km ppt neew one
- 1. KM Tools The scope of this section is to provide the reader with an overview of the types of KM tools available on the market today and to gain an understanding of what their role is in the KM process. This is the most important step, since there are literally thousands of options to choose from. However, in the future, I intend to also take a look at some actual KM tools and present a few reviews. In this section, I present an overview of the IT-based tools and systems that can help knowledge management (KM) fulfill its goals. To recap, I have dealt with KM tools throughout the section on tactical management initiatives, outlining its role in knowledge discovery, organization, sharing, etc. In the section on knowledge management strategy, I presented an article on knowledge management systems implementation, where I stated that IT based tools, for the most part, fall into one of the following categories .. Groupware systems & KM 2.0 The intranet and extranet Data warehousing , data mining, & OLAP Decision Support Systems Content management systems Document management systems
- 2. Groupware Systems & KM 2.0 Groupware is a term that refers to technology designed to help people collaborate and includes a wide range of applications. Wikipedia defines three handy categories for groupware: Communication tools: Tools for sending messages and files, including email, web publishing, wikis, file sharing, etc. Conferencing tools: e.g. video/audio conferencing, chat, forums, etc. Collaborative management tools: Tools for managing group activities, e.g. project management systems, workflow systems, information management systems, etc.
- 3. The Intranet & Extranet The intranet is essentially a small-scale version of the internet, operating with similar functionality, but existing solely within the firm. Like the internet, the intranet uses network technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP). It allows for the creation of internal networks with common internet applications that can allow them to communicate with different operating systems.. Although it need not be, the intranet is usually linked to the internet, where broader searches are implemented. However, outsiders are excluded through security measures such as firewalls. Extranet The extranet is an extension of the intranet to the firm's external network, including partners, suppliers and so on. The term is sometimes used to refer to a supplementary system working alongside the intranet or to a part of the intranet that is made available to certain external users.
- 4. Warehousing Data: The Data Warehouse, Data Mining, and OLAP Warehousing data is based on the premise that the quality of a manager's decisions is based, at least in part , on the quality of his information. The goal of storing data in a centralized system is thus to have the means to provide them with the right building blocks for sound information and knowledge. Data warehouses contain information ranging from measurements of performance to competitive intelligence (Tanler1997).
- 5. Warehousing Data: Design and Implementation anler (1997) identifies three stages in the design and implementation of the data warehouse. The first stage is largely concerned with identifying the critical success factors of the enterprise, so as to determine the focus of the systems applied to the warehouse. The next step is to identify the information needs of the decision makers. This involves the specification of current information lacks and the stages of the decision-making process (i.e. the time taken to analyze data and arrive at a decision). Finally, warehousing data should be implemented in a way that ensures that users understand the benefit early on. The size of the database and the complexity of the analytical requirements must be determined. Deployment issues, such as how users will receive the information, how routine decisions must be automated, and how users with varying technical skills can access the data, must be addressed. According to Frank (2002), the success of the implementation of the data warehouse depends on: Accurately specifying user information needs Implementing metadata: Metadata is essentially data about data. This is regarded as a particularly crucial step. Parankusham & Madupu (2006) outline the different roles of met a data as including: data characterization and indexing, the facilitation or restriction of data access, and the determination of the source and currency of data.
- 6. OLAP OLAP allows three functions to be carried out. Query and reporting: Ability to formulate queries without having to use the database programming language. Multidimensional analysis: The ability to carry out analyses from multiple perspectives. Tanler (1997) provides an example of a product analysis that can be then repeated for each market segment. This allows for quick comparison of data relationships from different areas (e.g. by location, time, etc.). This analysis can include customers, markets, products, and so on, Statistical analysis: This function attempts to reduce the large quantities of data into formulas that capture the answer to the query. OLAP is basically responsible for telling the user what happened to the organization . It thus enhances understanding reactively, using summarization of data and information. What is Data Mining? This is another process used to try to create useable knowledge or information from data warehousing. Data mining, unlike statistical analysis, does not start with a preconceived hypothesis about the data, and the technique is more suited for heterogeneous databases and date sets (Bali et al 2009). Karahoca and Ponce (2009) describe data mining as "an important tool for the mission critical applications to minimize, filter, extract or transform large databases or datasets into summarized information and exploring hidden patterns in knowledge discovery (KD)." The knowledge discovery aspect is emphasized by Bali et al (2009), since the management of this new knowledge falls within the KM discipline. th is beyond the scope of this site to offer an in-depth look at the data mining process. Instead, I will present a very brief overview, and point readers that are interested in the technical aspects towards free sources of information. Very briefly, data mining employs a wide range of tools and systems, including symbolic methods and statistical analysis. According to Botha et al (2008), symbolic methods look for pattern primitives by using pattern description languages so as to find structure. Statistical methods on the other hand measure and plot important characteristics, which are then divided into classes and clusters. Data mining is a very complex process with different process models.
- 7. Decision Support Systems There are several kinds of such systems, however, in this subsection I will look at only at data-driven decision support systems (from now on referred to solely as decision support systems). The role of these systems is to access and manipulate data. They usually work with a data warehouse, use an online analytical processing system (OLAP), and employ data mining techniques. The goal is to enhance decision-making and solve problems by working with the manager rather than replacing him. An effective decision support system thus requires that the organization: Investigates the decisions made within their firm Compares these decisions with KM activities Evaluates any current decision support system in light of this Modifies said system if necessary
- 8. Content Management Systems Content management systems are very relevant to knowledge management (KM) since they are responsible for the creation, management, and distribution of content on the intranet, extranet, or a website. Content management is a discipline in itself, so this section will be relatively brief, only outlining the basic considerations. A content management system may have the following functions: Provide templates for publishing: Making publishing easier and more consistent with existing structure/design. Tag content with metadata: I.e. Allowing the input of data that classifies content (e.g. keywords) so that it can be searched for and retrieved. Make it easy to edit content Version control: Tracking changes to pages and, if necessary, allowing previous versions to be accessed Allow for collaborative work on content Integrated document management systems Workflow management: Allowing for parallel content development Provide extensions and plug-ins for increased functionality Etc.
- 9. Document Management Systems Document management systems, as the name implies, are systems that aid in the publishing, storage, indexing, and retrieval of documents. Although such systems deal almost exclusively with explicit knowledge, the sheer volume of documents that an organization has to deal with makes them useful and in some cases even mandatory. Often they are a part of content management systems. Usually, a document management system will include the following functions: Capturing: In order for paper documents to be useable by the document management system, they must be scanned in. For companies that need to carry out this process and who have numerous paper documents this may be time consuming and expensive. Classification using metadata: Metadata (data about data) is used to identify the document so that it can be retrieved later. It can include keywords, date, author, etc. The user is often asked to input this metadata or the system may extract it from the document. Optical character recognition may be used to identify text on scanned images.