Chris Heien, David J. Fogarty, Rajesh Jugulum, Surya Putchala
Big Data Management and Analytics
Concepts, Tools, and Applications
Chris Heien, David J. Fogarty, Rajesh Jugulum, Surya Putchala
Big Data Management and Analytics
Concepts, Tools, and Applications
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As more companies go digital and conduct their business online, this book provides practical examples of how companies can better manage their data and use it to generate maximum value. The book offers an integrated approach by treating data as an asset and discusses how to preserve and protect it just like any other corporate asset.
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As more companies go digital and conduct their business online, this book provides practical examples of how companies can better manage their data and use it to generate maximum value. The book offers an integrated approach by treating data as an asset and discusses how to preserve and protect it just like any other corporate asset.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 194
- Erscheinungstermin: 15. Mai 2025
- Englisch
- Abmessung: 234mm x 156mm
- ISBN-13: 9781032040400
- ISBN-10: 1032040408
- Artikelnr.: 72210301
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 194
- Erscheinungstermin: 15. Mai 2025
- Englisch
- Abmessung: 234mm x 156mm
- ISBN-13: 9781032040400
- ISBN-10: 1032040408
- Artikelnr.: 72210301
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
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. Rajesh is the author/co-author of several papers and five books including books on robust quality, data quality and design for lean six sigma. Rajesh 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.
Foreword
Preface & Acknowledgements
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 - Importance of Data Quality
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
Preface & Acknowledgements
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 - Importance of Data Quality
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
Foreword
Preface & Acknowledgements
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 - Importance of Data Quality
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
Preface & Acknowledgements
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 - Importance of Data Quality
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