Analytics and Knowledge Management

467 Pages·2018·9.24 MB·English

by  Suliman Hawamdeh

Analytics and Knowledge
Management
Data Analytics Applications
Series Editor: Jay Liebowitz
PUBLISHED
Analytics and Knowledge
Actionable Intelligence for Healthcare
by Jay Liebowitz and Amanda Dawson
ISBN: 978-1-4987-6665-4
Management
Analytics and Knowledge Management
by Suliman Hawamdeh and Hsia-Ching Chang
ISBN 978-1-1386-3026-0
Big Data Analytics in Cybersecurity
by Onur Savas and Julia Deng
ISBN: 978-1-4987-7212-9
Big Data and Analytics Applications in Government:
Current Practices and Future Opportunities
by Gregory Richards
ISBN: 978-1-4987-6434-6
Big Data in the Arts and Humanities: Theory and Practice
by Giovanni Schiuma and Daniela Carlucci
ISBN 978-1-4987-6585-5
Data Analytics Applications in Education
by Jan Vanthienen and Kristoff De Witte

ISBN: 978-1-4987-6927-3
Edited by
Data Analytics Applications in Latin America and Emerging Economies
Suliman Hawamdeh
by Eduardo Rodriguez
ISBN: 978-1-4987-6276-2
Hsia-Ching Chang
Data Analytics for Smart Cities
by Amir Alavi and William G. Buttlar
ISBN 978-1-138-30877-0
Data-Driven Law: Data Analytics and the New Legal Services
by Edward J. Walters
ISBN 978-1-4987-6665-4
Intuition, Trust, and Analytics
by Jay Liebowitz, Joanna Paliszkiewicz, and Jerzy Gołuchowski
ISBN: 978-1-138-71912-5
Research Analytics: Boosting University Productivity and Competitiveness
through Scientometrics
by Francisco J. Cantú-Ortiz
ISBN: 978-1-4987-6126-0
Sport Business Analytics: Using Data to Increase Revenue and
Improve Operational Efficiency
by C. Keith Harrison and Scott Bukstein
ISBN: 978-1-4987-8542-6
Analytics and Knowledge
Management

Edited by
Suliman Hawamdeh
Hsia-Ching Chang
CRC Press
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Contents
Preface ...............................................................................................................vii
Editors ................................................................................................................xi
Contributors .....................................................................................................xiii
1 Knowledge Management for Action-Oriented Analytics .......................1
JOHN S. EDWARDS AND EDUARDO RODRIGUEZ
2 Data Analytics Process: An Application Case on Predicting
Student Attrition ..................................................................................31
DURSUN DELEN
3 Transforming Knowledge Sharing in Twitter-Based Communities
Using Social Media Analytics ...............................................................67
NICHOLAS EVANGELOPOULOS, SHADI SHAKERI,
AND ANDREA R. BENNETT
4 Data Analytics for Deriving Knowledge from User Feedback ...........121
KULJIT KAUR CHAHAL AND SALIL VISHNU KAPUR
5 Relating Big Data and Data Science to the Wider Concept
of Knowledge Management ................................................................141
HILLARY STARK AND SULIMAN HAWAMDEH
6 Fundamentals of Data Science for Future Data Scientists .................167
JIANGPING CHEN, BRENDA REYES AYALA, DUHA ALSMADI,
AND GUONAN WANG
7 Social Media Analytics .......................................................................195
MIYOUNG CHONG AND HSIA-CHING CHANG
8 Transactional Value Analytics in Organizational Development ........221
CHRISTIAN STARY
9 Data Visualization Practices and Principles .......................................251
JEONGHYUN KIM AND ERIC R. SCHULER
v
vi ◾ Contents
10 Analytics Using Machine Learning-Guided Simulations with
Application to Healthcare Scenarios ..................................................277
MAHMOUD ELBATTAH AND OWEN MOLLOY
11 Intangible Dynamics: Knowledge Assets in the Context
of Big Data and Business Intelligence ................................................325
G. SCOTT ERICKSON AND HELEN N. ROTHBERG
12 Analyzing Data and Words—Guiding Principles and Lessons
Learned ...............................................................................................355
DENISE A. D. BEDFORD
13 Data Analytics for Cyber Threat Intelligence ....................................407
HONGMEI CHI, ANGELA R. MARTIN, AND CAROL Y. SCARLETT
Index ...........................................................................................................433
Preface
The terms data analytics, Big Data, and data science have gained popularity in
recent years for a number of good reasons. The most obvious reason is the exponen-
tial growth in digital information and the challenge of managing large sets of data.
Big Data forms a challenge and opportunity at the same time. It is a challenge if
not managed properly and the organization does not make the needed investment
in the knowledge infrastructure recognizing the value of data as an organizational
asset. Knowledge infrastructure is made of several components including intel-
lectual capital (human capital, social capital, intellectual property, and content),
physical capital, and financial capital. What makes Big Data an opportunity is the
prospects of knowledge discovery from Big Data and the value of such knowledge
in enhancing an organization’s competitive advantage through improved products
and services, as well as enhanced decision-making processes.
Given the cost associated with managing Big Data, organizations must adopt
a knowledge management strategy in which Big Data is viewed as a key organi-
zational asset. This also includes making the necessary investment in data science
and data analytics tools and technologies. Knowledge management places a higher
emphasis on people and human capital as a key to realizing the concept of the
knowledge-based economy. This means any knowledge management strategy must
include a plan to educate and enhance the capacity of those working with Big Data
and knowledge discovery.
The shift toward the knowledge economy and the realization of the importance
of data as an organizational asset within the context of knowledge management
has given rise to the emerging fields of data science and data analytics. The White
House’s “Data to Knowledge to Action” initiative in 2013 aimed at building Big
Data partnerships with academia, industries, and public sectors. This initiative
led to the National Science Foundation (NSF) increasing the nation’s data science
capacity by investing in human capital and infrastructure development. The Big
Data to Knowledge (BD2K) initiative by the National Institutes of Health (NIH)
in 2012 and Google’s Knowledge Graph were also aimed at building big data
capacity. Such capabilities will be based on well-established knowledge infrastruc-
tures made of a network of individuals, organizations, routines, shared norms, and
practices. Building knowledge infrastructures requires human interactions through
vii
viii ◾ Preface
connecting people, organizations, and practices to facilitate knowledge discovery
from Big Data. However, relatively few organizations have developed data gover-
nance and knowledge management plans for handling and managing Big Data and
big data analytics projects.
Knowledge management is an interdisciplinary approach to dealing with all
aspects of knowledge processes and practices. Many of these activities are critical to
the notion of managing data and creating information and knowledge infrastruc-
ture in the long run. We all understand the importance of information organiza-
tion and data management for data (big or small) to be useful. The term “garbage
in, garbage out” is used to describe poor data and information organization prac-
tices. The long-term preservation of big data as part of the knowledge retention
process is crucial to the knowledge discovery process.
The process of transforming data into actionable knowledge is a complex pro-
cess that requires the use of powerful machines and advanced analytics techniques.
Analytics, on the other hand, is the examination, interpretation, and discovery of
meaningful patterns, trends, and knowledge from data and textual information. It
provides the basis for knowledge discovery and completes the cycle in which knowl-
edge management and knowledge utilization happen. This book examines the role
of analytics in knowledge management and the integration of Big Data theories,
methods, and techniques into the organizational knowledge management frame-
work. The peer-reviewed chapters included in this book provide an insight into
theories, models, techniques, applications, and case studies in the use of analytics
in organizations. The following are summaries of each chapter.
Chapter 1, “Knowledge Management for Action-Oriented Analytics” by
Edwards and Rodriguez traces the evolution of analytics, compares several practi-
cal concepts (analytics, business analytics, business intelligence, and Big Data),
and associates those concepts with knowledge management. They recommend
that organizations develop knowledge focuses on data quality, application domain,
selecting analytics techniques, and on how to take actions based on patterns and
insights derived from analytics. Such actions rest on leveraging people, processes,
and technology. They discuss the classifications of analytics from the dual per-
spectives of types of analytics techniques (descriptive, predictive, and prescriptive
analytics) and business management (strategic, managerial, operational, customer-
facing, or scientific analytics). From a global and cross-industry perspective, they
also provide examples that demonstrate how each individual analytics type corre-
sponds to one or multiple knowledge focuses.
Chapter 2, “Data Analytics Process: An Application Case on Predicting
Student Attrition” by Delen discusses a significant and coincidental resemblance
between the most popular data mining methodology, Cross-Industry Standard
Process for Data Mining (CRISP-DM), which addresses analytics processes, and
the Six Sigma-based DMAIC (define, measure, analyze, improve, and control)
methodology that not only improves organizational performances but supports
knowledge management processes. To exemplify the CRISP-DM approach, Delen
Preface ◾ ix
introduces a specific case of data analytics in higher education regarding predicting
student success and student attrition.
Chapter 3, “Transforming Knowledge Sharing in Twitter-Based Communities
Using Social Media Analytics” by Evangelopoulos, Shakeri, and Bennett showcases
eight vignettes of social media analytics and illustrates how different dimensions
(such as time, location, topic, and opinion) can contribute to the knowledge base
of Twitter-based communities. Incorporating derived facts and derived dimen-
sions, they devise a knowledge base data warehouse (KBDW) schema for present-
ing transformed explicit knowledge in Twitter-based communities.
Chapter 4, “Data Analytics for Deriving Knowledge from User Feedback” by
Chahal and Kapur is a practice-oriented contribution. To make analytics of user
feedback data meaningful, they suggest connecting data analytics with data man-
agement and knowledge management as a three-step approach. They select the case
of the Indian government’s demonetization drive in 2016 and describe how the
government can better understand public perceptions across the country by means
of opinion mining and data mining of Twitter data.
Chapter 5, “Relating Big Data and Data Science to the Wider Concept of
Knowledge Management” by Stark and Hawamdeh outlines the concept of data
science, a core component being taught within master’s programs that focuses on
data as well as the skill set desired by current employers who seek to hire data scien-
tists. The chapter reviews some of the current data science and data analytics tools
being used to store, share, mine, model, and visualize Big Data. The chapter also
reviews some of the case study applications and their relevance to the concept of
managing data, small and big.
Chapter 6, “Fundamentals of Data Science for Future Data Scientists” by
Chen, Ayala, Alsmadi, and Wang considers data science as an interdisciplinary
field revolving around data and puts data science in the context of knowledge man-
agement processes. Through literature review and the analysis of 298 job postings,
they identify the desired knowledge and skills for data scientists. Additionally, they
reviewed existing data science programs in the United States and provided sugges-
tions for integrated curriculum design.
Chapter 7, “Social Media Analytics” by Chong and Chang starts with the evo-
lution of social media and analytics. Besides introducing the definitions and pro-
cesses of social media analytics from different perspectives, the chapter focuses on
identifying the main techniques and tools used for social media analytics.
Chapter 8, “Transactional Value Analytics in Organizational Development” by
Stary introduces an approach of value network analysis (VNA) and suggests how
organizations can use different proven methods (repertory grid, critical incident
technique, and storytelling) to elicit the tacit knowledge from stakeholder transac-
tions for value management. Stary interviewed 14 active analysts who had diverse
backgrounds and experiences in practical value management for international orga-
nizations in Germany and Austria. Those analysts appeared to reach the consensus
that the repertory grid technique combining qualitative with quantitative insights