management information system

Data, Information and Knowledge Unit 2

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Unit 2 Data, Information and Knowledge

Structure

2.0 Introduction

2.1 Data, Information and Knowledge

Interrelationship, differences and characteristics

2.2 Data Meaning, Definition and types

Importance of Data in Managerial Process

2.3 Information Meaning, Definition, Changing concepts

Types and quality

2.4 Data / Information Processing Cycle

2.5 Data / Information Security, Cyber Security

2.6 Knowledge Meaning, Definition, Types

2.7 Summary


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2.0 Introduction

In the earlier unit, you came to know about Information Technology -

conceptual basis, definition, components, and application areas;

developments in computer hardware, software and their role in digital

transformation; developments in Telecommunication technology and

convergence of technologies.

Data are values of qualitative or quantitative variables, belonging to a set of

items. Data are typically the results of measurements and can be visualized

using graphs or images. Information, in its most restricted technical sense,

is a sequence of symbols that can be interpreted as a message. Information

can be recorded as signs, or transmitted as signals. Information is any kind

of event that affects the state of a dynamic system. Knowledge is a

familiarity with someone or something, which can include facts, information,

descriptions, or skills acquired through experience or education. It can refer

to the theoretical or practical understanding of a subject. It can be implicit

(as with practical skill or expertise) or explicit (as with the theoretical

understanding of a subject); and it can be more or less formal or systematic.

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In this unit, we appreciate the interrelation between Data-Information-

Knowledge dimensions; their types, interrelationship, differences and

characteristics; their importance in managerial process; processing cycle &

security aspects of data & information. For a better understanding of this

unit, prior knowledge about the Information Technology for Management is

recommended.

Objectives:

After studying this unit, you should be able to:

Explain the terms data, information, and knowledge

Appreciate the interrelation between the above terms

Understand the importance of data in managerial process

Explain the data/information processing cycle & security aspects

2.1 Data, Information and Knowledge

Data as an abstract concept can be viewed as the lowest level of

abstraction from which information and then knowledge are derived. Raw

data, i.e., unprocessed data, refers to a collection of numbers, characters

and is a relative term; data processing commonly occurs by stages, and the

"processed data" from one stage may be considered the "raw data" of the

next. Field data refers to raw data collected in an uncontrolled in situ

environment. Experimental data refers to data generated within the context

of a scientific investigation by observation and recording.

The word data is the plural of datum, neuter past participle of the Latin dare,

"to give", hence "something given". In discussions of problems in geometry,

mathematics, engineering, and so on, the terms givens and data are used

interchangeably. Such usage is the origin of data as a concept in computer

science or data processing: data are numbers, words, images, etc.,

accepted as they stand.

Conceptually, information is the message (utterance or expression) being

conveyed. This concept has numerous other meanings in different contexts.

Moreover, the concept of information is closely related to notions of

constraint, communication, control, data, form, instruction, knowledge,

meaning, mental stimulus, pattern, perception, representation, and

especially entropy.


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In philosophy, the study of knowledge is called epistemology, and the

philosopher Plato famously defined knowledge as "justified true belief."

However no single agreed upon definition of knowledge exists, and there

are numerous theories to explain it. Knowledge acquisition involves complex

cognitive processes: perception, communication, association and reasoning;

while knowledge is also said to be related to the capacity of

acknowledgment in human beings. Many of us would probably say

knowledge that something is true involves:

Certainty it's hard if not impossible to deny

Evidence it has to be based on something

Practicality it has to actually work in the real world

Broad agreement lots of people have to agree it's true

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Interrelationship, differences and characteristics


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The terms data, information and knowledge are frequently used for

overlapping concepts. The main difference is in the level of abstraction

being considered. Data is the lowest level of abstraction, information is the

next level, and finally, knowledge is the highest level among all three. Data

on its own carries no meaning. For data to become information, it must be

interpreted and take on a meaning. For example, the height of Mt. Everest is

generally considered as "data", a book on Mt. Everest geological

characteristics may be considered as "information", and a report containing

practical information on the best way to reach Mt. Everest's peak may be

considered as "knowledge".

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Information as a concept bears a diversity of meanings, from everyday

usage to technical settings. Generally speaking, the concept of information

is closely related to notions of constraint, communication, control, data,

form, instruction, knowledge, meaning, mental stimulus, pattern, perception,

and representation.

It is people and computers who collect data and impose patterns on it.

These patterns are seen as information which can be used to enhance

knowledge. These patterns can be interpreted as truth, and are authorized

as aesthetic and ethical criteria. Events that leave behind perceivable

physical or virtual remains can be traced back through data. Marks are no

longer considered data once the link between the mark and observation is

broken.


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Information is any type of pattern that influences the formation or

transformation of other patterns. In this sense, there is no need for a

conscious mind to perceive, much less appreciate, the pattern. Consider, for

example, DNA. The sequence of nucleotides is a pattern that influences the

formation and development of an organism without any need for a

conscious mind. If, however, the premise of "influence" implies that

information has been perceived by a conscious mind and also interpreted by

it, the specific context associated with this interpretation may cause the

transformation of the information into knowledge. Complex definitions of

both "information" and "knowledge" make such semantic and logical

analysis difficult, but the condition of "transformation" is an important point in

the study of information as it relates to knowledge, especially in the

business discipline of knowledge management.

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2.2 Types of Data


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In certain technical fields (especially computer programming and statistics),

a data type is a classification identifying one of various types of data, such

as real-valued, integer or Boolean, that determines the possible values for

that type; the operations that can be done on values of that type; the

meaning of the data; and the way values of that type can be stored.

Following types of data may be used for business analysis:

Nominal

The name 'Nominal' comes from the Latin nomen, meaning 'name' and

nominal data are items which are differentiated by a simple naming system.

The only thing a nominal scale does is to say that items being measured

have something in common, although this may not be described.

Nominal items may have numbers assigned to them. This may appear

ordinal but is not -- these are used to simplify capture and referencing.

Nominal items are usually categorical, in that they belong to a definable

category, such as 'employees'. Example: the number pinned on a sports

person; a set of countries.

Ordinal

Items on an ordinal scale are set into some kind of order by their position on

the scale. This may indicate such as temporal position, superiority, etc.

The order of items is often defined by assigning numbers to them to show

their relative position. Letters or other sequential symbols may also be used

as appropriate.

Ordinal items are usually categorical, in that they belong to a definable

category, such as '1956 marathon runners'. You cannot do arithmetic with

ordinal numbers -- they show sequence only. Example: the first, third and

fifth person in a race; pay bands in an organization, as denoted by A, B, C

and D.

Interval

Interval data (also sometimes called integer) is measured along a scale in

which each position is equidistant from one another. This allows for the

distance between two pairs to be equivalent in some way.

This is often used in psychological experiments that measure attributes

along an arbitrary scale between two extremes. Interval data cannot be

multiplied or divided. Example: my level of happiness, rated from 1 to 10;

temperature, in degrees Fahrenheit.


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Ratio

In a ratio scale, numbers can be compared as multiples of one another.

Thus one person can be twice as tall as another person. Important also, the

number zero has meaning. Thus the difference between a person of 35 and

a person 38 is the same as the difference between people who are 12 and

15. A person can also have an age of zero.

Ratio data can be multiplied and divided because not only is the difference

between 1 and 2 the same as between 3 and 4, but also that 4 is twice as

much as 2. Interval and ratio data measure quantities and hence are

quantitative. Because they can be measured on a scale, they are also called

scale data. Example: a person's weight; the number of pizzas I can eat

before fainting.

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Parametric vs. Non-parametric

Interval and ratio data are parametric, and are used with parametric tools in

which distributions are predictable (and often Normal). Nominal and ordinal

data are non-parametric, and do not assume any particular distribution.

They are used with non-parametric tools such as the Histogram.

Continuous and Discrete


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Continuous measures are measured along a continuous scale which can be

divided into fractions, such as temperature. Continuous variables allow for

infinitely fine sub-division, which means if you can measure sufficiently

accurately, you can compare two items and determine the difference.

Discrete variables are measured across a set of fixed values, such as age in

years (not microseconds). These are commonly used on arbitrary scales,

such as scoring your level of happiness, although such scales can also be

continuous.

Classes of data types

Machine data types

All data in computers based on digital electronics is represented as bits

(alternatives 0 and 1) on the lowest level. The smallest addressable unit of

data is usually a group of bits called a byte (usually an octet, which is 8

bits). The unit processed by machine code instructions is called a word (as

of 2011, typically 32 or 64 bits).

Boolean type

The Boolean type represents the values: true and false. Although only two

values are possible, they are rarely implemented as a single binary digit for

efficiency reasons. Many programming languages do not have an explicit

boolean type, instead interpreting (for instance) 0 as false and other values

as true.

Numeric types

Such as:

Integer data types, or "whole numbers". May be subtyped according

to their ability to contain negative values (eg. unsigned in C and

C++). May also have a small number of predefined subtypes (such

as short and long in C/C++); or allow users to freely define

subranges such as 1..12 (eg. Pascal/Ada).

Floating point data types, sometimes misleadingly called reals,

contain fractional values. They usually have predefined limits on

both their maximum values and their precision.

Fixed point data types are convenient for representing monetary

values. They are often implemented internally as integers, leading to

predefined limits.


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String and text types

Such as

Alphanumeric character. A letter of the alphabet, digit, blank

space, punctuation mark, etc.

Alphanumeric strings, a sequence of characters. They are typically

used to represent words and text.

Character and string types can store sequences of characters from a

character set such as ASCII. Since most character sets include the digits, it

is possible to have a numeric string, such as "1234". However, many

languages would still treat these as belonging to a different type to the

numeric value 1234.

Character and string types can have different subtypes according to the

required character "width".

Enumerations

The enumerated type. This has values which are different from each other,

and which can be compared and assigned, but which do not necessarily

have any particular concrete representation in the computer's memory;

compilers and interpreters can represent them arbitrarily. For example, the

four suits in a deck of playing cards may be four enumerators named CLUB,

DIAMOND, HEART, SPADE, belonging to an enumerated type named suit.

If a variable V is declared having suit as its data type, one can assign any of

those four values to it. Some implementations allow programmers to assign

integer values to the enumeration values, or even treat them as type-

equivalent to integers.

Composite types

Composite types are derived from more than one primitive type. This can be

done in a number of ways. The ways they are combined are called data

structures. Composing a primitive type into a compound type generally

results in a new type, eg. array-of-integer is a different type to integer.

An array stores a number of elements of the same type in a specific

order. They are accessed using an integer to specify which element

is required (although the elements may be of almost any type).

Arrays may be fixed-length or expandable.

Record (also called tuple or struct) Records are among the simplest

data structures. A record is a value that contains other values,


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typically in fixed number and sequence and typically indexed by

names. The elements of records are usually called fields or

members.

Union. A union type definition will specify which of a number of

permitted primitive types may be stored in its instances, eg "float or

long integer". Contrast with a record, which could be defined to

contain a float and an integer; whereas, in a union, there is only one

value at a time.

A set is an abstract data structure that can store certain values,

without any particular order, and no repeated values. Values

themselves are not retrieved from sets, rather one tests a value for

membership to obtain a Boolean "in" or "not in".

An object contains a number of data fields, like a record, and also a

number of programmed code fragments for accessing or modifying

them. Data structures not containing code, like those above, are

called plain old data structure.

Importance of Data in Managerial Process

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Information as a necessary evil: - Information was regarded as a

necessary evil, associated with the development, production and

marketing of products or services. Information was thus merely


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considered as a by-product of transactions in the organizations. As a

result, information systems of 1950s were primarily designed with

the aim to reduce the cost of routine paper processing in accounting

areas. The term Electronic Data Processing (EDP) was coined in

this period.

Information for General Management Support: - By mid-sixties,

organizations began recognizing information as an important tool,

which could support general management tasks. The information

systems corresponding to this period were known as management

information system (MIS) and were thought of as system processing

data into information.

Information for decisionmaking: -In early eighties, information

was regarded as providing special-purpose, tailor-made

management controls over the organization. Decision support

systems and executive support systems were important

advancements, which took place during this period. The purpose of

such information systems was to improve and speed-up the

decision-making process of top-level managers.

Information as a strategic resource: - In the revolutionary change

pattern, the concept of information changed again by the mid-

eighties and information has since then been considered as a

strategic resource, capable of providing competitive advantage or a

strategic weapon to fight the competition. Latest information systems

which are known as strategic systems, support this concept of

information

2.3 Quality of Information

Information quality (IQ) is a term to describe the quality of the content of

information systems. It is often pragmatically defined as: "The fitness for use

of the information provided." Although this pragmatic definition is usable for

most everyday purposes, specialists often use more complex models for

information quality. Most information system practitioners use the term

synonymously with data quality. However, as many academics make a

distinction between data and information, some will insist on a distinction

between data quality and information quality. This distinction would be akin

to the distinction between syntax and semantics where for example, the

semantic value of "one" could be expressed in different syntaxes like 00001;


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1.0000; 01.0; or 1. Thus a data difference may not necessarily represent

poor information quality.

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"Information quality" is a measure of the value which the information

provides to the user of that information. "Quality" is often perceived as

subjective and the quality of information can then vary among users and

among uses of the information. Nevertheless, a high degree of quality

increases its objectivity or at least the inter-subjectivity. Accuracy can be

seen as just one element of IQ but, depending upon how it is defined, can

also be seen as encompassing many other dimensions of quality.

If not, it is perceived that often there is a trade-off between accuracy and

other dimensions, aspects or elements of the information determining its

suitability for any given tasks. A list of dimensions or elements used in

assessing subjective Information Quality is:

Intrinsic IQ: Accuracy, Objectivity, Believability, Reputation

Contextual IQ: Relevancy, Value-Added, Timeliness,

Completeness, Amount of information

Representational IQ: Interpretability, Ease of understanding,

Concise representation, Consistent representation

Accessibility IQ: Accessibility, Access security

Proposed Quality Metrics

Authority/Verifiability: Authority refers to the expertise or

recognized official status of a source. Consider the reputation of the

author and publisher. When working with legal or government

information, consider whether the source is the official provider of


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the information. Verifiability refers to the ability of a reader to verify

the validity of the information irrespective of how authoritative the

source is. To verify the facts is part of the duty of care of the

journalistic deontology, as well as, where possible, to provide the

sources of information so that they can be verified

Scope of coverage: Scope of coverage refers to the extent to which

a source explores a topic. Consider time periods, geography or

jurisdiction and coverage of related or narrower topics.

Composition and Organization: Composition and Organization

has to do with the ability of the information source to present its

particular message in a coherent, logically sequential manner.

Objectivity: Objectivity is the bias or opinion expressed when a

writer interprets or analyze facts. Consider the use of persuasive

language, the sources presentation of other viewpoints, its reason

for providing the information and advertising.

Integrity: Adherence to moral and ethical principles; soundness of

moral character. The state of being whole, entire, or undiminished

Comprehensiveness:

1. Of large scope; covering or involving much; inclusive: a

comprehensive study.

2. Comprehending mentally; having an extensive mental grasp.

3. Insurance. covering or providing broad protection against loss.

Validity: Validity of some information has to do with the degree of

obvious truthfulness which the information caries

Uniqueness: As much as uniqueness of a given piece of

information is intuitive in meaning, it also significantly implies not

only the originating point of the information but also the manner in

which it is presented and thus the perception which it conjures. The

essence of any piece of information we process consists to a large

extent of those two elements.

Timeliness: Timeliness refers to information that is current at the

time of publication. Consider publication, creation and revision dates.

Beware of Web site scripting that automatically reflects the current

days date on a page.

2.4 Data / Information Processing Cycle

Data processing is the act of handling or manipulating data in some fashion.


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Regardless of the activities involved in it, processing tries to assign meaning

to data. Thus, the ultimate goal of processing is to transform data into

information. Data processing is the process through which facts and figures

are collected, assigned meaning, communicated to others and retained for

future use. Hence we can define data processing as a series of actions or

operations that converts data into useful information. We use the term 'data

processing system' to include the resources that are used to accomplish the

processing of data.

Data processing consists of those activities which are necessary to

transform data into information. Man has in course of time devised certain

tools to help him in processing data. These include manual tools such as

pencil and paper, mechanical tools such as filing cabinets,

electromechanical tools such as adding machines and typewriters, and

electronic tools such as calculators and computers. Many people

immediately associate data processing with computers. As stated above, a

computer is not the only tool used for data processing; it can be done

without computers also. However, computers have outperformed people for

certain tasks. There are some other tasks for which computers are a poor

substitute for human skill and intelligence.

Data Processing Activities

Regardless to the type of equipment used, various functions and activities

which need to be performed for data processing can be grouped under five

basic categories as shown below:

Collection

Data originates in the form of events transaction or some observations. This

data is then recorded in some usable form. Data may be initially recorded

on paper source documents and then converted into a machine usable form

for processing. Alternatively, they may be recorded by a direct input device

in a paperless, machine-readable form. Data collection is also termed as

data capture.


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Conversion

Once the data is collected, it is converted from its source documents to a

form that is more suitable for processing. The data is first codified by

assigning identification codes. A code comprises of numbers, letters, special

characters, or a combination of these. For example, an employee may be

allotted a code as 52-53-162, his category as A class, etc. It is useful to

codify data, when data requires classification. To classify means to

categorize, i.e., data with similar characteristics are placed in similar

categories or groups. For example, one may like to arrange accounts data

according to account number or date. Hence a balance sheet can easily be

prepared.

After classification of data, it is verified or checked to ensure the accuracy

before processing starts. After verification, the data is transcribed from one

data medium to another. For example, in case data processing is done

using a computer, the data may be transformed from source documents to

machine sensible form using magnetic tape or a disk.

Manipulation

Once data is collected and converted, it is ready for the manipulation

function which converts data into information. Manipulation consists of

following activities:

Sorting: It involves the arrangement of data items in a desired

sequence. Usually, it is easier to work with data if it is arranged in a

logical sequence. Most often, the data are arranged in alphabetical

sequence. Sometimes sorting itself will transform data into

information. For example, a simple act of sorting the names in

alphabetical order gives meaning to a telephone directory. The

directory will be practically worthless without sorting.

Business data processing extensively utilizes sorting technique.

Virtually all the records in business files are maintained in some

logical sequence. Numeric sorting is common in computer-based

processing systems because it is usually faster than alphabetical

sorting.

Calculating: Arithmetic manipulation of data is called calculating.

Items of recorded data can be added to one another, subtracted,

divided or multiplied to create new data. Calculation is an integral

part of data processing. For example, in calculating an employee's

pay, the hours worked multiplied by the hourly wage rate gives the


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gross pay. Based on total earning, income-tax deductions are

computed and subtracted from gross-pay to arrive at net pay.

Summarizing: To summarize is to condense or reduce masses of

data to a more usable and concise form as shown in fig. 2.2(b). For

example, you may summarize a lecture attended in a class by

writing small notes in one or two pages. When the data involved is

numbers, you summarize by counting or accumulating the totals of

the data in a classification or by selecting strategic data from the

mass of data being processed. For example, the summarizing

activity may provide a general manager with sales-totals by major

product line, the sales manager with sales totals by individual

salesman as well as by the product line and a salesman with sales

data by customer as well as by product line.

Comparing: To compare data is to perform an evaluation in relation

to some known measure. For example, business managers compare

data to discover how well their companies are doing. They many

compare current sales figures with those for last year to analyze the

performance of the company in the current month.

Managing the Output Results

Once data has been captured and manipulated following activities may be

carried out:

Storing: To store is to hold data for continued or later use. Storage

is essential for any organized method of processing and re-using

data. The storage mechanisms for data processing systems are file

cabinets in a manual system, and electronic devices such as

magnetic disks/magnetic tapes in case of computer based system.

The storing activity involves storing data and information in

organized manner in order to facilitate the retrieval activity. Of

course, data should be stored only if the value of having them in

future exceeds the storage cost.

Retrieving: To retrieve means to recover or find again the stored

data or information. Retrieval techniques use data storage devices.

Thus data, whether in file cabinets or in computers can be recalled

for further processing. Retrieval and comparison of old data gives

meaning to current information.

Communication


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Communication is the process of sharing information. Unless the

information is made available to the users who need it, it is worthless.

Thus, communication involves the transfer of data and information produced

by the data processing system to the prospective users of such information

or to another data processing system. As a result, reports and documents

are prepared and delivered to the users. In electronic data processing,

results are communicated through display units or terminals.

Reproduction

To reproduce is to copy or duplicate data or information. This reproduction

activity may be done by hand or by machine.

Data Processing Cycle

The data processing activities described above are common to all data

processing systems from manual to electronic systems. These activities can

be grouped in four functional categories, viz., data input, data processing,

data output and storage, constituting what is known as a data processing

cycle.

Input

The term input refers to the activities required to record data and to make it

available for processing. The input can also include the steps necessary to

check, verify and validate data contents.


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Processing

The term processing denotes the actual data manipulation techniques such

as classifying, sorting, calculating, summarizing, comparing, etc. that

convert data into information.

Output

It is a communication function which transmits the information, generated

after processing of data, to persons who need the information. Sometimes

output also includes decoding activity which converts the electronically

generated information into human-readable form.

Storage

It involves the filing of data and information for future use. The above

mentioned four basic functions are performed in a logical sequence as

shown in above figure in all data processing systems.

2.5 Information Security

Information security means protecting information and information systems

from unauthorized access, use, disclosure, disruption, modification, perusal,

inspection, recording or destruction. The terms information security,

computer security and information assurance are frequently used

interchangeably. These fields are interrelated often and share the common


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goals of protecting the confidentiality, integrity and availability of information;

however, there are some subtle differences between them.

Governments, military, corporations, financial institutions, hospitals, and

private businesses amass a great deal of confidential information about their

employees, customers, products, research, and financial status. Most of this

information is now collected, processed and stored on electronic computers

and transmitted across networks to other computers. Should confidential

information about a business' customers or finances or new product line fall

into the hands of a competitor, such a breach of security could lead to

negative consequences. Protecting confidential information is a business

requirement, and in many cases also an ethical and legal requirement.

For the individual, information security has a significant effect on privacy,

which is viewed very differently in different cultures. The field of information

security has grown and evolved significantly in recent years. There are

many ways of gaining entry into the field as a career. It offers many areas

for specialization including: securing network(s) and allied infrastructure,

securing applications and databases, security testing, information systems

auditing, business continuity planning and digital forensics science, etc.


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For over twenty years, information security has held confidentiality, integrity

and availability (known as the CIA triad) to be the core principles of

information security.

Confidentiality

Confidentiality is the term used to prevent the disclosure of information to

unauthorized individuals or systems. For example, a credit card transaction

on the Internet requires the credit card number to be transmitted from the

buyer to the merchant and from the merchant to a transaction processing

network. The system attempts to enforce confidentiality by encrypting the

card number during transmission, by limiting the places where it might

appear (in databases, log files, backups, printed receipts, and so on), and

by restricting access to the places where it is stored. If an unauthorized

party obtains the card number in any way, a breach of confidentiality has

occurred.

Integrity

In information security, integrity means that data cannot be modified

undetectably. This is not the same thing as referential integrity in databases,

although it can be viewed as a special case of Consistency as understood in

the classic ACID model of transaction processing. Integrity is violated when

a message is actively modified in transit. Information security systems

typically provide message integrity in addition to data confidentiality.

Availability

For any information system to serve its purpose, the information must be

available when it is needed. This means that the computing systems used to

store and process the information, the security controls used to protect it,

and the communication channels used to access it must be functioning

correctly. High availability systems aim to remain available at all times,

preventing service disruptions due to power outages, hardware failures, and

system upgrades. Ensuring availability also involves preventing denial-of-

service attacks.

Authenticity

In computing, e-Business, and information security, it is necessary to ensure

that the data, transactions, communications or documents (electronic or


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physical) are genuine. It is also important for authenticity to validate that

both parties involved are who they claim they are.

Non-Repudiation

In law, non-repudiation implies one's intention to fulfill their obligations to a

contract. It also implies that one party of a transaction cannot deny having

received a transaction nor can the other party deny having sent a

transaction. Electronic commerce uses technology such as digital

signatures and public key encryption to establish authenticity and non-

repudiation.

2.6 Types of Knowledge

Understanding the different forms that knowledge can exist in, and thereby

being able to distinguish between various types of knowledge, is an

essential step for knowledge management (KM). For example, it should be

fairly evident that the knowledge captured in a document would need to be

managed (i.e. stored, retrieved, shared, changed, etc.) in a totally different

way than that gathered over the years by an expert craftsman.

Over the centuries many attempts have been made to classify knowledge,

and different fields have focused on different dimensions. This has resulted

in numerous classifications and distinctions based in philosophy and even

religion. Within business and KM, two types of knowledge are usually

defined, namely explicit and tacit knowledge. The former refers to codified

knowledge, such as that found in documents, while the latter refers to non-

codified and often personal/experience-based knowledge. Some

researchers make a further distinction and talk of embedded knowledge.

This way, one differentiates between knowledge embodied in people and

that embedded in processes, organizational culture, routines, etc. (Horvath

2000).

Explicit Knowledge

This type of knowledge is formalized and codified, and is sometimes

referred to as know-what (Brown & Duguid 1998). It is therefore fairly easy

to identify, store, and retrieve (Wellman 2009). This is the type of knowledge

most easily handled by KMS, which are very effective at facilitating the

storage, retrieval, and modification of documents and texts.

From a managerial perspective, the greatest challenge with explicit

knowledge is similar to information. It involves ensuring that people have


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access to what they need; that important knowledge is stored; and that the

knowledge is reviewed, updated, or discarded.

Many theoreticians regard explicit knowledge as being less important (e.g.

Brown & Duguid 1991, Cook & Brown 1999, Bukowitz & Williams 1999,

etc.). It is considered simpler in nature and cannot contain the rich

experience based know-how that can generate lasting competitive

advantage.

Although this is changing to some limited degree, KM initiatives driven by

technology have often had the flaw of focusing almost exclusively on this

type of knowledge. As discussed previously, in fields such as IT there is

often a lack of a more sophisticated definition. This has therefore created

many products labeled as KM systems, which in actual fact are/were

nothing more than information and explicit knowledge management

software. Explicit knowledge is found in: databases, memos, notes,

documents, etc. (Botha et al. 2008)

Tacit (Embodied) Knowledge

This type of knowledge was originally defined by Polanyi in 1966. It is

sometimes referred to as know-how (Brown & Duguid 1998) and refers to

intuitive, hard to define knowledge that is largely experience based.

Because of this, tacit knowledge is often context dependent and personal in

nature. It is hard to communicate and deeply rooted in action, commitment,

and involvement (Nonaka 1994).

Tacit knowledge is also regarded as being the most valuable source of

knowledge, and the most likely to lead to breakthroughs in the organization

(Wellman 2009). Gamble & Blackwell (2001) link the lack of focus on tacit

knowledge directly to the reduced capability for innovation and sustained

competitiveness. KMS have a very hard time handling this type of

knowledge. An IT system relies on codification, which is something that is

difficult / impossible for the tacit knowledge holder.

Using a reference by Polanyi (1966), imagine trying to write an article that

would accurately convey how one reads facial expressions. It should be

quite apparent that it would be near impossible to convey our intuitive

understanding gathered from years of experience and practice. Virtually all

practitioners rely on this type of knowledge. An IT specialist for example will

troubleshoot a problem based on his experience and intuition. It would be


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very difficult for him to codify his knowledge into a document that could

convey his know-how to a beginner. This is one reason why experience in a

particular field is so highly regarded in the job market.

The exact extent to which IT systems can aid in the transfer and

enhancement of tacit knowledge is a rather complicated discussion. For

now, suffice it to say that successful KM initiatives must place a very strong

emphasis on the tacit dimension, focusing primarily on the people involved,

and they must understand the limitations imposed by computerized

systems.

Tacit knowledge is found in: the minds of human stakeholders. It includes

cultural beliefs, values, attitudes, mental models, etc. as well as skills,

capabilities and expertise (Botha et al 2008). On this site, I will generally

limit tacit knowledge to knowledge embodied in people, and refer separately

to embedded knowledge (as defined below), whenever making this

distinction is relevant.

(http://www.cognitivedesignsolutions.com/images/ExplicitTacitIceberg.jpg)

Embedded Knowledge


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Embedded knowledge refers to the knowledge that is locked in processes,

products, culture, routines, artifacts, or structures (Horvath 2000, Gamble &

Blackwell 2001). Knowledge is embedded either formally, such as through a

management initiative to formalize a certain beneficial routine, or informally

as the organization uses and applies the other two knowledge types.

The challenges in managing embedded knowledge vary considerably and

will often differ from embodied tacit knowledge. Culture and routines can be

both difficult to understand and hard to change. Formalized routines on the

other hand may be easier to implement and management can actively try to

embed the fruits of lessons learned directly into procedures, routines, and

products.

IT's role in this context is somewhat limited but it does have some useful

applications. Broadly speaking, IT can be used to help map organizational

knowledge areas; as a tool in reverse engineering of products (thus trying to

uncover hidden embedded knowledge); or as a supporting mechanism for

processes and cultures. However, it has also been argued that IT can have

a disruptive influence on culture and processes, particularly if implemented

improperly. Due to the difficulty in effectively managing embedded

knowledge, firms that succeed may enjoy a significant competitive

advantage.

Embedded knowledge is found in: rules, processes, manuals, organizational

culture, codes of conduct, ethics, products, etc. It is important to note, that

while embedded knowledge can exist in explicit sources (i.e. a rule can be

written in a manual), the knowledge itself is not explicit, i.e. it is not

immediately apparent why doing something this way is beneficial to the

organization.

2.7 Summary

Let us sum up what we have discussed in this unit.

Data represents unorganized and unprocessed facts. Usually data is

static in nature. It can represent a set of discrete facts about events.

Data is a prerequisite to information.

Information is not only relevant but also critical for the decision maker as

the quality of decision making is dependent on the quality of information.


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Information to be of use for the end user should be accurate, reliable,

relevant, complete, cost effective, timely available and consistent

Knowledge can be defined as the understanding obtained through the

process of experience or appropriate study. Knowledge is derived from

information in the same way information is derived from data. We can

view it as an understanding of information based on its perceived

importance or relevance to a problem area. Knowledge is often an

organisations most valuable asset

Information security means protecting information and information

systems from unauthorized access, use, disclosure, disruption,

modification, perusal, inspection, recording or destruction.

management information system

Documents

knowledge data

data information security

data meaning

raw data

terms data

summary data

unprocessed data

word data