Data Governance: From the Fundamentals to Real Cases

Foreword by Yang Lee
Foreword by Alberto Palomo
Preface
	Overview
	Organization
		Part I: Data Governance Fundamentals
		Part II: Data Governance Applied
	Target Readership
Acknowledgments
Contents
Contributors
List of Abbreviations
Part I: Data Governance Fundamentals
	Chapter 1: Introduction to Data Governance: A Bespoke Program Is Required for Success
		1.1 Chapter Overview
		1.2 Why Does Data Need to Be Governed?
			1.2.1 Long-Lasting Consequences of Poor Data Decisions?
			1.2.2 Mounting Data Debt
		1.3 Who Needs to Be Involved in DG?
		1.4 When Is It Appropriate for Organizations to Invest in DG?
		1.5 Where Should Organizations Get Started with DG?
		1.6 How Should Organizations Apportion Their DG Efforts Over Time?
			1.6.1 Data Debt´s Impact
			1.6.2 Proactive Versus Reactive DG
			1.6.3 MacGyver Abilities
		1.7 What Organizational Needs Does DG Fill?
			1.7.1 Improving the Ways That Data Is Treated as an Asset?
			1.7.2 Available but Not Widely Known Research Results
			1.7.3 Using Data to Better Support the Organizational Mission
			1.7.4 The Role of DG Frameworks
				1.7.4.1 Related Term Definitions
				1.7.4.2 A Small Concentrated Team Is Preferred Over Distributed (Dissipated) Knowledge
			1.7.5 Using Data Strategically
				1.7.5.1 Strategy Is About Why
				1.7.5.2 What Is Data Strategy?
				1.7.5.3 Working Together: Data and Organizational Strategy?
				1.7.5.4 Strategic Commitment: Program Versus Project Focus
				1.7.5.5 Digitizationing
				1.7.5.6 A Watchful Eye Toward the US Federal Government (FEPA)
			1.7.6 Breaking Through the Barriers of Data Governance
		1.8 Chapter Summary
	Chapter 2: Data Strategy and Policies: The Role of Data Governance in Data Ecosystems
		2.1 Introduction
		2.2 Data Strategy and Policies
			2.2.1 Data Strategy Fundamentals
			2.2.2 From Defensive to Offensive Data Strategy
			2.2.3 Data Policies
		2.3 New Development Trajectories for Data Governance
			2.3.1 Data as Strategic Asset for Organizations
			2.3.2 The Emergence of Data Ecosystems
		2.4 Widening the Scope of Data Governance Operations
			2.4.1 Consideration of Challenging External Influencing Factors
			2.4.2 Bridging the Intra-organizational Perspective on Data Governance with the Inter-organizational Perspective
		2.5 Utilizing Data Ecosystems as Part of Data Strategy
			2.5.1 The Role of Ecosystem Data Governance
			2.5.2 Inter-organizational Data Governance Modes
			2.5.3 Adequate Positioning for Engaging in Data Ecosystems
		2.6 Recommendations for Action
			2.6.1 Recommendations for Actions for Single Organizations
			2.6.2 Recommendations for Actions for Data Ecosystem Design
		References
	Chapter 3: Human Resources Management and Data Governance Roles: Executive Sponsor, Data Governors, and Data Stewards
		3.1 Introduction
		3.2 The Role of Human Resources in Data Governance
		3.3 Understanding the Structure of the Data Governance Organization
			3.3.1 Executive Steering Committee
			3.3.2 Data Governance Board
			3.3.3 Data Stewardship Council
			3.3.4 Data Governance Program Office (DGPO)
				3.3.4.1 Data Governance Program Office (DGPO) Responsibilities
				3.3.4.2 Data Governance Manager Responsibilities
				3.3.4.3 Enterprise Data Steward Responsibilities
		3.4 Key Roles and Responsibilities for Data Stewards
			3.4.1 Business Data Stewards
			3.4.2 Technical Data Stewards
			3.4.3 Operational Data Stewards
			3.4.4 Project Data Stewards
		3.5 Summary
	Chapter 4: Data Value and Monetizing Data
		4.1 Managing Data as an Actual Asset
			4.1.1 The Emergence of the Chief Data Officer
			4.1.2 Approaches to Data Asset Management
			4.1.3 Data´s Emergence as a Real Economic Asset
			4.1.4 The Need for Senior Executive Understanding
		4.2 Impediments to Maturity in Enterprise Data Management
			4.2.1 Leadership Issues
			4.2.2 IM Priorities Over Which You Have Control or Influence
			4.2.3 Resources Needed to Advance Data Management Capabilities
			4.2.4 Negative Cultural Attitudes About Data Management
			4.2.5 Overcoming the Barriers to Data Asset Management
			4.2.6 Moving Forward
		4.3 Generally Agreed-Upon Data Principles (GAIP)
		4.4 Data Supply Chains and Ecosystems
			4.4.1 Adapting the SCOR Model
			4.4.2 Metrics for the Data Supply Chain
		4.5 A New Model for the Data Supply Chain
		4.6 Data Ecosystems
			4.6.1 Data Within an Ecosystem
			4.6.2 Ecosystem Entities
			4.6.3 Ecosystem Features
			4.6.4 Ecosystem Processes
			4.6.5 Ecosystem Influences
			4.6.6 Ecosystem Management
		4.7 Applying Sustainability Concepts to Managing Data
		4.8 Data Management Standards
			4.8.1 Adapting IT Asset Management (ITAM) to Data Management
			4.8.2 Adapting ITIL to Data Management
			4.8.3 Adaptations from RIM and ECM
			4.8.4 Adaptations from Library Science
			4.8.5 Adaptations from Physical Asset Management
			4.8.6 Adaptations from Financial Management
	Chapter 5: Data Governance Methodologies: The CC CDQ Reference Model for Data and Analytics Governance
		5.1 Introduction
		5.2 Paradigm Shifts in Data Governance: From Control to Value Creation
			5.2.1 Data Governance: Definition and Mechanisms
			5.2.2 Data Governance 1.0: Focus on Control, Data Quality, and Regulatory Compliance
			5.2.3 Data Governance 2.0: Extending Beyond Control to Enable Value Creation
			5.2.4 Need for Guidelines Supporting Data and Analytics Governance
		5.3 The CC CDQ Reference Model for Data and Analytics Governance
			5.3.1 Data Governance as Key Theme in the Competence Center Corporate Data Quality
			5.3.2 Design Principles for Data and Analytics Governance
				5.3.2.1 Principle 1: Governance Linking Strategy to Operations
				5.3.2.2 Principle 2: Federated Data Governance Involving Data and Analytics, Business, and IT Experts
				5.3.2.3 Overview of the CC CDQ Reference Model for Data and Analytics Governance
		5.4 Step 1: Set the Scope for Data and Analytics Governance
			5.4.1 End-to-End Perspective for Defining Scope and Requirements
			5.4.2 Data and Analytics Products and Their Information Supply Chains
		5.5 Step 2: Who to Govern? - Processes, Roles, and Responsibilities
			5.5.1 Decision Areas (Processes)
			5.5.2 Data and Analytics Roles
				5.5.2.1 Data Management Roles and Responsibilities
				5.5.2.2 Analytics Roles and Responsibilities
				5.5.2.3 Organization-Wide Coordination of Data and Analytics
			5.5.3 Assigning Roles to Responsibilities
		5.6 Step 3: How to Govern? - Deriving the Operating Model
			5.6.1 Mapping Roles, Responsibilities, and Processes to the Organizational Context
				5.6.1.1 Typical Configurations
		5.7 Summary
		References
	Chapter 6: Data Governance Tools
		6.1 Introduction
		6.2 The Business Need for Data Governance and Its Importance
			6.2.1 Common Business Outcomes Led by Chief Data Officers
		6.3 Case Study: Southwest Airlines and the Role of Technology on Business Outcomes
			6.3.1 Data Challenges in the Transportation Industry
		6.4 Key Functionalities Needed in the Data Governance Tools
			6.4.1 Twelve Technology Features Chief Data Officers Can Use to Become Data-Driven
			6.4.2 Data Governance Technology Challenges
		6.5 Four Must-Have Technology Focus Areas to Kick-start Data Governance
			6.5.1 Flexible Operating Model
				6.5.1.1 Insurance Customer Story
			6.5.2 Identification of Data Domains
				6.5.2.1 Financial Services Customer Story
			6.5.3 Identification of Critical Data Elements (CDEs) Within Data Domains
				6.5.3.1 Federal Government Agency in Washington, D.C., Story
				6.5.3.2 Technology Company Story
			6.5.4 Enable Control Measurements
				6.5.4.1 Technology Company Out of California Story
		6.6 Conclusion
	Chapter 7: Maturity Models for Data Governance
		7.1 Introduction
		7.2 Maturity Models
			7.2.1 DAMA
			7.2.2 Aiken´s Model
			7.2.3 Data Management Maturity (DMM) Model
			7.2.4 IBM Model
			7.2.5 Gartner´s Enterprise Information Management Model
			7.2.6 DCAM
		7.3 MAMD (Alarcos´ Model for Data Maturity)
			7.3.1 ISO/IEC 33000 Standards Family
			7.3.2 MAMD Overview
			7.3.3 The Capability Dimension
			7.3.4 Process Dimension
			7.3.5 Organizational Maturity Model
		7.4 Practical Applications of MAMD
			7.4.1 Regional Government: Improving the Performance of Authentication Servers
			7.4.2 Insurance Company: Building a ``Source of Truth´´ Repository
			7.4.3 Bicycle Manufacturer: Enabling Better Analytics
			7.4.4 Telco Company: Building a Data Marketplace
			7.4.5 Hospital/Faculty of Medicine: Assessing the Organizational Maturity
			7.4.6 University Library: Assessing the Organizational Maturity
			7.4.7 DQIoT: Developing a MAMD-Based Maturity Model for IoT
			7.4.8 Regional Institute of Statistics: Developing a MAMD-Based Model for the Official Statistics Domain
			7.4.9 CODE.CLINIC: Tailoring MAMD for Coding Clinical Data
		References
Part II: Data Governance Applied
	Chapter 8: Data Governance in the Banking Sector
		8.1 Inception, Challenges, and Evolution
		8.2 Data-Driven Bank
		8.3 Data Stewardship
		8.4 Single Data Marketplace Ecosystem (SDM)
		8.5 DM&G Dashboard
			8.5.1 Overview
			8.5.2 Forecast
			8.5.3 Data Value
		8.6 Data as a Service (DaaS)
		8.7 The Magic Algorithm
	Chapter 9: Data Has the Power to Transform Society
		9.1 Introduction
		9.2 Federated Data Governance as a Pillar of Strategic Digital Autonomy
			9.2.1 From the Platform Model to the Ecosystem Model
			9.2.2 Features of Federated Data Ecosystems
			9.2.3 The Pillars of Federated Data Ecosystems
			9.2.4 Shared Common Infrastructure
		9.3 Data Governance in Public Administrations as a Guarantor of the Generation of Citizen Value
			9.3.1 Principle of Effective Data Governance
			9.3.2 Principle of Ethical Treatment of Data
			9.3.3 Principle of Reliable Data-Centric Processing
			9.3.4 Principle of Sovereign Sharing of Data
			9.3.5 Principle of Open Dissemination of Information
			9.3.6 Principle of Evidence-Based Public Policy Design and Analysis
			9.3.7 Data Culture Promotion Principle
		9.4 Conclusions
		References
	Chapter 10: Data Governance in the Insurance Industry
		10.1 The Insurance Industry and Its Main Features in Terms of Data Governance
		10.2 Heterogeneous Data Governance Strategies in the Insurance Industry
			10.2.1 Defensive vs. Offensive Strategy
			10.2.2 The Role of the CDO
			10.2.3 Centralized vs. Federated Model
			10.2.4 Data Strategy and Value Creation
		10.3 Insurance: A Regulated Sector
		10.4 Mature and Stable Companies
		10.5 High Data Usage with Data Culture in Progress
		10.6 Traditional Focus on Operational Excellence with a Vertical Approach
			10.6.1 Traditional Optimization Focus on Departmental Data
			10.6.2 Grade of Sophistication Dependent on Particular Data Promoters
			10.6.3 Asymmetries Among End Data Users
		10.7 The Insurance Companies´ Challenge of Attracting Talented People
		10.8 Insurance Trends and Their Impact on Data Governance
	Chapter 11: Data Governance in the Health Sector
		11.1 Importance and Implementation of Data Governance in Healthcare
		11.2 A Case Study of Portugal
			11.2.1 Clinical Coding and the Hospital Information Structure in Portugal
			11.2.2 CODE.CLINIC PRM
		11.3 Summary and Conclusions
		References
	Chapter 12: Data Governance in the Telco Sector
		12.1 Introduction
		12.2 How to Operate in General This Type of Company
		12.3 How Is the Data Collected, and What Can Be Done with All the Data Managed by This Type of Company?
		12.4 How Can You Govern the Data?
		12.5 Problems That Can Occur in the Interaction Between Technical Teams and Specific Disciplines Associated with Data Governan...
		12.6 Data Understanding
		12.7 Data Preparation
		12.8 Main Conclusions