Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Tacit Knowledge (TK) generally refers to information that is difficult to convey, store, or transfer explicitly. KT is a key challenge for corporations interested in capturing information in Knowledge Management (KM) systems that is generally lost with attrition or other human factors (e.g., dimensia). In particular, the challenge is in the capture of implicit information (e.g., additional related data, perspectives, and other frames of reference) – in a manner in which it can later be utilized. This paper suggests the use of Cognitive Computing (Analytics) as an advanced approach to capture and extract tacit knowledge. KM involves the process of identifying, capturing, extending, sharing, and ultimately exploiting individual or organizational knowledge. Today’s KM requires a multi-disciplinary approach, capable of extending itself to deal with large volumes of disparate data types and emerging technologies that provide a broad set of search and analytics capabilities to meet an organization’s need to innovate and thrive. Many organizations have extended their KM to include a variety of unstructured text (e.g., documents and web pages) and multimedia (e.g., pictures, audio and video). The last decade has shown a strong focus on analytics. Analytics provide large organizations the ability to deal with the exponential growth in data volumes and the complexities associated with effectively and efficiently exploiting corporate or organizational data – thus allowing them to dynamically meet internal goals, as well as survive in very competitive environments. This paper provides an overview of various analytic approaches that have been applied to KM over the years, and the state of the art in analytics (Cognitive Computing); and it identifies additional capabilities and technologies in the horizon.
