OICT summary
College 9 Knowledge management
Knowledge management refers to the production and services based on knowledge-intensive
activities that contribute to an accelerated pace of technical and scientific advance as well as rapid
obsolescence. There is greater reliance on intellectual capabilities than on physical inputs for
example. There are many efforts to integrate improvements in every state of a production process.
Methods to conduct a project, processes to produce a product, financing new projects or the
recruitment of talented personnel are all examples of knowledge. They have the purpose of
providing competitive advantage to a company. Knowledge increases when it is applied (differs from
capital goods which wears out over time). The law of diminishing returns does not apply to
knowledge generation. Knowledge can be created out of nothing and can be sold many times. A
capital good is a durable good that is used in the production of goods and services.
Knowledge management refers to the set of business processes developed in an organization to
acquire, store, disseminate (share/transfer) and apply knowledge. It increases the ability of the
organization to learn from its environment and to incorporate knowledge into its business processes.
There are different types of knowledge:
1. Explicit: Knowledge is expressed into words, numbers and graphics. The knowledge is mostly
facts and it represents the ‘know that’.
2. Tacit: Knowledge is expressed as an experience, insights, intuitions and hunches. It is based
on skills and on the minds of people. It represents the ‘know how’.
3. Structured: Knowledge is mostly stored in reports and presentations. It consists of formal
rules.
4. Semi-structured: Knowledge is stored in videos, pictures or e-mails.
5. Unstructured tacit knowledge: Knowledge is in the minds of the workers and it is very
imprecise.
Often, business content is semi-structured or unstructured.
There are several value-adding steps in the
knowledge management cycle. The cycle requires
the support of tools called knowledge
management systems. Knowledge is very related
to technology but the focus is on humans. Knowing
is a human act as it involves thinking. Knowledge
management can be separated from technology.
There are two perspectives on knowledge
management. One is personalisation which is the
flow approach. This focuses on the interaction
between people, ICT support and the fact that
people have to knowledge (tacit knowledge). The other focuses on codification which is the stock
approach. This focuses on storable and transferable knowledge and it states that ICT is leading and
that knowledge is stored in systems (explicit knowledge).
There are many knowledge management systems such as:
1. Business intelligence systems à acquire and create
2. Communications and networking à transfer
3. Enterprise content management systems à store
4. Intelligent systems à apply
, 5. Knowledge work systems à create and apply
Enterprise content management systems need knowledge repositories which contain organizational
knowledge such as documents or wikis. Search and retrieval functionality is essential for keywords,
full text search, semantic work etc.
Repositories need structuring knowledge which gives a meaningful structure to knowledge to help
humans and computers in finding the right knowledge. It also needs adding knowledge which focuses
on who can add what? How easy is it to add knowledge and what are the incentives to add?
Retrieving knowledge focuses on how easy is it to browse and find knowledge and what are the
incentives to retrieve?
There are two options for structuring knowledge:
1. Taxonomies: Taxonomies are classifications of objects such as documents or knowledge
types. They are trees of concepts and are usually restricted to is a relations. Taxonomies only
allow for a single link between classes instances. If you want more relations, a semantic
network is needed.
2. Semantic networks: These networks are structures where objects are part of classes, have
attributes and are linked by meaningful links. There is a ‘is a’ relation between classes and
individuals. It is a network with meaningful links. Topic maps are semantic networks defined
according to specific standards and technologies. Building a network includes tagging
(assigning a keyword to information) and linking (indicating relationships). Manual tagging
and linking is time consuming.
An expert system is an intelligent technique for capturing tacit knowledge in a very specific and
limited domain of human expertise. It captures the knowledge of skilled employees in the form of a
set of rules in a software system that can be used by others in the organization. The set of rules adds
to the memory or stored learning of the firm.
College 10 Foundations of business intelligence
Business intelligence is a set of techniques and tools for the transformation of raw data into
meaningful and useful information for business analysis purposes. BI technologies are capable of
handling large amounts of unstructured data to help identify, develop and create
new strategic business opportunities. Business intelligence systems are build on
top of databases. Business intelligence is typically located in the executives and
senior management levels. Executives make use of executive information
systems and senior managers of decision support systems.
A database is a group of related files. A file is a group of records of the same type.
A record is a group of related fields and a field is a group of characters as word(s)
or numbers. A byte is a group of eight bits and a bit is the smallest unit of data.
The traditional approach encourages each functional area in a corporation to
develop specialised applications. Each field like accounting and finance or human
resources has their own application program and each have specific files. There
can occur some problems when files are maintained separately by different
departments:
1. Data redundancy: The presence of duplicate in multiple files.
2. Data inconsistency: The same attribute has different values in different depts.
3. Program-data dependence: When changes in a program require changes to data accessed by
the program.
4. Lack of flexibility: When requests for reports take long to be implemented for example.
5. Poor security: It is difficult to audit access to data.
College 9 Knowledge management
Knowledge management refers to the production and services based on knowledge-intensive
activities that contribute to an accelerated pace of technical and scientific advance as well as rapid
obsolescence. There is greater reliance on intellectual capabilities than on physical inputs for
example. There are many efforts to integrate improvements in every state of a production process.
Methods to conduct a project, processes to produce a product, financing new projects or the
recruitment of talented personnel are all examples of knowledge. They have the purpose of
providing competitive advantage to a company. Knowledge increases when it is applied (differs from
capital goods which wears out over time). The law of diminishing returns does not apply to
knowledge generation. Knowledge can be created out of nothing and can be sold many times. A
capital good is a durable good that is used in the production of goods and services.
Knowledge management refers to the set of business processes developed in an organization to
acquire, store, disseminate (share/transfer) and apply knowledge. It increases the ability of the
organization to learn from its environment and to incorporate knowledge into its business processes.
There are different types of knowledge:
1. Explicit: Knowledge is expressed into words, numbers and graphics. The knowledge is mostly
facts and it represents the ‘know that’.
2. Tacit: Knowledge is expressed as an experience, insights, intuitions and hunches. It is based
on skills and on the minds of people. It represents the ‘know how’.
3. Structured: Knowledge is mostly stored in reports and presentations. It consists of formal
rules.
4. Semi-structured: Knowledge is stored in videos, pictures or e-mails.
5. Unstructured tacit knowledge: Knowledge is in the minds of the workers and it is very
imprecise.
Often, business content is semi-structured or unstructured.
There are several value-adding steps in the
knowledge management cycle. The cycle requires
the support of tools called knowledge
management systems. Knowledge is very related
to technology but the focus is on humans. Knowing
is a human act as it involves thinking. Knowledge
management can be separated from technology.
There are two perspectives on knowledge
management. One is personalisation which is the
flow approach. This focuses on the interaction
between people, ICT support and the fact that
people have to knowledge (tacit knowledge). The other focuses on codification which is the stock
approach. This focuses on storable and transferable knowledge and it states that ICT is leading and
that knowledge is stored in systems (explicit knowledge).
There are many knowledge management systems such as:
1. Business intelligence systems à acquire and create
2. Communications and networking à transfer
3. Enterprise content management systems à store
4. Intelligent systems à apply
, 5. Knowledge work systems à create and apply
Enterprise content management systems need knowledge repositories which contain organizational
knowledge such as documents or wikis. Search and retrieval functionality is essential for keywords,
full text search, semantic work etc.
Repositories need structuring knowledge which gives a meaningful structure to knowledge to help
humans and computers in finding the right knowledge. It also needs adding knowledge which focuses
on who can add what? How easy is it to add knowledge and what are the incentives to add?
Retrieving knowledge focuses on how easy is it to browse and find knowledge and what are the
incentives to retrieve?
There are two options for structuring knowledge:
1. Taxonomies: Taxonomies are classifications of objects such as documents or knowledge
types. They are trees of concepts and are usually restricted to is a relations. Taxonomies only
allow for a single link between classes instances. If you want more relations, a semantic
network is needed.
2. Semantic networks: These networks are structures where objects are part of classes, have
attributes and are linked by meaningful links. There is a ‘is a’ relation between classes and
individuals. It is a network with meaningful links. Topic maps are semantic networks defined
according to specific standards and technologies. Building a network includes tagging
(assigning a keyword to information) and linking (indicating relationships). Manual tagging
and linking is time consuming.
An expert system is an intelligent technique for capturing tacit knowledge in a very specific and
limited domain of human expertise. It captures the knowledge of skilled employees in the form of a
set of rules in a software system that can be used by others in the organization. The set of rules adds
to the memory or stored learning of the firm.
College 10 Foundations of business intelligence
Business intelligence is a set of techniques and tools for the transformation of raw data into
meaningful and useful information for business analysis purposes. BI technologies are capable of
handling large amounts of unstructured data to help identify, develop and create
new strategic business opportunities. Business intelligence systems are build on
top of databases. Business intelligence is typically located in the executives and
senior management levels. Executives make use of executive information
systems and senior managers of decision support systems.
A database is a group of related files. A file is a group of records of the same type.
A record is a group of related fields and a field is a group of characters as word(s)
or numbers. A byte is a group of eight bits and a bit is the smallest unit of data.
The traditional approach encourages each functional area in a corporation to
develop specialised applications. Each field like accounting and finance or human
resources has their own application program and each have specific files. There
can occur some problems when files are maintained separately by different
departments:
1. Data redundancy: The presence of duplicate in multiple files.
2. Data inconsistency: The same attribute has different values in different depts.
3. Program-data dependence: When changes in a program require changes to data accessed by
the program.
4. Lack of flexibility: When requests for reports take long to be implemented for example.
5. Poor security: It is difficult to audit access to data.
