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Slide 1: Journal of Knowledge Management Vol. 11, No. 1 (2007) A Strategy-Based Ontology of Knowledge Management Technologies André Saito, Katsuhiro Umemoto and Mitsuru Ikeda Japan Advanced Institute of Science and Technology Graduate School of Knowledge Science Ver 1.1 – 2006.01.17
Slide 2: Background  Knowledge management (KM) is still emerging   The word knowledge has many different meanings Contributions come from many disciplines  The role of technology in KM needs further explanation   Technology itself is complex and fast-paced Existing accounts present limitations  A link between KM technologies and strategy is missing   KM itself suffers from lack of strategic alignment Strategic alignment of IT is a well known issue 2 Saito, Umemoto and Ikeda 2006
Slide 3: Objectives and Methodology  Objectives   To describe the relations among technology, KM, and strategy To categorize available KM technologies according to those relations. An ontology development method was used to identify and formally define concepts and their relationships Two sub-domains were mapped: KM technologies and KM strategy 3  Methodology   Saito, Umemoto and Ikeda 2006
Slide 4: Findings on KM strategy Three meanings associated to the term:  An approach to KM Express a particular perspective on knowledge and how it can be managed A knowledge strategy Identify and prioritize knowledge to be managed, based on its contribution to business strategy A KM implementation strategy Describes steps and conditions for the   Saito, Umemoto and Ikeda 2006 4
Slide 5: KM strategy as… Approach to KM Saito, Umemoto and Ikeda 2006 5
Slide 6: KM strategy as… Knowledge strategy Saito, Umemoto and Ikeda 2006 6
Slide 7: KM strategy as… KM implementation strategy Saito, Umemoto and Ikeda 2006 7
Slide 8: KM strategy conceptual map Saito, Umemoto and Ikeda 2006 8
Slide 9: Findings on KM technologies Common sources of misunderstanding:  Technologies are usually associated with knowledge processes, which are numerous and highly context-dependent Technologies are usually integrated into systems, in many different levels   Component technologies ≠ KM systems  KM systems can be either generic or domain-specific applications  Generic KM applications ≠ Business applications 9 Saito, Umemoto and Ikeda 2006
Slide 10: An ontology of KM technologies Three basic categories:    Component technologies (integrated into other systems) KM applications (for general knowledge processes) Business applications with KM functionality (for specific business processes) 10 Saito, Umemoto and Ikeda 2006
Slide 11: KM component technologies Storage. Databases, repositories, file-servers, data warehouses, data marts, etc. Connectivity. Internet, security, wireless, mobility, authentication, P2P, etc. Communication. E-mail, mailing lists, discussion groups, chat, instant messaging, audio/video conferencing, VoIP, etc. Authoring. Office suites, desktop publishing, graphic suites, multimedia, imaging, etc. Distribution. Web, intranets, extranets, enterprise portals, personalization, syndication, audio/video streaming, etc. Search. Search engines, search agents, indexing, glossaries, thesauri, taxonomies, ontologies, collaborative filtering, etc. Analytics. Query, reporting, multi-dimensional analysis (OLAP), etc. Workflow. Process modeling, process engines, etc. E-learning. Interactive multimedia (CBT), web seminars, simulations, etc. Saito, Umemoto and Ikeda 2006 Collaboration. Calendaring, file sharing, meeting support, application sharing, group decision support, etc. Community. Community management, web logs, wikis, social network analysis, etc. Creativity. Cognitive mapping, idea generation, etc. Data mining. Statistical techniques, multidimensional analysis, neural networks, etc. Text mining. Semantic analysis, Bayesian inference, natural language processing, etc. Web mining. Collaborative profiling, intelligent agents, etc. Visualization. 2D and 3D navigation, geographic mapping, etc. Organization. Ontology development, ontology acquisition, taxonomies, glossaries, thesauri, etc. Reasoning. Rule-based expert systems, casebased reasoning, knowledge-bases, machine learning, fuzzy logic, etc. 11
Slide 12: KM applications Document management Automate the control of electronic documents through their entire life-cycle. Decision support Integrate a series of tools for decision making. Content management Manage the whole Web publishing process. Discovery and data mining Support the identification of patterns and in large amounts of data. Process management Automate the flow of tasks and information across business processes. Search and organization Facilitate access to and organize unstructured content. Group support Support work and collaboration of groups and teams. Enterprise portals Integrate access to a range of information at a single point of entry. Project management Support the management of project activities and resources. Learning management Support the delivery of online courses in a variety of formats. Community support Coordinate interaction in large groups. Saito, Umemoto and Ikeda 2006 Expertise management Brokers expertise in large communities. 12
Slide 13: Business apps with KM funcionality Representative Sales Force Automation Contact Center Backoffice systems Customer profiling Analytical applications Segmentation Profiling Personalization Profitability analysis Needs analysis Sales analysis Campaign analysis Etc. Solutions database Data warehousing Customer Information on demand Field Service Self-Service E-Commerce Campaign Management Saito, Umemoto and Ikeda 2006 Focus groups Operational CRM Analytical CRM 13
Slide 14: Linking KM technologies to strategy  A KM program is strategic if it includes:   A knowledge strategy that defines knowledge intents KM initiatives that support those knowledge intents  KM initiatives are inherently associated with particular approaches to KM Personalization Knowledge creation through personalization Knowledge transfer through personalization Creation Codification Knowledge creation through codification Knowledge transfer through codification 14 Saito, Umemoto and Ikeda 2006 Transfer Four generic modes of KM support for strategy
Slide 15: KM component technologies Personalization Collaboration Connectivity Communication Authoring Collaboration Community Creativity Workflow Codification Discovery Storage Search Analytics Data mining Text mining Web mining Visualization Creation Dissemination Connectivity Communication Authoring Distribution E-learning Collaboration Community Repository Connectivity Storage Authoring Search Workflow Organization Reasoning Saito, Umemoto and Ikeda 2006 Transfer 15
Slide 16: KM applications Personalization Collaboration Codification Discovery Creation Group support Project management Community support Dissemination Decision support Discovery & data mining Search & organization Repository Transfer Enterprise portals Learning management Expertise management Document management Content management Process management Saito, Umemoto and Ikeda 2006 16
Slide 17: An ontology of KM technologies Saito, Umemoto and Ikeda 2006 17
Slide 18: Conclusions  A wide range of technologies can support KM   Three basic categories: component technologies, KM apps and business apps with KM functionality KM applications summarize KM functionality  KM technologies are linked to strategy through KM initiatives that support specific knowledge intents There are four generic modes of technological support for strategy in KM 18  Saito, Umemoto and Ikeda 2006
Slide 19: Some implications  For research   KM technologies can be better analyzed in the context of KM initiatives instead of knowledge processes There seems to be exemplary KM initiatives that connect specific knowledge intents to typical approaches to KM and KM technologies Guidance in the design of particular KM strategies Guidance in the selection of adequate KM technologies for particular KM initiatives 19  For practice   Saito, Umemoto and Ikeda 2006