Paper: KM Tech 2: Ontology of KM technologies

Outline | Introduction | KM strategy | KM technologies | Ontology | Discussion | References

The review of previous studies on technologies’ supporting role to KM revealed three basic categories of KM technologies: component technologies, the building blocks of KM systems; KM applications, the KM-specific systems; and business applications, the business-oriented ones. Furthermore, it showed that understanding KM technologies in terms of knowledge processes can be misleading, since those processes are heavily context-related and dependent on subjective interpretation. We suggest instead to explain them in terms of the four types of contribution to strategy uncovered in the review on KM strategy.

An analysis of the conceptual map on KM strategy depicted in Figure 1 reveals that the key concepts regarding the strategic use of KM technologies are knowledge intents, approaches to KM, and KM initiatives. The map shows that KM technologies are implemented in the context of KM initiatives, which, if strategically designed, adopt a particular approach to KM to achieve specific knowledge intents. Two prominent approaches to KM are personalization and codification, and two generic knowledge intents are knowledge creation and transfer. These approaches and intents can be combined to further describe each of the three basic categories of technologies according to the following types:

• Collaboration technologies, supporting the creation of knowledge according to a personalization approach.
• Dissemination technologies, supporting the transfer of knowledge according to a personalization approach.
• Discovery technologies, supporting the creation of knowledge according to a codification approach.
• Repository technologies, supporting the transfer of knowledge according a codification approach.

The structure of the resulting ontology is shown in the Figure 3. We present following a survey of existing KM technologies according to the proposed ontology.

Take in Figure 3: Structure of the ontology of KM technologies

Component technologies

A comprehensive survey of technologies is a challenging task, since their quantity and variety is astounding. Their integration in multiple levels makes the task even more difficult. We present below a fairly extensive list of component technologies, grouped according to functionality to facilitate understanding. Some of the technologies are fairly common and widespread in organizations, and we could term them infrastructure technologies. Others are more specific, sometimes implemented transparently in other applications, and a few of them are cutting-edge, innovative ones.

• Storage. Databases, repositories, file-servers, data warehouses, data marts, etc.
• Connectivity. Internet, security, authentication, wireless networking, mobile computing, peer-to-peer, etc.
• Communication. E-mail, mailing lists, discussion groups, chat, instant messaging, audio/video conferencing, web seminars, voice over IP, etc.
• Authoring. Office suites, desktop publishing, graphic suites, multimedia, etc.
• Distribution. Web, intranets, extranets, enterprise portals, personalization, syndication, audio/video streaming, etc.
• Search. Search engines, agents, indexing, glossaries, thesauri, taxonomies, ontologies, etc.
• Analytics. Querying, reporting, multi-dimensional analysis (on-line analytical processing - OLAP), etc.
• Workflow. Process modeling, process engines, etc.
• E-learning. Interactive multimedia (computer-based training - CBT), web seminars, simulations, learning objects, etc.
• 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, multi-dimensional 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, case-based reasoning, knowledge-bases, machine learning, fuzzy logic, etc.

This myriad of technologies can support KM in multiple ways, fitting more than one of the collaboration-dissemination-discovery-repository categories. In Table II, we list the functional groupings according to their most relevant types of support.

Take in Table II: KM component technologies

Knowledge management applications

KM applications usually integrate numerous component technologies into systems with well defined functionality. Component technologies are from any of the strategic contribution quadrants, not necessarily the same as the intended system (Figure 4). We describe below the main KM applications found in the survey.

Take in Figure 4 - Component technologies integrated into KM applications

• Document management. Automate the control of electronic documents through their entire life-cycle. Provide functions such as store and archive, categorization, navigation and search, versioning and access control. Some have imaging functions that allow the digitalization of paper documents.
• Content management. Manage the whole Web publishing process. Manage authors and the content creation process, separate content from layout for standardized output, support multimedia repositories, automatic page-generation via templates, and staging of new content.
• Process management. Also known as workflow, automate the flow of tasks and information across business processes. Include workflow engines for handling cases, and tools for modeling processes, accessing external applications, and monitoring and managing operations.
• Group support. Also known as groupware, support the work of groups and teams. Include tools for communication (both synchronous and asynchronous), coordination (like calendaring, meeting support and workflow), and collaboration (file repositories, group decision making).
• Project management. Support the management of project activities and resources. Include functions for defining and organizing activities and tasks, assigning responsibilities and deadlines, allocating personnel and other resources, and identifying milestones, critical paths and constraints.
• Community support. Coordinate interaction in large groups. Include tools for communication and interaction, both synchronous and asynchronous, management of participation levels, including leading and facilitating roles, identity profiling, and collective decision making.
• Decision support. Also known as business intelligence, integrate a series of tools for decision making. Include query and report of operational data, managerial dashboards like the balanced scorecard, and decision models and techniques for structured and unstructured situations.
• Discovery and data mining. Allow the identification of patterns and associations in large amounts of data. Including tools for cleaning and organizing data into data warehouses, and a series of analytical techniques and visualization tools. It is used in a variety of domains, from finance, to customer behavior, to web navigation.
• Search and organization. Facilitate access to and organize unstructured content. Identify key words and topics in documents from varied sources, generate indexes and taxonomies automatically, categorize documents in topics according to relevance, and use domain-specific ontologies for specialized classification.
• Enterprise portals. Integrates access to a wide range of information and systems into a single point of entry. Allow controlled access to operational and managerial applications, and personalized presentation of content, along with workflow management, communication and collaboration.
• Learning management. Support the development and delivery of online courses in a variety of formats, from individual self-paced to group-based instructor led. Include functions like content creation and management, communication and interaction, and assessment and performance reporting.
• Expertise management. Provide expertise brokerage in large communities. Include functions like identification and profiling of experts, communication tools for questioning and answering, rating of answers and experts, and repositories for reusing contributions.

KM applications fit the strategic contribution quadrants better than component technologies. Although each type of KM application has some functionality that fits other quadrants, the main purpose and core function of the application suits well one of them (Table III). Commercial solutions available in the market, though, are offering full-featured KM suites that integrate several KM applications, reflecting the incessant trend towards higher levels of integration.

Take in Table III: KM applications

Business applications

KM systems may also focus specific business processes and functions. KM functionality has usually been included as modules of larger integrated enterprise systems that conquered organizations since the last decade. The first of these was called enterprise resource planning (ERP), and offered integrated control of all operations, from purchasing to manufacturing to sales, including back-office functions like finance and human resources. Soon after came customer relationship management (CRM), integrating marketing, sales and customer service, supply chain management (SCM), integrating suppliers, manufacturers and retailers in the supply chain, and more recently, business intelligence (BI), integrating managerial control and decision making (Turban, McLean & Wetherbe, 2002).

These large integrated enterprise systems are not KM systems per se, but include KM functionality in some of their modules and subsystems. A complete CRM suite (Figure 5), for example, presents KM functionality in all four strategic-contribution quadrants:

• Repository application: a customer representative at the contact center offers customer service using scripted responses for typical cases, which are collected in a database an updated as new problems and solutions arise.
• Discovery application: analytical CRM collects information from all points of contact (sales, contact center, web site) into a data warehouse, allowing analysis and data mining for customer profiling and segmentation.
• Dissemination application: an e-commerce web-site works much like a corporate portal, offering personalized information and e-mail alerts, along with access to some back office systems like inventory, logistics, and accounts receivable.
• Collaboration application: the marketing department may take advantage of user communities or discussion groups to conduct market research, conducting focus groups over the Internet for detecting consumer preferences or testing concepts.

Take in Figure 5: Customer relationship management system architecture

KM functionality in business applications, therefore, are always tied to a particular knowledge domain, usually represented by a business process or organizational function. Thus, it is not possible to categorize the business application themselves in the strategic-contribution quadrants, only specific modules and functions.

Figure 6 shows the ontology with the KM technologies identified in the survey. Since component technologies are too numerous, only the functional categories are included.

Take in Figure 6: Ontology of KM technologies.