Obtaining Value from Big Data for Service Systems: Big Data Technology

125 Pages·2019·2.006 MB·English

by  Stephen H. Kaisler, Frank Armour, J. Alberto Espinosa, William H. Money

Obtaining Value from Big Data K Service Systems and Innovations
THE BUSINESS A
EXPERT PRESS for Service Systems, Volume II ISL in Business and Society Collection
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DIGITAL LIBRARIES R Jim Spohrer and Haluk Demirkan, Editors
Big Data Technology (cid:127)
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EBOOKS FOR Second Edition M
BUSINESS STUDENTS O
U Obtaining Value
Curriculum-oriented, born- Stephen H. Kaisler (cid:127) Frank Armour (cid:127) R
digital books for advanced J. Alberto Espinosa (cid:127) William H. Money (cid:127) E
business students, written S from Big Data
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by academic thought Volume II of this series discusses the technology used to imple- IN
ment a big data analysis capability within a service-oriented O
leaders who translate real-
organization. It discusses the technical architecture necessary to S for Service
world business experience A
implement a big data analysis capability, some issues and chal- (cid:127)
into course readings and lenges in big data analysis and utilization that an organization M
reference materials for will face, and how to capture value from it. O Systems, Volume II
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students expecting to tackle It will help readers understand what technology is required E
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management and leadership for a basic capability and what the expected benefi ts are from
challenges during their establishing a big data capability within their organization. Big Data Technology
professional careers.
Stephen H. Kaisler is consultant at SHK & Associates and
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POLICIES BUILT adjunct professor of engineering at George Washington Univer- B
BY LIBRARIANS sity. He has focused his efforts on Big Data and analytics, and TA Second Edition
enterprise architecture. He has published over 45 papers and IN
(cid:127) Unlimited simultaneous 11 books on varied subjects such as Big Data and Historical Com- IN
usage puting Machines. G
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(cid:127) Unrestricted downloading Frank Armour is assistant professor in the Kogod School of Busi- A
and printing L
ness at the American University and the faculty program direc- U
(cid:127) Perpetual access for a tor for the MS degree program in analytics. He has over 25 years E F
one-time fee of extensive experience in both the practical and academic R
(cid:127) No platform or aspects of applying advanced information technology. OM Stephen H. Kaisler
maintenance fees B
(cid:127) Free MARC records J. Alberto Espinosa is professor and former chair of information IG Frank Armour
(cid:127) No license to execute technology in the Kogod School of Business at the American Uni- D
versity. His research focuses on coordination in global technic- A
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The Digital Libraries are a al projects across global boundaries, particularly distance and A J. Alberto Espinosa
comprehensive, cost-eff ective time. He published in various journals including IEEE Transac- FO
tions on Software Engineering and IEEE Transactions on Engineering
way to deliver practical R William H. Money
Management. S
treatments of important E
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business issues to every William H. Money is associate professor at the Baker School V
of Business, The Citadel. His recent research interests focus IC
student and faculty member. on collaborative solutions to complex business problems; E S
business process engineering, analytics; and information sys- Y
tem development, collaboration, and methodologies. He has sig- ST
nifi cant consulting experience in private organizations and the E
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For further information, a federal government. S
free trial, or to order, contact: , V
Service Systems and Innovations O
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[email protected] in Business and Society Collection U
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www.businessexpertpress.com/librarians Jim Spohrer and Haluk Demirkan, Editors E I
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Obtaining Value from Big
Data for Service Systems,
Volume II

Obtaining Value from Big
Data for Service Systems,
Volume II
Big Data Technology
Second Edition
Stephen H. Kaisler
Frank Armour
J. Alberto Espinosa
William H. Money
Obtaining Value from Big Data for Service Systems, Volume II,
Second Edition: Big Data Technology
Copyright © Business Expert Press, LLC, 2019.
All rights reserved. No part of this publication may be reproduced, stored
in a retrieval system, or transmitted in any form or by any means—
electronic, mechanical, photocopy, recording, or any other except for
brief quotations, not to exceed 250 words, without the prior permission
of the publisher.
First published in 2019 by
Business Expert Press, LLC
222 East 46th Street, New York, NY 10017
www.businessexpertpress.com
ISBN-13: 978-1-94999-146-8 (paperback)
ISBN-13: 978-1-94999-147-5 (e-book)
Business Expert Press Service Systems and Innovations in Business and
Society Collection
Collection ISSN: 2326-2664 (print)
Collection ISSN: 2326-2699 (electronic)
Cover and interior design by S4Carlisle Publishing Services Private Ltd.,
Chennai, India
First edition: 2016
Second edition: 2019
10 9 8 7 6 5 4 3 2 1
Printed in the United States of America.
Dedication
I would like to dedicate this book to my wife, Chryl, who has
encouraged me and supported me in its preparation.
—Stephen H. Kaisler
I would like to thank my wife, Delphine Clegg, for her support
and encouragement in preparing this book.
—J. Alberto Espinosa
To my wife, Rose, my parents, my children, and my grandchild:
you make everything beautiful.
—Frank Armour
To my wonderful wife, Libby, whose continuous support and
strength allow me to devote more energy to research and writing,
and to my daughter, Katy, whose enduring dedication and commitment
have taught me how to successfully close a project.
—William H. Money

Abstract
Big data is an emerging phenomenon that has enormous implications and
impacts upon business strategy, profitability, and process improvements.
All service systems generate big data these days, especially human-centered
service systems such as government (including cities), health care, educa-
tion, retail, finance, and so on. It has been characterized as the collection,
analysis, and use of data characterized by the five Vs: volume, velocity,
variety, veracity, and value (of data). As the plethora of data sources grows
from sensors, social media, and electronic transactions, new methods for
collecting or acquiring, integrating, processing, analyzing, understanding,
and visualizing data to provide actionable information and support inte-
grated and timely senior and executive decision making are required. The
discipline of applying analytic processes to find and combine new sources
of data and extract hidden crucial decision-making information from the
oceans of data is rapidly developing, but requires expertise to apply in
ways that will yield useful, actionable results for service organizations.
Many service-oriented organizations that are just beginning to invest in
big data collection, storage, and analysis need to address the numerous
issues and challenges that abound—technological, managerial, and legal.
Other organizations that have begun to use new data tools and techniques
must keep up with the rapidly changing and snowballing work in the
field. This booklet will help middle, senior, and executive managers to un-
derstand what big data is: how to recognize, collect, process, and analyze
it; how to store and manage it; how to obtain useful information from it;
and how to assess its contribution to operational, tactical, and strategic
decision making in service-oriented organizations.
Keywords
analytic science; big data; business analytics; business intelligence; data
science; descriptive analytics; enterprise architecture; NoSQL; predictive
analytics; service delivery; service-oriented architecture

Contents
Purpose .................................................................................................xi
Acknowledgments .................................................................................xiii
List of Acronyms ...................................................................................xv
Chapter 1 Big Data Infrastructure—A Technical Architecture
Overview ..........................................................................1
Chapter 2 Issues and Challenges in Big Data and Analytics .............47
Chapter 3 Conclusion......................................................................61
Appendix ..............................................................................................81
References .............................................................................................85
Further Reading ...................................................................................93
Glossary ...............................................................................................95
About the Contributors .........................................................................97
Index .................................................................................................101