Data Stewardship in Action: A roadmap to Data Value Realization and Measurable Business Outcomes

✍ Scribed by Pui Shing LEE

• Publisher: Packt
• Year: 2024
• Tongue: English
• Leaves: 272
• Edition: 1
• Category: Library

✦ Synopsis

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 enhance data stewardship

Follow a step-by-step program to develop a data operating model and implement data stewardship effectively

Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement—it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency. From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You'll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You'll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you'll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship. By the end of this book, you'll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learn

Enhance your job prospects by understanding the data stewardship field, roles, and responsibilities

Discover how to develop a data

✦ Table of Contents

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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