Transcript Slide 1

Decision Support &
Executive Information
Systems:
Week 7
Amare Michael Desta
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Data, Information and
Knowledge is needed to …
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To manage internal operations
React to the external environment, to
potential threats and opportunities
Manage/Minimise risk
Generate knowledge, ideas and,
through this,
- New way(s) of doing things and
- New Products & Services may be achieved
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The Naïve View
Data is what we find in databases
- But how does the database ‘know’ what data
to hold?
Information is what we find in IS and it allow us
to ask questions of the data.
- But how does the information system ‘know’
what questions we will want to ask?
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Data & Data Values
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Data – that which is given
In problem solving (decision making)
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What is known or assumed to be true
Typically a member or members of one or
more collection or sets of ‘objects’
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E.g. in Mathematics – Given a line and a point not
on that line …
Data = any one individual member of the set of
lines and any one individual member of the set of
points that satisfies the condition.
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Data & Data Values
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In engineering we move from the
individual to the particular
From the mathematical concept of a
line to the practical realisation of this
particular line from here to there.
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Relational Data Tables
R#
Make
Model
Colour
Yellow
Red
etc.
Primary Key
{
Make Model Colour
ABC
XYZ
Ford
Ford
Escort
Escort
Red
Blue
PQR
GM
Astra
White
340
Red
LMN Volvo
Tuples
Cardinality
Relation
R#
}
Domains
Attributes
Degree
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Data, Measurement and
Observation
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A chicken and egg situation
There can be no observation without
knowledge
We have to decide what something is –
to categorise it before we can measure
it and record the data values.
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Reason
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Reason derives from the same root
meaning as ratio and also connected
with relation
Connected to the idea of the balance
To weigh the evidence
To put things in proportion
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Decision Theory 1
Decisions consist of:
 A set of possible courses of action
 A set of outcomes form each action
 A state of the world
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Decision Theory 2
Decision making contexts can be
classified in terms of the Information and
knowledge available
- Certainty
- Risk
- Uncertainty
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Rationality
When modelling peoples behaviour
economists and management scientists usually
assume that people are rational
which means that:
- They always choose their best option the one
that maximises their payoff
- Which means they have the knowledge and
ability to determine what their best option is!
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Bounded Rationality
Problems with assuming rational actors
- It is very easy to provide counter examples
from experience
- Most people are not in possession of enough
information (data) to determine what their
best option is
- Most people do not have the necessary
knowledge to determine their best option
even given the necessary information
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Bounded Rationality
Herbert Simon introduced the term
bounded rationality as a more realistic
view of decision making:
- BR is NOT irrationality
- Actors make the best decision (act rationally)
given
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limited information
Limited knowledge
Limited resources
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Learning & Knowing process
Requires an understanding of:
 Know who
 Know where
 Know when
 Know what
 Know about
 Know how
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Learning & Knowing 2
Categorisation & Knowledge
- Similarity – common properties
- Difference – distinct properties
- Contiguity – at the same place and or
at the same time
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Knowledge Management in
Organisations
Knowledge Management, (KM), is:
Systemically and actively managing and
leveraging stores of knowledge in
organisation
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Knowledge Management
Systems, (KMS)
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KMS – are sophisticated decision
support systems
KMS – Support Decision Support
Systems
KMS – Are systems for managing the
knowledge processes of an organisation
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Information and Knowledge Work
Systems
DATA WORKERS: People who process &
Disseminate organization’s paperwork
INFORMATION WORK: Work consists
primarily of creating, processing
information
KNOWLEDGE WORKERS: People who
design products or services or create new
knowledge for organization
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Knowledge Processes 1
A Mechanistic View
People as Computers
 Creation
 Acquisition
 Transmission
 Storage
 Retrieval
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Knowledge Management and IT
NETWORKS
DATABASES
SHARE
KNOWLEDGE
DISTRIBUTE
KNOWLEDGE
GROUP
COLLABORATION
SYSTEMS
OFFICE
AUTOMATION
SYSTEMS
ARTIFICIAL
INTELLIGENCE
SYSTEMS
KNOWLEDGE
WORK
SYSTEMS
CAPTURE,
CODIFY
KNOWLEDGE
PROCESSORS
SOFTWARE
CREATE
KNOWLEDGE
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Organisational Knowledge
Codified
Situated
Individual
Collective
Explicit; knowledge is known by an
individual, who knows that (s)he
knows it, and can explain what (s)he
knows to others.
Migratory; knowledge is possessed
by a group in the nature of their
roles, interacts, methods, procedures
and routines that can be documented
and copied.
Tacit; knowledge is known to an
individual who may or may not
know that (s)he knows it but cannot
explain what it is (s)he knows.
Embedded; knowledge is possessed
by a group in terms of the nature of
its members and their relationships
that can only be learned by
becoming a member of the group.
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Bohn’s Stages of Knowledge
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Data, Information & Knowledge in Use
Data
Information
Knowledge
1840KL0617
The KLM flight
leaves Detroit at
18:40
That’s not a good
flight; often busy
and delayed
Relationships and trust are required for knowledge
transfer and reuse
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Relationship of Data,
Information & Knowledge
Prior Expectation
The World
Data
Data
Filters
Information
The Agent’s
Knowledge
Base
Knowledge: a disposition to act
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Processing Hierarchy
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Centrality of data
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(Wilson 1996)
Does data always
lead to information?
Does information
always lead to
knowledge?
And where does
good judgement
come from?
Action
Decision
Knowledge
Information
Data
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Data Systems & Knowledge
Intelligence in Data Processing Systems
Processing
Data
Collection
Data
Entry
Report USERS
Manipulation
Knowledge is a pervasive characteristic of information
systems
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Data & Information: System
Perspective
Decoding
Encoding
Data
Information
Receiver
Sender
Computer System
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Information Systems
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Information systems process data and turn it into information
that can be used for management decision-making
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Knowledge is used to design, encode and operate IS
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Knowledge is needed to decode the information that comes out
of IS
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IS professionals need to understand the human (perceptual)
processes involved in the encoding and decoding process
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Data, Information & Knowledge: Linear
Models
Usual Approach
Data
Information Knowledge
Actions
Results
Benefits-Driven
Approach
Data, Information & Knowledge Cyclic
Model
Accumulate
Knowledge
(Experience)
Knowledge
Data
Format,
Information
Filter
Summarise
Interpret,
Decide, Results
Act
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Information & Knowledge: Sharing &
Transmission
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Information
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Capture, creation and dissemination
Releasing the Value
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by use and re-use
Knowledge – transmission(s)
 Explicit to Explicit
 Tacit to explicit
 Explicit to tacit.
 Tacit to Tacit.
Nonanka (1991)
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Information & Quality – the main
issues
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Accurate
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Meaningful
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Out of date information is misleading if not useless.
Available
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to user
Up to date - information is very time
sensitive.
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Appropriate detail and accuracy for the user
at point of need/use. related to decision-maker's context
Complete and comprehensive
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Providers the receiver with all they need to know
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Information & Quality
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Format
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in a form that aids assimilation.
Cost effective
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costs of production and delivery lower than the
benefits derived.
e.g. a decision taken on the basis of the information
provided could result in reduced costs, increased
sales/revenue, better utilisation of resources
Must be secure BUT
.... the potential value of information
depends on its quality.
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Historical Trends
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Massive structural shifts in Western economies
Shift in Importance
Knowledge
Information
Data
Represented in Technology
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The Changing Economic Eras
Era
Variables
Strategic Factors
of Economic
Growth
Organisational
Factors
Predominant
Consumer Goods
Agricultural
Industrial
Land
Capacity of
Industrial
Production
Hierarchy (Land
Owner)
Food
Technology
Agricultural
Predominant
Resource
Workforce
Post-industrial or
Information
Information
Blue Collar
White Collar
Bureaucracy
Bureaucracy
(Factory Owner, (Administrators, IT
Trade Union
Managers
Leaders )
Agricultural Goods,
Information &
House & Clothing
Communications
Services &
Products
Manufacturing &
Information &
Engineering
Communications
Physical Sources of
Energy
Information
Knowledge
Knowledge
Collaboration
(Communities)
Intellectual
Products &
Services
Technology for
Learning,
Innovating,
Consulting,
Collaborating
Ideas
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The Shift to Information & K Work
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Shift away from agriculture and manufacturing to services
Outsourcing of manufacturing to the Developing World.
Trend towards knowledge-based manufacturing
Increased growth in knowledge-based products and knowledgeenhanced goods – mobile phones, CD’s, digital photos,
electronic journals
Growth in information and Knowledge-based services,
particularly in advanced economies
Massive growth in information based occupations & knowledge
work. In the US, over 85% population works in services, with
65% in high skilled areas.
Fastest growth areas: education, communications and
information, computing, electronics and aerospace industries
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Key Drivers of Change
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Political Changes
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Business Changes
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Growth of free trade, deregulation, emergence of new
producers, globalisation
Technological Changes
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Collapse of Communism, formation of major economic and
political alliances
Biotechnology, telecommunications and computing
Social Changes
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Stakeholder Society, spread of egalitarian ideal
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The New Economy: Key Points
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Key driver is INNOVATION rather than production efficiency
(quality rather than quantity)
Knowledge is the main source of innovation
Economic wealth depends on knowledge creation, production
and distribution
Organisations are increasingly information and knowledge-based
Workforce is more skilled and knowledgeable
State and employers invest heavily in research and development
in science and technology
Greater investment in education and training
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The Emergence of IM & KM
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IM & KM are new fields of study
Multi-disciplinary
Focus on information and managing expertise
not on technology – IT underpins information
and knowledge management
New type of professional may be required –
one who understands information, Knowledge
, IT and business
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Information Use: Management Issues
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What information do we need?
What information do we have?
Where is the information held and in
what form?
How much does it cost to acquire and
process information?
How can we tell if it adds any value?
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Knowledge Use: Management
Issues
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What information is needed to create
knowledge?
What explicit knowledge do we have? Where
can we find it?
What implicit knowledge do our employees
have? How can we capture and use it?
How much value does knowledge add?
How can we cultivate knowledge within the
organisation?
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Why Knowledge Management?
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Organisations have lots of useful knowledge
lying around that could be used to their
advantage
But identifying it, finding it and leveraging it
can be problematical
A knowledge intensive culture promotes
knowledge creation and knowledge sharing
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Taxonomy of Knowledge
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Tacit – rooted in actions, experience & context
Explicit – articulated, generalized
Social - know who – collective
Declarative – know about
Procedural – know how
Causal – know why
Conditional – know when
Relational – know with
Pragmatic – best practice
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Basic Knowledge Processes
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Knowledge
Knowledge
Knowledge
Knowledge
creation
storage & retrieval
transfer
application
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Knowledge Creation
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Development of new tacit/explicit knowledge
– individual & social
Modes:
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Socialization, externalisation, internalisation,
combination
IS
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Data mining & data warehousing
CSCW, intranets
Brainstorming at a distance
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Knowledge Storage & Retrieval
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Organisational memory
Documents (hard & soft), databases,
expert systems, plus tacit knowledge
Supports status quo
May not always be easy to interpret
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Knowledge Transfer – can
be achieved
Between individuals, groups, explicit sources,
organisations
Depends on:
- perceived value of source unit’s knowledge,
- willingness to share,
- willingness to listen,
- richness of transmission channel (implications
for IS)
- absorptive capacity of recipient.
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Issues (i.e. Problems) in
Practice
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Using KM for strategic advantage
Obtaining top management support
Motivating staff to contribute
Identifying relevant knowledge
Evaluation
Verification
Design & development
Sustaining progress
Security
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Tacit Knowledge
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“We know more than we can tell”
Hard to formalise & communicate
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Driving a car
Explicit knowledge may imply tacit
knowledge
Polimorphic knowledge, relating to
social behaviour, can only be learned
through experience and socialisation
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The Role of Experts
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Usually provides a certain status
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Unlikely to give away years of experience for
nothing
Experts often linked in a community of
practice
Experts often disagree
Experts can be wrong but may be more likely
to spot things going wrong and have
sufficient judgement to change course
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KM – A Dehumanising
Technology?
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“The next fad to forget people?” (e.g. BPR)
“The idea behind KM is to stockpile workers’
knowledge and make it accessible via a
searchable application”
KM emphasis is on IT, not HR
Knowledge treated as a codified commodity
Danger of increased rigidity
Impact on remaining people – alienation?
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Characteristics of Data,
Information & Knowledge
Data
Explicit
Use
Accept
No learning
Direction
Efficiency
Information
Interpreted
Construct
Confirm
Single loop
Communication
Effectiveness
Knowledge
Tacit/embedded
Reconstruct
Disconfirm
Double loop
Sense-making
Innovation
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Information Management
Infrastructure
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Identifying & meeting information
requirements
Assessing the cost of obtaining and
processing information and the systems and
staff needed to do it
Appointing people with responsibility for
managing information and IT resources
Creating divisions, departments or sections
responsible for managing information
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Putting the Right People in Charge
Chief Information Officer
Chief
Technology
Officer
Chief
Knowledge
Officer
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Comparison of Roles
CIO
CTO
Manage internal
information, IT &
administrative
resources
Monitor, evaluate &
Transforming
select new technologies intellectual capital into
business value
Develop IT strategy &
link it to business
Provide technical vision
to complement the
business vision
Identify knowledge
requirements &
strategies for
increasing knowledge
Ensure operational
efficiency of systems
Determine what
technologies will
generate best ROI
Design & implement
knowledge
infrastructure
Educate business in the Translate ideas into a
use of IT
form that laypeople
understand
CKO
Create collaborative
work environment
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The Chief Information Officer
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Role emerged in the mid 1980’s
Earl (1996) argues that it was a result of:
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Convergence of computing & telecommunications
& consequent need to manage complex IT
infrastructure
Increased size of IT departments and budgets
Realization that information & IT were strategic
resources
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The Chief Information Officer: Role
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In reality, often about managing IT
CIO role has a very high turnover rate
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High project failure rate/soaring costs
Inability of IT to support business goals
and innovation
Many organisations are devolving
responsibility for IT to the business
units and eliminating the role
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The Chief Information Officer
Earl (1996) found that the following
attributes were critical:
- Very high level of technical competence
- Excellent leadership skills – ability to
create a shared vision, good at
relationship-building, ability to deliver
- Good at politicking
- Extroverted
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The Chief Information Officer: CIO Genus
Gartner Group Research (2000)
- Strategist
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Technologist
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Enterprise-wide responsibility for ensuring technology-based
services across the enterprise deliver
Technology opportunist
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Enterprise-wide responsibility for IM & IT management
Executive-level responsibility for spotting the opportunity to
use new technology
Executive
-
Head of business unit responsible for managing IT-related
services
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Conclusion
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Major changes in the sources of wealth creation have
transformed the value of information & made knowledge a key
organisational resource
Organisations need to manage their information & knowledge
resources effectively
This requires an understanding of what information is and how
it can best be captured, stored, disseminated and used to
generate knowledge
The task for managers is to create an infrastructure to exploit
information and knowledge resources
The appointment of senior staff to manage IT and Knowledge is
a recognition of the importance of information but the high
turnover rate suggests that information is frequently not well
managed
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Conclusion (Contd….)
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Like its forerunners (DM & IM) KM is
encountering problems that mere technology
cannot solve
The blind application of KM principles is
unlikely to be very successful but some useful
tools may be developed along the way,
together with vast amounts of (un)usable
data
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Conclusion (Contd….)
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All decision support systems involve data,
information and knowledge
When designing decision support system it is
important to identify what data, information
and knowledge is relevant to the problem
Having “too much” or “the wrong data”,
“Wrong information” or Wrong knowledge”
can be even more problematic than having
too little.
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Sources
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Earl, M.J. (1996) Information Management: The Organizational
Dimension, Oxford University Press.
Harrison, R. and Kessels, J. (2004) Human Resource
Development in a Knowledge Economy: An Organisational View,
Palgrave MacMillan.
Kaku, M. (1998) Visions, Oxford University Press
Pralahad, , C.K. and Ramaswamy, V. (2002) The Co-creation
Connection,” Strategy & Business, Issue 27, 2nd Quarter: 50-61.
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