This type of KM involves identifying the organization's experts and leveraging their knowledge and experience to benefit the organization.
Knowledge management: A process of creating, sharing, using, and managing knowledge and information in an organization to support its goals and objectives.
Expertise identification: The process of identifying individuals within an organization who possess specific skills, knowledge, or experience that may be useful for a particular task or project.
Expertise mapping: The process of visualizing the distribution of expertise within an organization to identify areas of strengths and weaknesses.
Expertise retrieval: The process of searching for and accessing relevant expertise within an organization, usually through a knowledge management system.
Social capital: The value that is derived from the relationships, networks, trust, and goodwill that exist within and between individuals and groups in an organization.
Communities of practice: Informal groups of individuals who share a common interest or expertise and engage in collaborative learning and knowledge sharing.
Knowledge sharing: The process of disseminating knowledge and information across an organization, often through formal or informal channels.
Learning organizations: Organizations that promote continuous learning, innovation, and adaptation as a key factor for success.
Expertise retention: The process of retaining and utilizing the knowledge and experience of employees who are retiring or leaving the organization.
Big data analytics: The use of advanced data analytics tools and techniques to analyze large and complex datasets to uncover patterns and insights that can be used to improve expertise location and management.
Talent management: The process of identifying, developing, and retaining talent within an organization, including the management of expertise.
Decision support systems: Computer-based systems that provide decision-makers with relevant information and analytical tools to support decision-making.
Artificial intelligence and machine learning: The use of computer algorithms and models to analyze and make predictions based on large amounts of data, which can be used to improve expertise location and management.
Knowledge transfer: The process of transferring knowledge and expertise from one individual or group to another, often through mentoring, coaching, or training.
Measuring expertise: The process of quantifying and tracking the distribution of expertise within an organization, usually through the use of metrics and key performance indicators.
People-based Expertise location and management: This involves identifying experts within an organization based on their skills, knowledge, and experience. The experts can then be utilized to solve problems, train others, and share knowledge.
Content-based Expertise location and management: This involves identifying and organizing knowledge resources within an organization, such as documents, reports, and other data sources. This helps to ensure that relevant information is accessible to those who need it.
Community-based Expertise location and management: This involves creating communities of practice within an organization, where people share knowledge, collaborate on projects, and learn from each other.
Social-based Expertise location and management: This involves leveraging social networks and social media tools to identify and connect experts within an organization.
Data-driven Expertise location and management: This involves analyzing large data sets to identify patterns, trends, and other insights that can be used to inform decision-making and knowledge sharing.
Model-based Expertise location and management: This involves using mathematical models and simulations to better understand complex systems and processes within an organization.
Expert systems Expertise location and management: This involves building intelligent software systems that can assist with problem-solving and decision-making, based on the knowledge and expertise of subject matter experts.
Machine learning-based Expertise location and management: This involves using machine learning algorithms to identify patterns and insights in data, and to predict future trends and outcomes.
Cognitive computing-based Expertise location and management: This involves building systems that can learn and adapt based on their interactions with users, and that can assist with complex decision-making processes.