Data Governance : From the Fundamentals to Real Cases / / Ismael Caballero and Mario Piattini, editors

• Pubblicazione: Cham, Switzerland : , : Springer, , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (255 pages)
• Disciplina: 658.4038
• Soggetto topico: Knowledge management; Management information systems
• Persona (resp. second.): CaballeroIsmael; PiattiniMario
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- 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.
• Titolo autorizzato: Data Governance
• ISBN: 3-031-43773-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910799479403321
• Lo trovi qui: Univ. Federico II