Big Data Management and Analytics: Concepts, Tools, and Applications

by Rajesh Jugulum, David J. Fogarty, Chris Heien, Surya Putchala

Overview

As more companies go digital and conduct their business online, this book provides practical examples of how they can better manage their data and use it to generate maximum value. It offers an integrated approach by treating data as an asset and discusses how to preserve and protect it just like any other corporate asset.

Big Data Management and Analytics: Concepts, Tools, and Applications illustrates effective strategies for managing, governing, and analyzing big data to gain a competitive edge for companies utilizing big data and analytics. It offers a comprehensive guide on methods, tools, and concepts to efficiently manage and analyze big data in order to make informed decisions. Additionally, this book explores the significance of artificial intelligence and machine learning in leveraging big data and how they can be optimized in a well-structured environment. This book also emphasizes treating big data as a valuable asset and outlines strategies for preserving and safeguarding it like any other corporate asset. The inclusion of case studies ensures that the methodologies and concepts presented can be easily implemented in day-to-day operations.

Given the current significance of big data in the business world, this book equips readers with the necessary skills to effectively manage this valuable asset. It is tailored for practitioners, students, and professionals working in data mining, big data, and machine learning across various industries, including manufacturing.

Product Details

ISBN-13:	9781032040400
Publisher:	CRC Press
Publication date:	06/30/2025
Pages:	186
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Rajesh Jugulum, Ph.D., is the Chairman and Chief Data Science and Analytics Officer at DataDragon and an affiliate professor at Northeastern University. Prior to this, he held executive positions in the areas of data science, analytics and process engineering at Cigna, Citi Group and Bank of America. Rajesh completed his Ph.D. under the guidance of Dr. Genichi Taguchi. Before joining industry, Rajesh was with Massachusetts Institute of Technology, where he was involved in research and teaching.

Currently, he is also an affiliate faculty at University of Arkansas, Little Rock. ajesh is the author/co-author of several papers and five books including books on robust quality, data quality and design for lean six sigma. ajesh is also a certified Six Sigma Master Black Belt and holds two US patents and he has delivered talks across the globe as the keynote speaker at several conferences, symposiums, and events related to data science, analytics and process engineering. He has also delivered lectures at several universities/companies across the globe and participated as a judge in data-related competitions

David Fogarty, PhD. MBA currently works for one of the largest global health insurers as their Chief Marketing Analytics Officer and Head of Global Customer Value Management and Analytics.

For 20 years David worked at the General Electric Company and has held quantitative analysis leadership roles in the various business units of the company across several functions including risk management and marketing both internationally and in the US. David has over 15 US patents or patents pending on business analytics algorithms and is a certified Six Sigma Master Black Belt in Quality which is the highest qualification within the Six Sigma Quality methodology.

David has over 15 years of teaching experience having held various adjunct academic appointments at both the graduate and undergraduate level in statistics, international management and quantitative analysis. He has also taught business analytics courses at the esteemed GE Crotonville Management Development Institute in Crotonville, New York and has 50 published research papers in peer reviewed academic journals and has also published three books. His research interests include how to conduct analysis with missing data, the cultural meaning of data, integrating machine learning and artificial intelligence algorithms into the statistical science framework and many other topics related to quantitative analysis in business.

Chris Heien, Ph.D., is a software engineer and data management professional with expertise in information manufacturing systems and data flow optimization. Chris has served across multiple roles at Cigna, GE Capital, and Citigroup where he applied his background to multiple analytics activities ranging from development of data quality monitoring strategies to analytics execution in big data environments.

Chris holds a PhD in Information Quality from the University of Arkansas at Little Rock where he previously instructed Object-Oriented programming courses and continues to serve as affiliate faculty.

Surya Putchala is a Technology leader with expertise in driving measurable operational improvements for Fortune 500 companies and startups through innovative consulting and transformation solutions. As an entrepreneur and principal, Surya has spearheaded the development of three innovative AI solutions, resulting in $20 million in mergers and acquisitions. His expertise spans Generative AI, Large Language Models (LLMs), Data Science, and Machine Learning, with a strong focus on delivering tangible business outcomes.

Throughout his career, Surya has demonstrated ability by building high-performing teams and establishing Centers of Excellence. He has mentored over 300 employees and guided six startups, contributing to the advancement of AI-driven innovations.

Surya has delivered over 50 Proofs of Concept across finance, retail, and healthcare industries, generating significant revenues and acquiring marquee clients. Surya has been named a LinkedIn Top Voice in AI and actively contributes to the AI community through mentoring, publishing, and speaking engagements. Surya combines technical acumen with strategic insights, making him a trusted advisor for organizations aiming to harness the power of AI and data-driven strategies.

Table of Contents

Foreword

Preface and Acknowledgements

Authors

Chapter 1 - The Management of BIG Data Overview

Chapter 2 - Big Data, Hadoop Distro, Data Triad, and Enterprise Data Lakes

Chapter 3 - The Data Supply Chain

Chapter 4 - Data Quality – Measurement and Its Impact

Chapter 5 - Analytics Landscape, Execution, and Evaluation

Chapter 6 - Big Data and Cloud Solutions

Chapter 7 - Structuring Unstructured Data and NoSQL

Chapter 8 - Design and Development of Multivariate Diagnostic Systems

Chapter 9 - Big Data and Artificial Intelligence (AI)

Chapter 10 - Aligning Big Data and AI Strategy with Business Goals

Chapter 11 - Facets of Responsible AI

References

Appendixes