| Description |
1 online resource (467 pages) |
| Series |
Data Analytics Applications Ser |
|
Data Analytics Applications Ser
|
| Contents |
Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; Editors; Contributors; 1: Knowledge Management for Action-Oriented Analytics; 2: Data Analytics Process: An Application Case on Predicting Student Attrition; 3: Transforming Knowledge Sharing in Twitter-Based Communities Using Social Media Analytics; 4: Data Analytics for Deriving Knowledge from User Feedback; 5: Relating Big Data and Data Science to the Wider Concept of Knowledge Management; 6: Fundamentals of Data Science for Future Data Scientists; 7: Social Media Analytics |
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8: Transactional Value Analytics in Organizational Development9: Data Visualization Practices and Principles; 10: Analytics Using Machine Learning-Guided Simulations with Application to Healthcare Scenarios; 11: Intangible Dynamics: Knowledge Assets in the Context of Big Data and Business Intelligence; 12: Analyzing Data and Words-Guiding Principles and Lessons Learned; 13: Data Analytics for Cyber Threat Intelligence; Index |
| Summary |
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 |
| Notes |
Print version record |
| Subject |
Knowledge management -- Data processing
|
|
Management -- Statistical methods
|
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Big data.
|
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COMPUTERS -- Database Management -- Data Mining.
|
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MATHEMATICS -- Probability & Statistics -- General.
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Big data
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Knowledge management -- Data processing
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Management -- Statistical methods
|
| Form |
Electronic book
|
| Author |
Chang, Hsia-Ching
|
| ISBN |
9781351807005 |
|
1351807005 |
|
9781351806992 |
|
1351806998 |
|
9781351806985 |
|
135180698X |
|
9781315209555 |
|
1315209551 |
|
