446 pages | 10 Color Illus. | 15 B/W Illus.
The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations.
The process of transforming data into actionable knowledge is a complex process 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 knowledge management and knowledge utilization happen. Organizations should 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.
Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics.
Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships.
This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.
1 Knowledge Management for Action-Oriented Analytics
[JOHN S. EDWARDS AND EDUARDO RODRIGUEZ]
2 Data Analytics Process: An Application Case on Predicting
3 Transforming Knowledge Sharing in Twitter-Based Communities
Using Social Media Analytics
[NICHOLAS EVANGELOPOULOS, SHADI SHAKERI,
AND ANDREA R. BENNETT]
4 Data Analytics for Deriving Knowledge from User Feedback
[KULJIT KAUR CHAHAL AND SALIL VISHNU KAPUR]
5 Relating Big Data and Data Science to the Wider Concept
of Knowledge Management
[HILLARY STARK AND SULIMAN HAWAMDEH]
6 Fundamentals of Data Science for Future Data Scientists
[JIANGPING CHEN, BRENDA REYES AYALA, DUHA ALSMADI,
AND GUONAN WANG]
7 Social Media Analytics
[MIYOUNG CHONG AND HSIA-CHING CHANG]
8 Transactional Value Analytics in Organizational Development
9 Data Visualization Practices and Principles
[JEONGHYUN KIM AND ERIC R. SCHULER]
10 Analytics Using Machine Learning-Guided Simulations with
Application to Healthcare Scenarios
[MAHMOUD ELBATTAH AND OWEN MOLLOY]
11 Intangible Dynamics: Knowledge Assets in the Context
of Big Data and Business Intelligence
[G. SCOTT ERICKSON AND HELEN N. ROTHBERG]
12 Analyzing Data and Words—Guiding Principles and Lessons
[DENISE A. D. BEDFORD]
13 Data Analytics for Cyber Threat Intelligence
[HONGMEI CHI, ANGELA R. MARTIN, AND CAROL Y. SCARLETT]