Data Stewardship in Action A Roadmap to Data Value Realization and Measurable Business Outcomes
Take your organization's data maturity to the next level by operationalizing data governance Key Features Develop the mindset and skills essential for successful data stewardship Apply practical advice and industry best practices, spanning data governance, quality management, and compliance, to...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009801533606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1: Why Data Stewardship and Why Me?
- Chapter 1: From Business Strategy to Data Strategy to Data Stewardship
- Understanding the strategic, tactical, and operational value of data stewardship
- Bridging the gap between data strategy and data operation
- Unlocking business value with data stewardship
- Understanding business strategy
- Understanding data strategy
- Operationalizing your data strategy via data stewardship
- Exploring the mindset and skillset gap
- Translating strategy into execution
- Data collection
- Data governance framework
- Analytics and reporting tools
- Tracking progress
- Decoding data governance, management, and stewardship
- Data stewards don different hats
- Summary
- Chapter 2: How Data Stewardship Can Help Your Organization
- Defining data stewardship
- The work scope of a data steward
- Understanding the role of a data steward
- Types of data stewards
- Comparing strategic data stewardship with a standard operating procedure
- Using business cases for storytelling and value realization
- Creating a competitive edge with data stewardship
- Summary
- Chapter 3: Getting Started with the Data Stewardship Program
- Defining the origin and destination of your data stewardship program
- Getting buy-in of data stewardship from stakeholders
- Building a prioritization matrix
- The impact-effort matrix
- The RICE method
- MoSCoW analysis
- Comparing different prioritization matrices
- Assessing data maturity
- Building the foundation of your data stewardship program
- Summary
- Part 2: How to Become a Data Steward and Shine!
- Chapter 4: Developing a Comprehensive Data Management Strategy
- What is a data strategy?.
- Assessing your current data environment for creating a data strategy - Where are you now?
- Data maturity assessment
- The CDMC framework
- Fulfilling the business and data strategy - Where do you want to go?
- Spider web diagram
- Introducing the people, process, and technology - How do we get there?
- Making the impact visible to your stakeholders - Feedback loop to measure and report progress
- Engagement model
- Summary
- Chapter 5: People, Process, and Technology
- Empowering people for an effective data stewardship program
- Roles and responsibilities
- Skills and training
- Stakeholder engagement
- Demonstrating the return on investment (ROI) of a data stewardship program
- Standardizing processes to ensure consistent data operation
- Data governance framework
- Data compliance and risk management
- Leveraging technology to fast-track your data journey
- Data infrastructure
- Data integration
- Data security and privacy technologies
- Investment strategy on technology for data stewardship
- Fostering the data culture
- Cultivating a data-driven culture
- Overcoming resistance
- Measuring cultural change
- Understanding the TOM - From strategy to operation
- Designing the TOM
- Implementing the TOM
- Evaluating and adjusting the TOM
- Summary
- Chapter 6: Establishing a Data Governance Organization
- Establishing data governance bodies
- Building your team
- Team structure and hierarchy
- Mode of data stewardship
- Data is a team sport
- Creating a data governance roadmap
- Creating short-term and long-term roadmaps
- Risk management and mitigation
- Defining KPIs
- Measuring and reporting on KPIs
- Using KPIs for continuous improvement
- Reviewing the fitness of your data stewardship mode
- Summary
- Chapter 7: Data Steward Roles and Responsibilities.
- Understanding high-level roles and responsibilities
- Day-to-day activities for data stewards
- RACI matrix for data governance
- Establishing data quality and lineage principles and practices
- Data quality
- The DQM cycle
- Data lineage
- Setting up data classification, access control, and security
- Data classification
- Data access control
- Data security and protection
- Monitoring and ensuring data privacy and compliance
- Summary
- Chapter 8: Effective Data Stewardship
- Establishing data stewardship principles and standardizing data incident management
- The data life cycle
- Principles and policies
- Data incident management
- Defining and implementing data ownership
- Defining and designing a data domain
- Assigning an owner to a data domain
- Day 2 for data ownership and domain
- Defining a target state - What does good data stewardship look like?
- Understanding the level of data complexity
- Defining a target state
- What does good look like for data owners?
- Summary
- Chapter 9: Supercharge Data Governance and Stewardship with GPT
- Pairing data and AI
- Leveraging AI and GPT for data governance
- Enhancing data quality and trust
- Automation and enrichment
- Driving innovation and insight
- Understanding the challenges and limitations
- Embracing a responsible AI framework
- Best practices for responsible AI in data governance
- Operationalizing a responsible AI Framework
- Future of AI for data governance
- Summary
- Part 3: What Makes Data Stewardship a Sustainable Success?
- Chapter 10: Data Stewardship Best Practices
- Rolling out a people-first operational model
- Aligning data mindset and continuous learning
- Creating a culture of accountability and ownership
- Executing day-to-day data processes
- Who does what, by when, and approved by whom?
- Data mapping and metadata management.
- Data risk assessment and mitigation
- Optimizing your data journey with strategic technological integration
- Data stewardship tools and technologies
- Data catalog and metadata
- Blockchain and AI
- Valuing and protecting data as an asset
- Realizing short- and long-term business value via data
- Summary
- Chapter 11: Theory versus Real Life
- Understanding why there is a gap between theory and reality
- Discovering the gaps
- Identifying the gaps
- Bridging the gap between theory and reality
- Critical thinking in bridging theory and reality
- Integrating data stewardship into daily operations
- Gap #1 - Standard operating procedure is written but not followed
- Gap #2 - Insufficient commitment from stakeholders
- Gap #3 - Data governance operating model cannot keep up with ever-changing regulatory requirements
- Gap #4 - Technical debt
- Future-proofing your data stewardship program
- Benchmarking with DAMA and EDMC surveys
- Enhancing data stewards' skills with skillset matrix
- Cultivating resilience and adaptability in data stewardship
- Evolving theoretical models with real-life experiences
- Summary
- Chapter 12: Case Studies
- Nurturing a data culture with a data mindset - case study #1
- A plan of action
- Data stewardship in action
- Outcome
- Streamlining fund performance and reporting - case study #2
- A plan of action
- Data stewardship in action
- Outcome
- Summary
- Index
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