Book Image

Data Stewardship in Action

By : Pui Shing Lee
Book Image

Data Stewardship in Action

By: Pui Shing Lee

Overview of this book

In 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.
Table of Contents (18 chapters)
Free Chapter
1
Part 1:Why Data Stewardship and Why Me?
5
Part 2:How to Become a Data Steward and Shine!
12
Part 3:What Makes Data Stewardship a Sustainable Success?

Understanding the challenges and limitations

While AI and GPT possess the transformative power to automate and streamline data governance processes, they are not the silver bullet that can instantly resolve all data-related issues. While promising, incorporating AI into data governance presents its own set of challenges and limitations. The primary obstacle lies in the sheer volume and complexity of enterprise data. There are some considerations when you deploy the AI solutions for data governance:

  • Fueling the right AI with the right data for data governance in a real-time manner: AI and GPT models require large amounts of high-quality data to learn from and generate outputs. However, the data are often scattered, siloed, incomplete, inconsistent, or outdated in many organizations. Data governance itself is a complex and dynamic process that involves multiple stakeholders, roles, processes, communications, metrics, and tools. There is a question mark to ensure that the right...