Knowledge Management


The objectives of this chapter are to:

  • Identify important dimensions of knowledge
  • Explain what is meant by knowledge management
  • Describe three types of knowledge management systems;
    • Enterprise knowledge management systems
    • Knowledge work systems
    • Intelligent systems and techniques
  • Identify the main challenges to implementing knowledge management systems


There has been strong growth in knowledge management and investment in knowledge management systems. Knowledge management has become an important subject at many large firms as managers realise that much of their firm’s value depends on their ability to create and manage knowledge. Creating and using knowledge is not limited to informationbased companies. It is essential for all organisations, regardless of industry or sector to embrace knowledge management. It’s not enough to make good products; companies must make products that are better, less expensive to produce and more desirable than those of their competitors’. Using corporate and individual knowledge assets will help companies’ fulfil these requirements.


Data Information and Knowledge

Knowledge is different from data and information. Data are collections of facts and measurements, while information is organised or processed data.  Data by itself has no meaning but is the first step in the creation of knowledge. Knowledge includes concepts, experience and insight that provide a framework for creating, evaluating and using information. Wisdom is the collective and individual experience of applying knowledge to the solution of problems. Knowledge can be explicit or tacit. Explicit knowledge is knowledge that has been documented (codified) and can be made available to others. Explicit knowledge includes procedures, guides, reports, guides, policies, etc. A process description is an example of explicit knowledge. Tacit (implicit) knowledge is the expertise, experience, skills know-how, understanding, insights and learning of organisational members that has not been formally documented. Tacit knowledge is slow, difficult and costly to capture and document as it tends to be very personalised.

What is Knowledge Management?

Knowledge management is the set of processes developed in an organisation to create, gather, store, maintain, disseminate and apply the firm’s knowledge. Knowledge management promotes organisational learning as it defines and makes explicit the organisation’s knowledge base. Knowledge management enables the organisation to learn from its environment and incorporate this new knowledge into its business processes. Knowledge management systems facilitate the creation and support of knowledge networks, knowledge repositories and communities of practice.  Moreover, knowledge networks enable people to be linked, so that experts in a given area can be easily identified and share tacit knowledge.  Knowledge management streamlines the workflow and provides tools for creating a knowledge repository.

The knowledge management cycle includes four main steps that transform data and information into usable knowledge (see Figure 10.1).




Knowledge comes from a variety of sources. Companies are using more sophisticated technologies to gather information and knowledge from emails, transaction-processing systems, and outside sources such as news reports and government statistical data. It’s important to remember that while there are many internal sources of knowledge some knowledge should come from external sources. This is important as it brings new knowledge into the company and helps inform the organisation of the changes that are happening in its external environment.


As knowledge is created and captured it must be stored efficiently and effectively and in a way that allows it to be accessed by others. Document management systems are an easy way to digitise, index and tag documents so that employees can retrieve them without too much difficulty. For a knowledge system to be effective employees and management need to support and contribute and not feel threatened by it. All the people in the organisation need to realise how important a resource a knowledge management system is.


Once the system has acquired and stored the knowledge, it must be made straightforward and efficient for employees to access the knowledge. People often complain nowadays of having too much information. The organisation needs to make knowledge available in a useful format for whoever needs it, when the need it and wherever it is needed. If not it will be ignored or under-utilised.


If the organisation is to gain business benefit from its investment in knowledge management then employees and managers needs to apply the knowledge. The more people that apply the knowledge to solve organisational problems the greater the benefit that accrues.  One way to ensure the knowledge is applied is to build the knowledge dissemination into every functional area and every system used throughout the organisation. As old information systems are upgraded or new ones deployed, attention must be given to how knowledge can be drawn into them. The digital firm also needs to explore how it can use the knowledge system to build new processes for its suppliers and employees or new products for its customers.

Types of Knowledge Management Systems

Laudon & Laudon, (2010) identified three main categories of knowledge management systems as follows:

  • Enterprise knowledge management systems
  • Knowledge work systems
  • Intelligent systems and techniques.

Enterprise knowledge management systems are integrated systems that acquire, store and disseminate knowledge across the organisation. These systems provide databases and tools for organising and storing structured and unstructured documents and other knowledge objects, directories, as well as tools for locating employees with particular expertise. These systems includes knowledge network systems, email systems, office work systems, group ware and collaboration tools, and document management systems

The structured knowledge systems were the first to capture knowledge and making it easily available to a wider range of people inside the organisation. These were effectively document management systems.

As people started using newer forms of communications such as emails, voice mail, and digital reports, graphics and presentations, organisations had to adapt their systems to accommodate for this semi-structured knowledge. These semi-structured knowledge systems sat on top of the more rigidly structured knowledge systems to incorporate a wider range of information. These systems are also referred to as digital asset management systems.

Organisations can create a centralised knowledge repository by building upon document management systems and including information from the structured and semi-structured knowledge systems. The knowledge repository is then easily accessed by employees throughout the organisation. However it also needs to be properly managed by a senior person in the organisation who is responsible for the firms’ knowledge management program.  Because it’s simply too expensive and too time-consuming to try to capture all the organisations knowledge, firms are turning to knowledge networks systems in an attempt to link those who hold the knowledge with those that need the knowledge. Employees who have the tacit knowledge about a product, service or process in their head (expertise) need to be connected with those employees who need this knowledge. Users are easily connected to the experts through these networks and can communicate and collaborate on a variety of subjects.


There are three main categories on enterprise-wide knowledge management system: 1. Structured Knowledge Systems

  • Semi-structured Knowledge Systems
  • Knowledge Network System

Structured Knowledge Systems

Structured knowledge is knowledge that has been captured and recorded in structured documents and reports.

Businesses have realised over the years that most problems or situations are in most cases new versions of previously experienced difficulties. By creating structured knowledge systems, employees can research how the problem was solved in the past and can then adapt the old solution to the current situation. This saves time, money, and frustration. It also allows the organisation to re-use solutions to previous problems instead of trying to create a new solution every time.

A structured knowledge system organises structured knowledge in a repository where it can be accessed throughout the organisation. The capabilities of this system include being able to develop large online databases with case-based rules that employees can easily access.

Semi-structured Knowledge Systems

A semi-structured knowledge system is a system for organising and storing less structured information, such as e-mail, voice mail, videos, graphics and brochures. A centralised repository can be created to pull data from employees, customers, partners, and suppliers and feed it back into the company through a portal. E-mails are also codified using case-based rules that allow for easy searching.

Note: The (U.S) Sarbanes-Oxley Act of 2002 requires financial service firms to maintain all forms of communications. It also meant that new knowledge management systems were required by companies in order to comply with the law.

Classifying and Tagging Organising Knowledge

Taxonomy is a scheme for classifying information and knowledge in such a way that it can be easily accessed. Each firm has to develop its own taxonomy to classify documents. Once knowledge taxonomy is produced, documents are tagged with the proper classification (generally using XML tags); then the documents can be retrieved through a Web-based system. There are several tools available that perform “auto tagging” and reduce the need for managers to develop their own unique taxonomies. These tools identify key phrases in documents that can be used to assign appropriate tags. The documents are then organised into categories and the tags are created.

Knowledge Network System 

A major problem for organisations is the difficulties they experience in accessing undocumented knowledge. Because knowledge cannot be conveniently found, employees use up significant time and energy rediscovering knowledge.

Knowledge network systems seek to turn tacit unstructured and undocumented knowledge into explicit knowledge that can be stored in a database. Knowledge networks provide an online directory of corporate experts in well-defined knowledge domains and use communication technologies to make it easy for employees to find the appropriate expert in a company. Solutions that are developed by experts and others in the firm are added to the knowledge database. This new knowledge can be stored as an answer in a database of frequently asked questions. Figure 10.2 shows the basic components of a knowledge network system.




Many of the systems discussed in the previous section centred on how to collect, store, distribute and apply knowledge. In this section we look at systems that can be used by those classified as knowledge workers to create knowledge.

Knowledge Workers and Knowledge Work

Knowledge workers include researchers, designers, architects, scientists and engineers who create knowledge and information for the organisation. Knowledge workers will usually have high levels of education. Knowledge workers perform three key roles in an organisation:

  • They keep the organisation up to date in knowledge as it develops in the external world; in technology, science and the arts. They monitor the changes taking place, identifying opportunities and threats.
  • They serve as internal consultants in the areas appropriate to their knowledge.
  • They act as change agents; appraising, initiating, and promoting change projects.

Knowledge workers will rely on office systems, such as word processors, voice mail, e-mail and video conferencing systems, which are designed to increase worker productivity in the office. However knowledge workers also require specialised knowledge work systems. These knowledge work systems are designed to support the creation of knowledge and to ensure that new knowledge and technical expertise are properly integrated into the business and made available to others.

Knowledge Work Systems

Knowledge work systems provide knowledge workers with the specialised tools they need, which include: • Graphics tools

  • Analytical tools
  • Communication tools
  • Document management tools
  • User friendly interfaces

They must have adequate computing power to handle the specialised tasks and complex calculations, provide easy access to external databases to support research, and present a user-friendly interface. These systems highlight the special needs of knowledge workers.

Examples of Knowledge Work Systems

Laudon and Laudon, (2010) identified the following examples of knowledge work systems:

  • Computer-aided design (CAD) tools automate the creation and revision of designs, using computers and sophisticated graphics software. CAD applications are used by design engineers to build new products or improve old ones. Modern CAD systems have significantly reduced the time required to design new cars and airplanes and are ultimately saving the car companies and aircraft manufactures millions.
  • Virtual reality systems have sophisticated visualisation, and simulation capabilities that go far beyond conventional CAD systems. They use computer generated simulations that attempt to be as close to reality as possible. In many virtual reality systems users are required to wear special equipment that records the user’s movements and feeds them back to the computer so that it can plan its responses to the user input. Virtual reality is beginning to provide benefits in educational, scientific and business.
  • VRML (Virtual Reality Modelling Language) is a set of specifications for interactive 3-D modelling on the Web. Some companies are putting their training systems on the Internet so that people can have access to the latest information and can use it when they need it. Some Web sites use Java applets to help process the programs on the local workstation.
  • Investment workstations: These are used in the financial sector to analyse trading situations instantaneously and facilitate portfolio management.



Artificial intelligence (AI) technology consists of computer-based systems (hardware and software) that attempt to emulate intelligent human behaviour. Such systems are able to learn languages, accomplish physical tasks, and emulate human expertise and decision-making. While AI systems are limited to very narrow domains they play an important role in modernday knowledge management.

Artificial intelligence and database technology provide a number of intelligent systems and techniques that organisations can use to capture individual and collective knowledge and to extend their knowledge base. Expert systems, case based reasoning, and fuzzy logic are used for capturing tacit knowledge. Neural networks and data mining are used for knowledge discovery. These can discover underlying patterns, categories, and behaviours in large quantities of data that could not be discovered by managers alone or simply through experience. Genetic algorithms have the ability to search for solutions to problems that are too large and complex for human beings to analyse on their own. Intelligent agents can automate routine tasks to help firms search and filter information for use in electronic commerce, supply chain management and other activities.

Data mining, which is discussed in Chapter 4, helps organisations capture undiscovered knowledge hidden in large databases, providing managers with new insights into problems for improving business performance. Data mining is also an important tool for management decision-making.

The following intelligent systems and techniques are discussed here: • Expert Systems

  • Organisational Intelligence: Case-Based Reasoning
  • Fuzzy Logic
  • Neural Networks
  • Genetic Algorithms

Expert Systems

An expert system is a system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise. An expert system can be used in the following situations:

  • By non-experts to improve their problem-solving capabilities
  • To propagate scarce knowledge resources for improved consistent results
  • Where the area of expertise is limited to a narrow area (referred to as the domain)
  • As a tool to improve productivity and quality
  • To support strategic decisions

An expert system is a computer program that simulates the judgement and behaviour of a human or an organisation that has expert knowledge and experience in a particular field. Typically, such a system consists of a knowledge base containing the accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program. Sophisticated expert systems can be enhanced with additions to the knowledge base or to the set of rules.

While Expert systems (Figure 10.3) lack the breath of knowledge and the understanding of a human expert, they can provide benefits, helping organisations make high quality decisions with fewer people. Today expert systems are used in business in distinct highly structured decision-making situations.


Figure 10.3: A simplified model of an Expert System

The Components of an Expert System

An expert system will generally contain the following components:

Knowledge base: The knowledge base contains the knowledge of humans experts based on their experiences and knowledge built up over many years. The knowledge base also requires a set of rules that direct the use of the knowledge to solve specific problems in a particular domain.

Inference engine: The inference engine is a computer that draws inferences from the results of applying the user supplied facts to the rules in the knowledge base. It then proceeds to the next fact-rule combination. The inference engine is considered the “brain” of the system.  

User interface: The user interface allows the user to communicate with the system. The system communicates with the user using a question and answer format. This communication drives the inference engine to match the symptoms of the problem with the knowledge in the base so that a conclusion is drawn and a recommendation is made to solve the problem. Explanation facility: This feature of the expert system gives it the ability to explain its recommendation.

Current Data storage: This is a storage area set aside for input data related to the current problem.

Knowledge engineer: The person who pulls the data from the human expert and fits it into the expert system is called the knowledge engineer.

Benefits of an Expert System

The benefits of an expert system include the following:

  • Reduced errors
  • Reduced cost and reduced training time
  • Improved decision making
  • Improved quality and services
  • Improved user and customer satisfaction

Organisational Intelligence: Case-Based Reasoning

Expert systems primarily capture the tacit knowledge of individual experts, but organisations also have collective knowledge and expertise that they have built up over the years. This organisational knowledge can be captured and stored using case-based reasoning systems. In case-based reasoning (CBR), descriptions of past experiences represented as cases, are stored in a database for later retrieval when the user encounters a new case with similar characteristics. The system searches for stored cases with problem characteristics similar to the new one. It finds the closest fit, and applies the solutions of the old case to the new case. Successful solutions are tagged to the new case and both are stored together with the other cases in the knowledge base. Unsuccessful solutions are also added to the case database along with explanations as to why the solutions did not work (See Figure 10.4).

Expert systems work by applying a set of IF-THEN-ELSE rules against a knowledge base, both of which are extracted from human experts. Case-based reasoning, in contrast, represents knowledge as a series of cases, and this knowledge base is continuously updated by users of the system.



Figure 10.4: A simplified model of a Case-based reasoning system

Fuzzy Logic

Fuzzy logic is a rule-based artificial intelligence technology that handles uncertainty, by mimicking the process of human reasoning and allows computers to handle incomplete or ambiguous data. Fuzzy logic represents more closely the way people actually think than traditional IF-THEN rules. Decision making often involve situations that are neither black nor white. They are grey at best with the term fuzzy often being suitable. Fuzzy logic systems are only starting to be applied to business situations.

Neural Networks

Neural networks are systems of programs and data structures that attempt to model the capabilities of the human brain. Neural Networks are an array of interconnected processors operating in parallel in which knowledge is represented by the pattern of interconnections among them and by adjustable weights of these connections. They have good pattern recognition techniques and can identify hidden patterns in data and can also deal with incomplete input. They also have an ability to learn new information and behaviour.

A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes more inputs and categorises them based on the experience of the model.

Difference between Neural Networks and Expert Systems

Table 10.5 provides a summary of the differences between neural networks and expert systems


Table 10.5: Summary of the differences between neural networks and expert systems


Expert Systems Neural Networks
Expert systems           emulate             human decision-making. Neural networks learn human thought processes and reasoning patterns.
Expert systems use rules and frames of reference in which they make their decisions. Neural networks adjust to inputs and outputs.
Expert systems require humans to update their database of knowledge. Neural networks continue to expand their own base of knowledge

Genetic Algorithms

The concept of genetic algorithms was developed by John Holland in the US in the 1970s. The concept of genetic algorithms is taken from nature and is based on the idea of natural selection and genetics. Genetic algorithms are search procedures that can be used to find the optimal solution to a specific problem by searching through a very large number of possible solutions to that problem. Genetic algorithms involve adaptive computation where possible solutions can evolve and can even be combined to form a new population of solutions. As solutions alter and combine, the worst ones are discarded and the better ones survive to go on and produce even better solutions.

Genetic algorithms are particularly suited to the areas of optimisation and search. They are used to solve problems that are complex, changing and usually involve large numbers of variables.


The difficulties of implementing knowledge management systems include:

  • Insufficient resources available to structure and update the stored content
  • Poor quality and high variability of content because of insufficient validation
  • Document and content stores lack context, making documents difficult to understand
  • Individual employees are not rewarded for contributing knowledge, and many are resistant to sharing knowledge with others
  • Search engines return too much information, reflecting lack of knowledge structure or mechanism for tagging documents

Laudon and Laudon, (2010) suggest that for businesses to obtain value for knowledge management systems they should use the following steps:

  • Develop in stages
  • Choose a high-value business process
  • Choose the right audience
  • Measure return on investment during initial implementation
  • Use the result of the measurements to establish the organisational wide values.
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