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Week 7

Amare Michael Desta

Decision Support & Executive Information

Systems:

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Data, Information and Knowledge is needed to … 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 Data – that which is given In problem solving (decision making)

What is known or assumed to be true Typically a member or members of one

or more collection or sets of ‘objects’ 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 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

M a k e M o d e l C o lo u rR #

ABC Ford Escort Red

XYZ Ford Escort Blue

PQR GM Astra White

LMN Volvo 340 Red

Tuples

Cardinality

Attributes

D e g re e

{Relation

Primary K ey

YellowRed

etc. } Domains

R# Make Model Colour

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Data, Measurement and Observation 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 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 beclassified in terms of the Information

andknowledge available- Certainty- Risk- Uncertainty

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RationalityWhen modelling peoples behavioureconomists and management scientists usuallyassume 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 RationalityProblems 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 RationalityHerbert Simon introduced the termbounded rationality as a more realisticview of decision making:- BR is NOT irrationality- Actors make the best decision (act

rationally) given - limited information - Limited knowledge- Limited resources

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Learning & Knowing processRequires 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 inorganisation

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Knowledge Management Systems, (KMS) 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 consistsprimarily of creating, processinginformation

KNOWLEDGE WORKERS: People whodesign products or services or create newknowledge for organization

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Knowledge Processes 1 A Mechanistic ViewPeople as Computers Creation Acquisition Transmission Storage Retrieval

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Knowledge Management and IT

SHARE SHARE KNOWLEDGEKNOWLEDGE

DISTRIBUTE DISTRIBUTE KNOWLEDGEKNOWLEDGE

CREATE CREATE KNOWLEDGEKNOWLEDGE

CAPTURE, CAPTURE, CODIFY CODIFY KNOWLEDGEKNOWLEDGE

GROUP GROUP COLLABORATION COLLABORATION

SYSTEMSSYSTEMS

OFFICE OFFICE AUTOMATION AUTOMATION

SYSTEMSSYSTEMS

ARTIFICIAL ARTIFICIAL INTELLIGENCE INTELLIGENCE

SYSTEMSSYSTEMS

KNOWLEDGE KNOWLEDGE WORK WORK SYSTEMSSYSTEMS

NETWORKSNETWORKS

DATABASESDATABASES

PROCESSORSPROCESSORS

SOFTWARESOFTWARE

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Organisational Knowledge

Individual Collective

Codified

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.

Situated

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 flightleaves Detroit at

18:40

That’s not a goodflight; often busy

and delayed

Relationships and trust are required for knowledgetransfer and reuse

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Relationship of Data, Information & Knowledge

The World

The Agent’s Knowledge

BaseData

FiltersData Information

Prior Expectation

Knowledge: a disposition to act

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Processing Hierarchy Centrality of data

(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

ProcessingReport

ManipulationDataEntry

DataCollection

USERS

Knowledge is a pervasive characteristic of information systems

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Data & Information: System Perspective

Data Information

Sender Receiver

Encoding Decoding

Computer System

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Information Systems

Information systems process data and turn it into

information that can be used for management decision-

making

Knowledge is used to design, encode and operate IS

Knowledge is needed to decode the information that

comes out of IS

IS professionals need to understand the human

(perceptual) processes involved in the encoding and

decoding process

Data, Information & Knowledge: Linear Models

Knowledge ActionsData Information Results

Benefits-Driven Approach

Usual Approach

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Data, Information & Knowledge Cyclic Model

Accumulate Knowledge

(Experience)

Format,Filter

Summarise

Interpret,Decide,

Act

Knowledge

Information ResultsData

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Information & Knowledge: Sharing & Transmission

Information Capture, creation and dissemination

Releasing the Value 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

Accurate Appropriate detail and accuracy for the user

Meaningful to user

Up to date - information is very time sensitive.

Out of date information is misleading if not useless. Available

at point of need/use. related to decision-maker's context Complete and comprehensive

Providers the receiver with all they need to know

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Information & Quality Format

in a form that aids assimilation. Cost effective

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 Massive structural shifts in Western economies

Knowledge

Data

Information

Represented in Technology

Shift in Importance

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The Changing Economic Eras

EraVariables

Agricultural Industrial Post-industrial orInformation

Knowledge

Strategic Factorsof EconomicGrowth

Land Capacity ofIndustrial

Production

Information Knowledge

OrganisationalFactors

Hierarchy (LandOwner)

Blue CollarBureaucracy

(Factory Owner,Trade Union

Leaders )

White CollarBureaucracy

(Administrators, ITManagers

Collaboration(Communities)

PredominantConsumer Goods

Food Agricultural Goods,House & Clothing

Information &Communications

Services &Products

IntellectualProducts &Services

Technology Agricultural Manufacturing &Engineering

Information &Communications

Technology forLearning,

Innovating,Consulting,

CollaboratingPredominantResource

Workforce Physical Sources ofEnergy

Information Ideas

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The Shift to Information & K Work

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

knowledge-enhanced 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 Political Changes

Collapse of Communism, formation of major economic and political alliances

Business Changes Growth of free trade, deregulation, emergence of new

producers, globalisation Technological Changes

Biotechnology, telecommunications and computing

Social Changes Stakeholder Society, spread of egalitarian ideal

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The New Economy: Key Points 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 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

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 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? 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 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

Knowledge creation Knowledge storage & retrieval Knowledge transfer Knowledge application

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Knowledge Creation Development of new tacit/explicit

knowledge – individual & social Modes:

Socialization, externalisation, internalisation, combination

IS Data mining & data warehousing CSCW, intranets Brainstorming at a distance

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Knowledge Storage & Retrieval 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, organisationsDepends on:- perceived value of source unit’s knowledge,- willingness to share, - willingness to listen, - richness of transmission channel

(implicationsfor IS)- absorptive capacity of recipient.

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Issues (i.e. Problems) in Practice 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 “We know more than we can tell” Hard to formalise & communicate

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 Usually provides a certain status

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? “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

DataExplicitUseAcceptNo learningDirectionEfficiency

InformationInterpretedConstructConfirmSingle loopCommunicationEffectiveness

KnowledgeTacit/embeddedReconstructDisconfirmDouble loopSense-makingInnovation

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Information Management Infrastructure 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 Knowledge Officer

Chief Technology Officer

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Comparison of RolesCIO CTO CKOManage internal information, IT & administrative resources

Monitor, evaluate & select new technologies

Transforming 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 use of IT

Translate ideas into a form that laypeople understand

Create collaborative work environment

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The Chief Information Officer

Role emerged in the mid 1980’s Earl (1996) argues that it was a result

of: 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

In reality, often about managing IT CIO role has a very high turnover

rate 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 followingattributes 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

- Enterprise-wide responsibility for IM & IT management

- Technologist- Enterprise-wide responsibility for ensuring technology-

based services across the enterprise deliver

- Technology opportunist- 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 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….) 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….) 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 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.