3.1 Data Modeling tools1593.2 Lineage tools1593.3 Data Profiling Tools1603.4 Metadata Repositories1603.5 Data model patterns1603.6 Industry data models1604, Best practices16l4.1 Best Practices in Naming Conventions1614. 2 Best Practices in Database design16l5, Data model governance1625.1 Data Model and Design Quality Management1625.2 Data Modeling metrics1646. Works Cited/ Recommended166Chapter 6: Data Storage and operations1691. Introduction1691.1 Business drivers1711.2 Goals and Principles1711.3 Essential Concepts1722. Activities1932.1 Manage Database Technology1942.2 Manage databases1963. Tools2093. 1 Data Modeling tools2093.2 Database Monitoring tools2093.3 Database Management Tools2093.4 Developer Support Tools2094. Techniques2104. 1 Test in lower environments2104.2 Physical Naming Standards2104.3 Script Usage for All Changes2105 Implementation guidelines2105.1 Readiness Assessment/Risk Assessment2105.2 Organization and Cultural Change2116. Data Storage and operations governance6.1 Metric2126.2 Information Asset Tracking2136.3 Data Audits and data validation2137. Works Cited/Recommended214Chapter 7: Data Security2171. Introduction2171. 1 Business drivers1.2 Goals and Principles2221.3 Essential Concepts2234· DMBOK22. Activities2452.1 Identify Data Security Requirements2452.2 Define Data Security Policy2472.3 Define Data Security standards2483. Tools2563.1 Anti-Virus Software/ Security Software2563.2 Https2563.3 Identity Management Technology2573.4 Intrusion detection and prevention software2573.5 Firewalls(Prevention2573.6 Metadata Tracking2573.7 Data Masking/Encryption2584. Techniques2584.1 CRUD Matrix Usage2584.2 Immediate Security Patch Deployment2584.3 Data security attributes in metadata2584.4 Metrics2594.5 Security Needs in Project requirements2614.6 Efficient Search of Encrypted Data2624.7 Document sanitization625 Implementation guidelines5.1 Readiness Assessment/Risk Assessment2625.2 Organization and cultural change2635.3 Visibility into User Data Entitlement2635. 4 Data Security in an Outsourced World2645.5 Data security in Cloud environments2656. Data Security governance2656.1 Data Security and Enterprise architecture2657. Works Cited/Recommended266chapter 8: Data Integration and Interoperability2691. Introduction2691.1 Business drivers2701.2 Goals and principles2721.3 Essential Concepts2732. Data Integration Activities2862. 1 Plan and analyze2862.2 Design Data Integration Solutions2892.3 Develop data Integration Solutions2912.4 Implement and monitor2933. Tools2943.1 Data Transformation Engine/EtL Tool2943.2 Data virtualization server2943.3 Enterprise Service Bus2943.4 Business rules engine2953.5 Data and Process Modeling tools2953.6 Data Profiling tool2953.7 Metadata Repository2964. Techniques296CONTENTS·55 Implementation Guidelines2965.1 Readiness assessment/ Risk Assessment2965.2 Organization and Cultural change2976, Dll Governance2976.1 Data Sharing agreements2986.2 Dll and data lineage2986.3 Data Integration Metrics2997. Works Cited/recommended299Chapter 9: Document and Content Management3031 Introduction3031.1 Business drivers3051.2 Goals and principles3051.3 Essential Concepts3072. Activities3232. 1 Plan for lifecycle management3232.2 Manage the lifecycle3262. 3 Publish and deliver content3293. Tools3301 Enterprise Content Management Systems3302 Collaboration tools3333.3 Controlled vocabulary and metadata tools3333. 4 Standard markup and exchange formats3333.5 E-discovery Technology3364. Techniques3364. 1 Litigation response playbook3364. 2 Litigation Response Data Map3375 Implementation Guidelines3375.1 Readiness Assessment/ Risk Assessment3385.2 Organization and Cultural Change3396. Documents and content governance3406.1 Information governance frameworks3406.2 Proliferation of information3426.3 Govern for Quality Content3426.4 Metrics3437. Works Cited/ recommended344Chapter 10: Reference and Master Data3471 Introduction3471. 1 Business drivers3491.2 Goals and principles3491.3 Essential Concepts3502. Activities702.1 MDM ACtivities3712.2 Reference data activities3733. Tools and Techniques3754. Implementation guidelines3754.1 Adhere to master data architecture3764.2 Monitor data movement3766· DMBOK24.3 Manage Reference Data Change3764.4 Data Sharing Agreements3775. Organization and cultural change3786, Reference and master data governance3786.1 Metrics3797. Works Cited/Recommended379Chapter 11: Data Warehousing and Business Intelligence3811 Introduction3811.1 Business drivers3831.2 Goals and Principles3831.3 Essential Concepts3842. Activities3942.1 Understand requirements3942.2 Define and Maintain the DW/BI Architecture3952.3 Develop the data Warehouse and Data marts3962.4 Populate the data Warehouse3972.5 Implement the Business Intelligence Portfolio3982.6 Maintain data products3993. Tools4023.1 Metadata repository4023.2 Data Integration Tools4033. 3 Business Intelligence Tools Types4034. Techniques4074.1 Prototypes to Drive Requirements4074.2 Self-Service bi4084.3 Audit Data that can be queried4085 Implementation Guidelines4085.1 Readiness Assessment/Risk Assessment4085.2 Release Roadmap4095.3 Configuration Management4095.4 Organization and Cultural Change4106. DW/BI Governance4116.1 Enabling business acceptance4116.2 Customer/User Satisfaction4126. 3 Service Level Agreements416. 4 Reporting strategy6.5 Metrics442237. Works cited/ recommended414Chapter 12: Metadata Management4171 Introduction4171.1 Business drivers4201.2 Goals and Principles4201. 3 Essential Concepts422. Activities4342. 1 Define metadata Strategy4342.2 Understand Metadata Requirements4352.3 Define metadata architecture436
2021-06-08 10:22:15
10.78MB
1
运营商数据治理实践
数据资产管理
DAMA数据资产管理体系
数据治理的战略目标
数据治理架构
浙江移动的数据治理思路
建立IT域数据治理流程、标准和体系
DT域数据蓝图规划
DT域数据基线整理等
2021-05-23 11:02:02
1.91MB