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.

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.

Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.

Organizational Intelligence and Knowledge Analytics expands the traditional intelligence life cycle to a new framework - Design-Analyze-Automate-Accelerate - and clearly lays out the alignments between knowledge capital and intelligence strategies.

Although there are numerous publications in the field of knowledge management (KM), there are still gaps in the literature regarding the aspects of KM that reflect new technology adoption and a deeper analysis discussing the interlinked process between KM and data analytics in business process improvement. It is essential for business leaders to understand the role and responsibilities of leaders for the adoption and consolidation of a KM system that is effective and profitable. Understanding, Implementing, and Evaluating Knowledge Management in Business Settings provides a comprehensive approach to KM concepts and practices in corporations and business organizations. Covering topics such as information overload, knowledge sharing adoption, and collective wisdom, this premier reference source is a comprehensive and essential resource for business executives, managers, IT specialists and consultants, libraries, students, entrepreneurs, researchers, and academicians.

Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well—and not so well—so others can learn from these cases during their knowledge management journey. Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility. Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization. - Provides keen insights for knowledge management practitioners and educators - Conveys KM lessons learned through both successes and failures - Includes straightforward, jargon-free case studies and research developed by the leading KM researchers and practitioners across industries

This book features a selection of extended papers presented at the 8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021, held in Yokohama, Japan, in January 2021, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2020.* The 14 revised and extended papers presented together with an invited talk were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and discuss methodological, technical and organizational aspects of artificial intelligence used for knowledge management. *The workshop was held virtually.

With a Foreword by Dr. Heinrich von Pierer President and CEO of Siemens AG While theoretical perspectives on knowledge management abound, there is clearly a lack of shared practical applications and experiences. This book provides a perspective on knowledge management at Siemens - an internationally recognised benchmark. Tom Davenport and Gilbert Probst bring together instructive case studies from different areas of this major transnational corporation that reflect the rich insights gained from years of experience in practising knowledge management. The Knowledge Management Case Book provides a comprehensive account of how organisational knowledge assets can be managed effectively. Specific emphasis is given to the development of generic lessons that can be learned from Siemens' experience. The book also offers a roadmap to building a 'mature knowledge enterprise', thereby enhancing our understanding of the steps that need to be taken in order to sustain competitive dominance in the knowledge economy.

This book provides a range of application areas of data and knowledge management and their solutions for the fields related to the convergence of information and communication technology (ICT), healthcare, and telecommunication services. The authors present approaches and case studies in future technological trends and challenges in the aforementioned fields. The book acts as a scholarly forum for researchers both in academia and industry.