May 1, 2024
Updated June 6, 2025
22 minute read
Navigating the Landscape of Data Integrity
Data integrity refers to the accuracy, consistency, completeness, and reliability of data throughout its entire lifecycle. Think of it as the trustworthiness of your information. In an increasingly data-driven world, organizations rely heavily on their data to make critical business decisions, understand market trends, and provide services. Therefore, ensuring that this data is sound is not just a technical requirement but a fundamental business imperative.
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Find a path to becoming a Data Integrity. Learn more at:
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Reading list
We've selected 27 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Integrity.
This comprehensive reference guide covering all aspects of data management, with dedicated sections on data governance and data quality. It provides a foundational understanding of data integrity within the broader data management landscape. is highly valuable as a reference tool for professionals and can serve as a textbook for advanced courses.
Is authored by the renowned data warehousing expert W.H. Inmon and provides a deep dive into the principles and best practices of data integrity.
Foundational text on data governance, which is essential for establishing policies and processes to ensure data integrity. It covers the design, implementation, and ongoing management of a data governance program. It's a must-read for anyone involved in establishing data governance frameworks.
This recent book addresses data quality from the perspective of data pipelines and data observability. It provides practical guidance and techniques for ensuring data quality in modern data architectures. is particularly relevant for data engineers and those working with large-scale data systems.
Combines data integrity and data governance, providing a comprehensive guide to ensuring the accuracy, consistency, and reliability of data.
Provides a comprehensive overview of data integrity practices, covering data governance, data quality assessment, data cleansing, and data protection.
Offers a detailed exploration of data quality, a core component of data integrity. It covers various dimensions of data quality, methods for measurement, strategic approaches, and management frameworks. It's a valuable resource for those looking to deepen their understanding of data quality principles and practices.
This recent publication offers a practical, step-by-step approach to building a data governance strategy. It emphasizes creating a data-driven culture to support data integrity efforts. is highly relevant for organizations starting their data governance journey.
While not solely focused on data integrity, this book delves into the challenges of building reliable and maintainable data systems. It covers topics such as data models, storage, retrieval, and distributed systems, all of which impact data integrity in modern applications. is valuable for those seeking a deeper technical understanding of data systems.
This practical guide provides a step-by-step methodology for implementing data quality projects. It focuses on actionable strategies and best practices for improving data quality within an organization. is particularly useful for professionals involved in data quality initiatives and can serve as a practical reference.
This guide provides a comprehensive overview of operationalizing data trustworthiness through data governance. It covers the people, processes, and tools required for effective data governance. valuable resource for implementing data governance in practice.
Introduces a practical approach to data governance by leveraging existing organizational processes. It focuses on formalizing and improving current data management activities to achieve better data integrity. This book is valuable for organizations looking for a less disruptive way to implement data governance.
Provides a detailed approach to assessing data quality. It presents methodologies and techniques for measuring and analyzing the quality of data. This book useful reference for data quality professionals and auditors.
This practical guide focuses on the essential task of data cleaning, a fundamental aspect of ensuring data integrity. It provides hands-on techniques and approaches for identifying and correcting errors in datasets. is highly relevant for anyone involved in preparing data for analysis or use.
This classic textbook provides a comprehensive introduction to database concepts, including data modeling, database design, and data integrity constraints. It lays the groundwork for understanding how data is structured and managed to maintain integrity. It is commonly used as a textbook in academic institutions.
Covers both master data management (MDM) and data governance, two closely related disciplines that are critical for ensuring data integrity across an enterprise. It discusses strategies and techniques for managing master data and implementing data governance programs. It valuable resource for professionals dealing with enterprise data.
Specifically focuses on the accuracy dimension of data quality, a key aspect of data integrity. It provides methods and techniques for assessing and improving data accuracy. It valuable resource for data quality practitioners.
Specifically addresses data governance challenges in the context of big data. It discusses principles, strategies, and architectures for managing and governing large volumes of diverse data while maintaining integrity. This book is relevant for professionals working with big data technologies.
This textbook covers the fundamentals of database management, including database design, modeling, and systems. It provides a solid theoretical foundation for understanding how databases work and how to manage data effectively to ensure integrity. is suitable for undergraduate and graduate students.
Focuses on the role of data stewards in ensuring data integrity and effective data management. It provides practical guidance on organizing, training, and empowering data stewards within an organization. This book is valuable for understanding the human aspect of data governance and integrity.
Provides a practical guide to data modeling, a crucial step in ensuring data integrity from the design phase. It covers various data modeling techniques and best practices for creating well-structured databases. It useful resource for both business and IT professionals involved in data projects.
Offers a clear explanation of relational database design principles. A well-designed database is foundational to maintaining data integrity. This book is suitable for beginners and those who need a solid understanding of database structure.
This influential book focuses on dimensional modeling for data warehouses, which is relevant to data integrity in analytical systems. It provides design principles and techniques for organizing data for reporting and analysis while maintaining consistency. It valuable reference for data warehousing professionals.
Provides a basic primer on data governance in an accessible format. It introduces the core concepts and importance of data governance for ensuring data integrity. This book is ideal for those new to the topic or seeking a high-level overview.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/7rtoh5/data
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