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.

Leveraging the knowledge gained from Knowledge Management and from the growing fields of Analytics and Artificial Intelligence (AI), this Research Agenda highlights the research gaps, issues, applications, challenges and opportunities related to Knowledge Management (KM). Exploring synergies between KM and emerging technologies, leading international scholars and practitioners examine KM from a multidisciplinary perspective, demonstrating the ways in which knowledge sharing worldwide can be enhanced in order to better society and improve organisational performance.

"This book addresses the multiple strands that feed into our understanding of sustainable big data and data analytics, as well as knowledge management"--

Knowledge management (KM) is the identification and analysis of available and required knowledge, and the subsequent planning and control of actions, to develop "knowledge assets" that enable businesses to generate profits and improve their competitive positions. This volume provides the framework for the strategic use of the information intelligen

This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.

This volume presents a methodology for defining, measuring and improving data quality. It lays out an economic framework for understanding the value of data quality, then outlines data quality rules and domain- and mapping-based approaches to consolidating enterprise knowledge.

The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.

"This encyclopedia is a research reference work documenting the past, present, and possible future directions of knowledge management"--Provided by publisher.