The Knowledge for Development (K4D) Challenge Conference, quasi successor of the Knowledge for Development Partnership Conference (K4DP Conference), initially the Global Knowledge for Development Summit, quasi-successor of the Austrian Conference on Knowledge Management and Knowledge Politics (Agenda Knowledge), will present all findings of the Knowledge for Development (K4D) Challenges made by the K4D Challengers in
International Symposium on Knowledge Acquisition and Modeling (KAM)**
The International Symposium on Knowledge Acquisition and Modeling (KAM) is concerned with the aspects of Intelligent Information Processing, acquiring, modeling, managing and exploiting knowledge, and the role of these aspects in the construction of knowledge-intensive systems and Intelligent Information services. It provides a chance for academic and industry professionals to discuss recent progress in the
Investigating the Data Analytics and Knowledge Management Job Market
Abstract: Purpose: This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design. Design/methodology/approach: This study traced and retrieved job ads on LinkedIn
Knowledge Management as part of the IS undergraduate curriculum
Abstract: Knowledge management (KM) is an area that has captured the attention of many organisations that are concerned with the ways knowledge is managed more effectively. KM offers systematic methods in leveraging and managing organisational knowledge through KM processes of creation, storing, sharing, and application of knowledge. Due to the importance of KM, the
Knowledge Scientists
Abstract: Organizations across all sectors are increasingly undergoing deep transformation and restructuring towards data-driven operations. The central role of data highlights the need for reliable and clean data. Unreliable, erroneous, and incomplete data lead to critical bottlenecks in processing pipelines and, ultimately, service failures, which are disastrous for the competitive performance of the organization.