CHIA B

Question 1

Heirachy in the data, information, knowledge, wisdom (DIKW) model

Answer 1

“Wisdom
Knowledge
Information
Data”

Question 2

Definitiosn of data, information, knowledge, wisdom

Answer 2

”* Data – representation of facts such as measurements or statistics collected for reference or analysis. For completeness, a fact is defined as something that truly exists or happens.

• Information – data in context, i.e., the meaning given to data by the way in which it is used and interpreted.
• Knowledge – an understanding about a subject attained via experience or study, either known by one person or by people generally.
• Wisdom – the ability to use knowledge and other personal attributes to make good decisions and judgments.
  “

Question 3

Define information management in contet of DIKW model

Answer 3

“Information management, of which data management is a subset, can be described as ““the process of collecting, storing, managing and maintaining information in all its forms”” (Techopedia, 2017). It involves:
* Identifying, capturing, assuring the quality of, and storing the subset of all available data and information likely to add the most value to the organisation, bearing in mind the associated costs and other relevant constraints.
* Organising, transforming, analysing, and assimilating data and information into forms that enable authorised users to access and generate insights from them appropriately, efficiently, and effectively.
* Managing data, information, and the resources their management consumes cost-effectively from conceptualisation to archival/disposal.”

Question 4

Define knowledge management in contet of DIKW model

Answer 4

“Knowledge management can be described as ““the process of capturing or creating, distributing, and effectively using knowledge”” (Davenport, as cited in Koenig, 2018). Information is either:
* Used in conjunction with existing knowledge to draw conclusions about the subject matter concerned, or
* Analysed to add to or modify the existing body of knowledge (or both).”

Question 5

The nature of wisdom - Western (2)

Answer 5

”* Theoretical – concerning ‘truth’ itself.
* Practical – essentially concerning whether or not a particular action should be done in a specific circumstance.”

Question 6

The nature of wisdom - Psychology (5)

Answer 6

“Wisdom
* Is interwoven with values and ethics.
* Is linked to practice and action.
* Is a mixture of cognition and emotion.
* Engages knowledge and experience.
* Goes beyond the personal level, aiming for the common good””.”

Question 7

The nature of wisdom - Management (10)

Answer 7

“The management view of wisdom tends to be ““a moral, epistemic [knowledge-based], and practical virtue geared towards achieving appropriate goals through appropriate management decisions and actions””

• Concerned with the fundamental aspects and uncertainty of human lives.
• Associated with possessing both knowledge and the ability to use knowledge and experience effectively.
• Dependent on experience. ““Although experience, in and of itself, does not necessarily lead to wisdom, it is essential and required for practical wisdom”” (Intezari & Pauleen, 2018, p.53).
• Practice-oriented, not merely theoretical.
• Ethical. Morality is considered an essential feature of wisdom. However, it must be remembered that different ethical approaches can lead to different conclusions from any given set of circumstances or facts. Accordingly, it is important to understand that there are various ways of thinking about things and possess self-awareness concerning ethical thinking.
• Self-transcendent. Practical wisdom is not limited to individualism but includes enhancing the well-being of others and possessing the awareness that personal actions have social consequences.
• Associated with good character (to make the right decisions) and judgement (to know what decisions need to be made).
• A unifying quality of rationality (cognition, reason) and non-rationality (feeling, intuition).
• Emotional. ““Wise people acknowledge the emotional component of all stakeholders including themselves when making decisions”” (Intezari & Pauleen, 2018, p.55).
• Self-aware. ““Wise people are aware of the limitations of their knowledge (Intezari & Pauleen, 2018, p.55).”

Question 8

Derscriptive statistics

Answer 8

“Describe the data
Central tendency (mean, mode, median
Dispersion (percentiles, std deviation)”

Question 9

Disadvanages of central tendancy

Answer 9

Mean = Outliers, mutli-modal

Question 10

How to calculate std deviation

Answer 10

“Calculate mean
Variance of each data point from mean - squared
Sum thse values
divide by number of values (or 1 less than the number of values if a population)
Square root of that”

Question 11

Inferential statistics

Answer 11

“Describe the inferences from the data sampolkes from large populations using probabilty theory
(Estimation and hypothesis theory)”

Question 12

How to calculate probability

Answer 12

“The probability of a specific event (Px) is the number of possible instances of that event x divided by the total number of possible outcomes.
The addition rule of probability states that if two events are independent, then the probability of one or another event occurring is equal to the sum of the probabilities of each event
The multiplication rule of probability states that if two events are independent, then the probability of their joint occurrence is equal to the product of their individual probabilities”

Question 13

Define statistics

Answer 13

Statistics are concerned with the collection and summary of empirical data. Statistical reasoning is inductive - it argues from observed specifics (e.g., an observed set of outcomes from a series of coin tosses) to broader generalisations.

Question 14

Characeteritsi of an ideal sample

Answer 14

“The ideal sample is a random sample of the target population of a suitable size to draw valid conclusions. In other words:
* Every member of the target population should have an equal probability of being selected as part of the sample.
* The population from which the sample is taken should be the target population. In case this point is less obvious, consider the case where the target population is females aged 25-40 years. Surveying every third person passing a point on a street does not target this population. Unless the street has been chosen for its special characteristics, the sample will most likely include males and females outside the targeted age range.
* There must be enough respondents in the sample to control sampling errors.”

Question 15

What is a confidence interval

Answer 15

The central question here is ‘how certain can we be that a statistic produced from a sample approximates the statistic that would have been produced from a census of the entire target population?’

Question 16

Confidence interval 6 stigma

Answer 16

The basis of Six Sigma is in fact “six standard deviations”. 99.99966% of values lie within 6 standard deviations of the mean. Turning this around, attaining Six Sigma quality means attaining a sustained standard of fewer than 3.4 mistakes per million opportunities to make a mistake.

Question 17

Estimation using students t-values

Answer 17

“Student’s t distributions are a family of probability models that are bell-shaped and centred on 0. There is more than one t distribution, each identified by a parameter known as its degree of freedom. Student’s t distributions become increasingly normal as the degrees of freedom rise, and a t distribution with infinite degrees of freedom is a standard normal distribution

Give the range for the true mean of a population from a sample

caclulate mean +/- (t-table value times std deviation divided by square root of the sample size)”

Question 18

Estimation using hypothesis testing

Answer 18

”

• The probability value (p-value) indicates the probability of a particular result when it is assumed that there is no relationship in the population (i.e., when the null hypothesis is true).
• The significance level is the decision rule, chosen in advance, which is applied to conclude that the null should be rejected or otherwise (e.g., 5%, 1%).

If the probability value more than or equal to significance level and the finding is statistically significant, then the null hypothesis cannot be rejected.”

Question 19

Data visualisation principles

Answer 19

”* Indicate how data values relate to one another?
* Represent the quantities involved accurately?
* Make it easy to compare these quantities?
* Make it easy to see a ranked order of values?
* Make obvious how the information should be used?’”

Question 20

Actors involved in visualisation

Answer 20

Designer and consumer

Question 21

Principles of data visualisation

Answer 21

”* Definging the goals
* Understanding the consumers
* Understanding the object of the visualisation
* Undertanidng the tools of visual representation”

Question 22

Laws of gestalt theory (visial perception) (6)

Answer 22

”* Proximity – things placed together in space and time tend to be perceived as a group, even if they are not similar.
* Similarity – things that have similar characteristics tend to be grouped in sets. Similarity occurs mainly in terms of colour, shape and texture. Normally, similarity is not overlapped by proximity.
* Closing – things are placed in a certain way to form an almost closed outline or incomplete shape that could become regular and stable. This draws upon the tendency of human perception to complete shapes by filling in the missing pieces.
* Simplicity – things are perceived more easily when they present symmetry and regularity without textures.
* Continuity – human perception tends to orientate things that appear to build a pattern or flow. Hence, things that display continuity are easier to perceive than those that indicate abrupt direction or relationship changes.
* Figure/background – any perceptive field can be divided into figures and backgrounds. The figure is distinguished from the background by characteristics like size, shape, colour and position. The object is only perceived as a figure after being separated from the background.”

Question 23

Guidelines for the use of colour in data visualisation include (5)

Answer 23

“Guidelines for the use of colour in data visualisation include (Kashyap, 2020; Tackels, n.d.):
* ““Use colour when you should, not when you can”” (Kashyap, 2020). Too much colour can overwhelm, whereas sparing use can highlight important features.
* Use the number of colours in a chart sparingly.
* Use categorical colours for unrelated data but variations in luminance to highlight differences in related data.
* Varying the type of chart can substitute for the use of colours.
* Minimise variations in the background colour. This is because human perception of the colour of an object is relative to the object’s surroundings.”

Question 24

Information science theory - Activity theory

Answer 24

“It is characterised by:
* Describing human activity as a dynamic, purposeful relationship in which people learn and grow, with the social context and tools used for the activity being continuously re-interpreted.
* Noting that tools extend the human ability to transform but restrict what can be done due to the tools’ limitations. This, in turn, often leads to the enhancement of the tools.

A key concept of Activity Theory is that consciousness is shaped by experience and the subjectivity of human awareness. Accordingly, activities, actions, operations and tools are what people think they are, and this can vary depending on prior experiences, culture and/or purpose.”

Question 25

Information science theory - Distributed cognition theory

Answer 25

“Edwin Hutchins developed the theory of distributed cognition. Its central tenet is that cognitive (computational) processes are generally best understood as distributed, particularly for complex sociotechnical systems. This contrasts with traditional cognitive theory, which sees individuals as the logical unit of analysis. Instead, the intent is to analyse at the systems level to reveal the system’s functioning, rather than the individuals who are part of the system.
Distributed cognition focuses on building models to capture the information flows, layouts and artefacts of systems”

Question 26

Information science theory - Situated cognition

Answer 26

“The central tenet of situated cognition theory is that knowing is inseparable from doing. All knowledge is situated in activity bound to social, cultural and physical contexts. This implies that knowledge and learning occur most effectively in situ rather than via the storage and retrieval of
conceptual understanding. It also states that cognition cannot be separated from context (activities, people, cultures, languages, etc.).

In this approach, relevant human factor knowledge is extracted by system designers and tailored to the specific operational demands and envisioned technologies via use cases (stories about users undertaking activities) and claims (testable hypotheses concerning requirements). This results in a requirement baseline created from the user’s perspective.”

Question 27

Information science theory - Decision theory

Answer 27

“Decision theory is also known as the ‘theory of choice’.

Normative decision theory analyses the outcomes of decisions, while optimal decision theory concerns why agents make the decisions they do.”

Question 28

Crawford and Hasan (2006) highlight a seven dimension framework based on Activity Theory

Answer 28

”* The activity system and activities being studied.
* People (actors) involved with these.
* Purposes of the activities and the motives of the subjects.
* Actions and operations undertaken and their rationales.
* Tools used.
* Culture and context within which activities, actions and operations are undertaken, and tools are used. A key concept of Activity Theory is that consciousness is shaped by experience and the subjectivity of human awareness. Accordingly, activities, actions, operations and tools are what people think they are, and this can vary depending on prior experiences, culture and/or purpose.
* Changes sought.”

Question 29

Bardram and Doryab (2011) identify four principles for systems design in specific settings from activity analysis methodology:

Answer 29

“1. Activity – Focus on human activity, creating activity-aware systems rather than context-aware ones.
2. Levels – Design for all three levels of human activity (activity, actions, and operations). For example, rather than supporting context-aware systems on an action level of a hospital ward round, incorporate support for the whole patient treatment.
3. Context – Take into consideration that all human activity is enacted through operations adjusted to the specific conditions of the real world. For example, the activities undertaken during surgery are adapted to contingencies arising during the procedure.
4. Collaboration – Since all human activity is collaborative, involving both concurrent and conflicting actions, systems design should consider how human actions are part of a larger social pattern. “

Question 30

The traditional approach to decision-making based on this theory entails: (5)

Answer 30

“List the possible choices
Identify the outcomes
Articulate the payoff/profit/reward
Select/.build a decision model
Apply the model and make a decision”

Question 31

limitations in decision-making (4)

Answer 31

“1. Rarely have perfect information
2. Search for a decision may be stoppeed as soon as the minimum acceptable level of rationality is reached
3. Decision making situation may involved multiple goals that cannot be simulanteoutly optimised
4. Individiuals do not behave rationaly, e.g. value losses more than wins”

Question 32

Cognitive fit theory

Answer 32

“It posits that problem-solving performance is superior when there is ‘cognitive fit’ (close correspondence) between the representation of the task that must be performed and the mode of presentation of pertinent information

Essentially, the theory notes that when:
* Humans consider the problem-solving task requirements and the problem representation (the relevant information). They form mental representations of the problem.
* The closer the ‘fit’ of the mode of problem representation to the requirements of the task, problem-solving task, the more conducive the mental representation to a faster, more accurate problem-solving process.
* On the other hand, mismatches between the problem-solving task requirements and the problem representation result in lower problem-solving performance.”

Question 33

Theory of reasoned action (technology acceptance)

Answer 33

“The theory of reasoned action is a psychological theory that suggests a person’s behavioural intentions depend on the interplay between their attitudes and subjective norms,

According to the theory of reasoned action, behavioural intention is the main predictor of behaviour as long as the conduct in question is specific with respect to the action required, the targeted outcome, context, and timeframe. The theory was originally developed to explain health behaviours. However, perceived limitations include some individuals’ inability to translate intentions into actions due to skill, knowledge, experience or other resource deficits.”

Question 34

Technology acceptance model

Answer 34

”* An individual’s actual use of an information system is predicted by their intention to use it.
* Their intention to use it is determined by their perceptions of its: Usefulness & Ease of use.”

Question 35

Diffusion of innovation theory (5)

Answer 35

”* Innovators. This cohort wants to be the first to try the innovation. It comprises risk-takers who are keen to embrace or develop new ideas. Little external motivation is required for this group.
* Early Adopters. This cohort comprises opinion leaders and change agents. They understand the need to change and are very comfortable adopting new ideas but want to do so sensibly. Resources targeting this cohort describe how to implement, not whether to.
* Early Majority. This cohort tends to be followers rather than leaders but will innovate as they see fit. People in this category typically need evidence that the innovation works before they are willing to adopt it. Resources targeting this cohort describe success stories and evidence of effectiveness.
* Late Majority. This cohort is sceptical about change and will innovate only after most of the population has done so. Resources targeting this cohort describe how many people have adopted the innovation successfully.
* Laggards. This cohort is very conservative and difficult to shift. Strategies targeting this cohort include social pressure from other adopter groups.”

Question 36

Stages of innovation theory (5)

Answer 36

“1. Knowledge – awareness of the innovation.
2. Persuasion – reasons why they should adopt it.
3. Analysis – information to help them decide whether to accept or reject the innovation.
4. Implementation advice – information about how they would embrace the innovation if they chose to accept it.
5. Confirmation – feedback about usage.”

Question 37

Social cognitive theory (technology acceptance) (6)

Answer 37

”* Behaviour results from the interplay between personal factors and the social environment.
* A person must possess the knowledge and skills required to perform the desired behaviour successfully, and they learn from the consequences of their behaviours.
* People can observe a behaviour performed by others and then reproduce (model) those actions.
* Positive or negative reinforcement will affect the person’s likelihood of continuing or discontinuing the behaviour.
* People have expectations about the consequences of their behaviours, largely derived from previous experiences and the value they place on the outcome. Expectations are subjective to the individual.
* Self-efficacy (a person’s confidence level in their ability to perform a behaviour) also contributes to their likelihood of attempting and succeeding at the behaviour.”

Question 38

Evaluation theories

Answer 38

“Theory-based approaches to evaluation use an explicit theory of change to draw conclusions about whether and how an intervention contributed to observed results”” (Treasury Board of Canada Secretariat, 2021).

"”A theory of change explains how an intervention is expected to produce its results [and ouitlines] the mechanisms of change, as well as the assumptions, risks and context that support or hinder the theory from being manifested as observed outcomes”” (Treasury Board of Canada Secretariat, 2021).”

Question 39

“The Data Governance Institute (DGI) defines data governance as:
including the 6 common focus areas”

Answer 39

”"”the exercise of decision-making and authority for data-related matters””

DGI highlights six common focus areas – policy, standards, and strategy; management alignment; architecture and integration; privacy, compliance, and security; data warehousing and business intelligence; and data quality.”

Question 40

DAMA International defines data quality management as

Answer 40

The planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data users” (DAMA International, 2017, p. 454

Question 41

DAMA also identifies several dimensions of data quality “about which there is general agreement” (10)

Answer 41

”* Accuracy – the degree to which the data correctly represents the facts.
* Completeness – the extent to which all required data is present.
* Consistency – the extent to which data values are represented the same way within and between datasets.
* Reasonability – the extent to which a data pattern conforms to expectations based on understanding the facts.
* Timeliness – this generally refers to the duration between the occurrence of a fact and the availability of data about it. DAMA also includes data currency (whether the data values represent the most recent version) and volatility (how frequently the data values change).
* Uniqueness – the property that exists more than once within a dataset.
* Validity – the extent to which data values are consistent with a defined domain of acceptable values.”

Question 42

7 Principles of the ABS Data Quality Framework

Answer 42

“1. Instiutional environment (Data producer)
2. Relevance (data producer)
3. Timeliness (data)
4. Accuracy (data)
5. Coherance (data)
6. Intepretability (data)
7. Accessibility (data)”

Question 43

Victorian Government’s Data Quality Guideline 7 data quality demensions

Answer 43

“1. Representativeness
2. Timliness
3. Fitness for purpose
4. Consistency
5. Collection
6. Accuracy
7. Completeness”