Meet the editor

Dr. B. Santhosh Kumar is a professor at Guru Nanak Institute of Technology, Hyderabad. His research interests include data science, machine learning, blockchain technology, and data mining. He has eight patents and one copyright to his credit and has authored five books and four book chapters. He has delivered 40 guest lectures and received 14 awards from various professional bodies. He is a reviewer for several journals, including

*IEEE Transactions*, *IEEE Access*, *ACM Transactions*, and others. He has served as session chair for various international conferences organized by ASDF(Association of Scientists, Developers and Faculties), Institute of Electrical and Electronics Engineers (IEEE), and Springer. He is a senior member of IEEE, appointed as an ACM Distinguished Speaker, and his profile was listed in the *World Book of Researchers* as a 2019 researcher of the year.

## Contents


Preface

Data is one of the essential resources for an organization to perform well. We are living in an era that is highly data-driven. From decision-making processes to enhancing customer experiences, data is involved in almost all such business activities. It is the responsibility of organizations to obtain the most benefit from the numerous petabytes and exabytes of data residing in humungous databases. This is where data integrity and quality come into play. Ensuring the integrity and quality of data enriches the insights into the business operations performed. Confidentiality and safety are major concerns in this era of big data. Modifications in technologies, rapid development of the Internet and electronic trade, and the implementation of more cultured schemes for gathering, assessing, and making use of private data have made confidentiality a key focus. Data integrity is becoming more important due to the emergence of immense volumes of information being gathered and stored in computing systems. Large amounts of data acquired from diverse mediums often contain private and delicate information and

Data integrity answers questions such as: When was the data created? What is its lifetime? Are the entries consistent? Data quality answers questions such as: Is the data relevant? Is the data complete? Is it unique? This book attempts to answer these questions to help individuals use data integrity and data quality to glean useful information

Section 2, "Data Integrity and Applications", includes the following three chapters: "Data Quality Measurement Based on Domain-Specific Information", "Multiplicative Data Perturbation Using Random Rotation Method", and "FAIR Data Model for Chemical Substances: Development Challenges, Management Strategies, and

Section 3, "Data Governance and Applications", also includes three chapters: "Ethical Considerations for Health Research Data Governance", "Predictive Data Analysis Using Linear Regression and Random Forest", and "Field Programmable Reconfigurable

In writing this book, I have been fortunate to be assisted by technical experts in many of the subdisciplines of data integrity and quality. First and foremost, praises and

I record my indebtedness to the Chairman, Vice-Chairman, Managing Director, and Principal for their guidance and sustained encouragement for the successful completion of this book. I am profoundly grateful to my colleagues at Guru Nanak Institute of

thus it is of the utmost importance to safeguard this data.

thanks to God the Almighty for his showers of blessings.

Technology for their encouragement to complete this book on time.

from large volumes of data.

Applications".

Mesh (FPRM)".

Section 1 includes the Introductory chapter.
