Data Governance and Data Management: Contextualizing Data Governance Drivers, Technologies, and Tools

✍ Scribed by Rupa Mahanti

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 218
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data.  Considering the business impact of data, there needs to be adequate management around it to derive the best value. 

 

Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. 

 

This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives.

✦ Table of Contents

Foreword
Preface
Acknowledgments
About This Book
Contents
About the Author
Acronyms and Abbreviations
List of Figures
List of Tables
1 Introduction to Data, Data Governance, and Data Management
Abstract
1.1 Evolution of Data
1.2 Data and Its Governance
1.3 Data Governance and Data Management
1.4 Concluding Thoughts
Reference
2 Data and Its Governance
Abstract
2.1 The Data Deluge
2.2 About Data and the Organization of Data
2.3 Data as an Asset and Governance
2.3.1 Is Data an Asset?
2.4 Data Asset Life Cycle
2.5 Common Problems with Data Not Being Treated as an Asset
2.6 Classification of Data
2.6.1 Entities
2.6.1.1 Master Data
2.6.1.2 Transactional Data
2.6.1.3 Reference Data
2.6.1.4 Metadata
2.6.2 Varieties of Data
2.6.2.1 Structured Data
2.6.2.2 Unstructured Data
2.6.2.3 Semi-structured Data
2.6.3 Acquisition/Creation of Data
2.6.4 Data Domains or Data Subject Areas
2.6.5 Time
2.6.6 Uses of Data
2.6.7 Data Criticality Based on Integrity and Availability
2.6.7.1 Non-critical
2.6.7.2 Critical
2.6.7.3 Mission Critical
2.6.8 Location of Data
2.6.9 The Sensitivity of the Data and the Level of Protection the Data Requires
2.6.9.1 Restricted Data
2.6.9.2 Confidential Data
2.6.9.3 Private or Internal Data
2.6.9.4 Public Data
2.7 Data Quality and Data Quality Dimensions
2.7.1 Data Quality Dimensions
2.8 Need for Good Data Governance
2.9 Informal Versus Formal Data Governance
2.9.1 Warning Signs that Indicate, You Need Formal Data Governance
2.10 Data Governance is not the Same as Data Management or Data Quality
2.10.1 Data Governance and Data Management
2.10.2 Data Governance and Data Quality
2.11 Data Governance Goals
2.12 Data Governance—The Key Elements
2.12.1 People
2.12.2 Processes
2.12.3 Tools and Technology
2.13 Key Data Governance Business Drivers and Uses Cases
2.13.1 Compliance
2.13.2 Improving Customer Satisfaction
2.13.3 Reputation Management
2.13.4 Better Decision Making
2.13.5 Data Security and Privacy
2.13.6 Improving Data Quality
2.13.7 Analytics
2.13.8 Big Data
2.13.9 Revenue Growth
2.13.10 Improving Operational Efficiency
2.13.11 Mergers and Acquisitions
2.13.12 Partnering and Outsourcing
2.14 Key Benefits of Data Governance
2.14.1 Common Understanding of Data
2.14.2 Greater Collaboration
2.14.3 Improved Data Discovery
2.14.4 Increased Confidence in Data
2.14.5 Improved Brand Protection
2.14.6 Improved Decision Making
2.14.7 Competitive Advantage
2.14.8 Improved Data Management
2.14.9 Improved Risk Mitigation
2.14.10 Cost Savings
2.14.11 Support Impact Analysis
2.14.12 Business and IT Partnership
2.15 Concluding Thoughts
References
3 Data Governance and Data Management Functions and Initiatives
Abstract
3.1 Data Governance and Data Management
3.2 Data Management Functions and Initiatives
3.3 Data Architecture, Data Modeling, Design, and Data Governance
3.4 Data Governance, Data Integration, and Data Interoperability
3.4.1 Stakeholder Engagement and Management
3.4.2 Establish Governance Policies, Processes, and Best Practices
3.4.3 Metadata Management and Data Lineage
3.4.4 Security and Privacy
3.4.5 Data Sharing Agreements
3.4.6 Data Integration Metrics
3.5 Data Governance and Reference Data Management
3.5.1 What is Reference Data?
3.5.2 Reference Data Categories
3.5.2.1 Internal Reference Data
3.5.2.2 External Reference Data
3.5.3 Reference Data Governance
3.6 Data Governance and Master Data Management
3.6.1 Agreement and Management of Critical Master Data Elements
3.6.2 Defining and Enforcing Data Policies, Processes, Rules, and Standards
3.6.3 Roles, Responsibilities, and Accountabilities
3.6.4 Agreement on Metrics
3.6.5 Agreement on All Associated Reference Data
3.7 Data Governance, Data Warehousing, and Business Intelligence
3.8 Data Governance and Data Migration
3.9 Data Governance and Metadata Management
3.10 Data Governance, Document, and Content Management
3.10.1 Document Management
3.10.2 Content Management
3.10.3 Document Management System (DMS) Versus Content Management System (CMS)
3.11 Data Governance and Data Security Management
3.11.1 Define a Data Classification Policy
3.11.1.1 Restricted Data
3.11.1.2 Confidential Data
3.11.1.3 Private or Internal Data
3.11.1.4 Public Data
3.11.2 Discover Sensitive Data, Establish Data Ownership, and Data Stewardship
3.11.3 Classify Data
3.11.4 Use the Data Classification Results to Improve Security and Compliance
3.12 Data Governance, Data Storage, and Operations
3.13 Data Governance and Data Quality Management (DQM)
3.14 Big Data and Data Analytics
3.14.1 What is Big Data?
3.14.2 How is Big Data Different from Data or Traditional Data?
3.14.2.1 Volume
3.14.2.2 Velocity
3.14.2.3 Variety
3.14.3 Data Analytics
3.14.3.1 Descriptive Analytics
3.14.3.2 Diagnostic Analytics
3.14.3.3 Predictive Analytics
3.14.3.4 Prescriptive Analytics
3.15 Big Data, Analytics, Data Lake, and Data Governance
3.16 Concluding Thoughts
References
4 Data Governance Technology and Tools
Abstract
4.1 Data Governance and Technology
4.2 Data Governance Tools Versus Data Management Tools
4.3 Data Governance Elements That Can Be Supported By Tools
4.3.1 Managing Data Artifacts
4.3.2 Metadata Management
4.3.3 Governance Organizational Structure
4.3.4 Data Security and Privacy
4.3.5 Program Management and Workflow Management
4.3.6 Data Stewardship Activities
4.3.7 Business Alignment
4.3.8 Communication and Collaboration
4.3.9 Data Management Activities and Data Quality
4.3.9.1 Data Profiling
4.3.9.2 Data Cleansing
4.3.9.3 Data Monitoring
4.3.10 Master Data Management (MDM) and Reference Data Management
4.3.11 Data Governance Metrics
4.3.12 Data Policy Management
4.3.13 Data Issue Resolution
4.3.14 Managing Other Artifacts
4.4 Data Governance Tool Readiness, Selection, and Acquisition
4.5 Data Governance Tool Vendors
4.6 Conclusion and Final Thoughts
References
5 Data Governance and Data Management—Concluding Thoughts and Way Forward
Abstract
5.1 Data and Its Governance
5.2 Data Governance Stakeholders
5.3 Data Governance and Data Management
5.4 Data Governance—The Way Forward
References
Appendix A: Restricted Data
A.1 Payment Card Industry (PCI) Information
A.2 Protected Health Information (PHI)
A.3 Individually Identifiable Health Information (IIHI)
A.4 Electronic Protected Health Information (e-PHI)
A.5 Sensitive Personal Identifiable Information (PII)
A.6 Personal Data from GDPR Perspective
A.7 Personally Identifiable Education Records
Appendix B: Glossary of Terms
B.1 Asset
B.2 Confidential Data
B.3 Critical Data
B.4 Data
B.5 Databases
B.6 Data Classification
B.7 Data Criticality
B.8 Data Domain
B.9 Data Governance
B.10 Data Lake
B.11 Data Management (the Discipline)
B.12 Data Management (the Thing)
B.13 Database Management System (DBMS)
B.14 Data Profiling
B.15 Data Quality
B.16 Data Quality Dimensions
B.17 Database Schema
B.18 Dataset
B.19 Data Stewardship
B.20 Data Warehouse
B.21 Datamart
B.22 Dimension Modeling
B.23 Master Data
B.24 Metadata
B.25 Mission Critical Data
B.26 Non-critical Data
B.27 Normalization
B.28 Private or Internal Data
B.29 Public Data
B.30 Reference Data
B.31 Relational Database Management System (RDBMS)
B.32 Restricted Data
B.33 Semi-structured Data
B.34 Structured Data
B.35 Table
B.36 Transactional Data
B.37 Unstructured Data
Appendix C: Bibliography
Index