Embedded Files
Prescribed enduring understandings
Prescribed enduring understandings
There are many types, uses and ways of representing data.
Big data and data analytics involve extracting and processing useful information in ways that are often impossible for humans.
There are significant opportunities and dilemmas associated with data in digital society.
(IB. Digital Society Guide, 2022)
3.1.A
3.1.A. Data as distinct from information, knowledge and wisdom.
Opening Exercise:
1.1.A. “Digital society” is a contested term used to describe contemporary life. There may be multiple digital societies rather than just one. (IB DP, Digital Society Guide)
The concept of the Fourth Industrial Revolution was coined in 2016 by Klaus Schwab, the founder of the World Economic Forum, in a book of the same name. "The Fourth Industrial Revolution creates a world in which virtual and physical systems of manufacturing cooperate with each other in a flexible way at the global level".
(source)
Opening task:
In the enduring understanding that digital society is a contested term, discuss the meaning of digital society.
In Activity #5, you will need to recall your meaning of digital society.
The DIKW model
The IKW model was published in 1982 by The Futurist magazine. This progressed into the DIKW model.
Knowledge, Wisdom (DIKW) Pyramid?
Knowledge, Wisdom (DIKW) Pyramid?
The DIKW Pyramid represents the relationships between data, information, knowledge and wisdom. Each building block is a step towards a higher level - first comes data, then is information, next is knowledge and finally comes wisdom. Each step answers different questions about the initial data and adds value to it. The more we enrich our data with meaning and context, the more knowledge and insights we get out of it so we can take better, informed and data-based decisions. (source)
Activity #1
Discuss the difference between data and information.
Contrast Data and information, clearly stating your understanding of the differences.
Present your group's ideas to other groups in the class.
Definitions
Data: Discrete pieces of facts, perceived facts, images or other information that has not been processed or manipulated. It may or may not have meaning.
Information: Data that has been processed and has perceived value or true value.
Knowledge: the fact or condition of knowing something with familiarity gained through experience (source).
Wisdom: ability to discern inner qualities and relationships (source) and can apply to situations to create lasting value or benefit.
Activity #2
What is the value of the DIKW model?
Is there more wisdom than data? Explain your answer.
To what extent is it true that data, information and knowledge belong to the past?
The origin claims of DIKW.
The origin claims of DIKW.
Interestingly the first ever mention of the hierarchy is neither in the Knowledge Management field, nor the Information Science domain, but in an unexpected place: poetry. In his Futurist article, Cleveland cites T.S. Eliot as the person who suggested the hierarchy in the first place. Cleveland names it “the T.S. Eliot hierarchy”. The poet T.S. Eliot was the first to mention the “DIKW hierarchy” without even calling it by that name. In 1934 Eliot wrote in “The Rock”:
Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
(Source)
TOK Activity #1:
How likely, in all of human history, is it that Eliot was the first literary to recognize this philosophical stance?
To what extent do Eliot's words have value for us?
An alternative view of the DIKW model.
An alternative view of the DIKW model.
The Problem with the Data-Information-Knowledge-Wisdom Hierarchy
The Problem with the Data-Information-Knowledge-Wisdom Hierarchy
The real problem with the DIKW pyramid is that it’s a pyramid. The image that knowledge (much less wisdom) results from applying finer-grained filters at each level, paints the wrong picture. That view is natural to the Information Age which has been all about filtering noise, reducing the flow to what is clean, clear and manageable. Knowledge is more creative, messier, harder won, and far more discontinuous.
(source)
Activity #3.
What are the limitations of the DIKW model, if any?
To what extent do you agree/disagree that knowledge is "more creative, messier, harder won, and far more discontinuous". What does this statement lead us to think, in contrast, to data?
To what extent can data be seen as "noise"?
Data
Data
Data can be defined in several different ways.
Data as fact
"Most people believe their perception of a situation to be a reality, i.e., to be fact. They believe their interpretation of what happened– their perception of fact – to be fact. Facts, however, are the direct, objective observation of what happened, e.g., a deadline not met, a promise not delivered, a statement made." (source)
Data as symbol
Emojis, logos, words, numbers, diagrams, simple structures, and other symbols that are easily recognizable.
Activity #4. In your own words,
Define data.
Define information.
Define knowledge.
Define wisdom.
Discuss where intelligence emerges in the DIKW model
(Special note: if you google this information, you will come up with another model of the DIKW model. This other model is not required by 3.1A.)
Activity #5.
What is the value of data in a digital society?
(Think about your definition of digital society at the beginning of 3.1.A.)
3.1.B
Types of data.
3.1.C
3.1.C. Uses of Data
3.1.D
3.1.C. Data life cycle.
3.1.E
3.1.C. Ways to collect and organize data.
3.1.F
3.1.C. Ways of representing data.
3.1.G
3.1.C. Data security.
3.1.H
3.1.H Characteristics and uses of big data and data analytics.
3.1.I
3.1.I. Data dilemmas.
Control, ownership and access to data.
Control, ownership and access to data.
Cross-border data flows are not trade and should be treated differently.
Cross-border data flows are not trade and should be treated differently.
The particular characteristics of data suggest that they need to be treated differently from conventional goods and services, including in their international transfers. In the new context of the data-driven digital economy, concepts such as ownership and sovereignty are being challenged. Rather than trying to determine who “owns” the data, what matters is who has the right to access, control and use the data. There are significant difficulties in reconciling the notion of national sovereignty traditionally associated with country territories and the borderless nature, globality and openness of the digital space in which data flow. Digital sovereignty is often associated with the need to store data within national borders, but the link between the geographic storage of data and development is not evident. Assigning territoriality to cross-border data flows is also a challenge. Data can be better understood as shared, rather than as traded or exchanged.
Activity #1.
Activity #1.
"Rather than trying to determine who 'owns' the data, what matters is who has the right to access, control and use the data."
What is your response to this statement from UNCTAD?
Imagine your personal data is parceled with other people's personal data, accessed, controlled and used by others. What future is opened by this practice?
What is the benefit of controlling and using data that can't be 'owned'?
Concept: 2.4 (Power) and 2.7 (Ethics)
Content: 3.1.I. (Data dilemmas)
Context: 4.2.1. (Business) and 4.6 (Political) and 4.7 (Social)
Activity #2.
Activity #2.
TikTok's data dilemma.
TikTok's data dilemma.
Open the video and listen to the report about the Chinese-owned social media company.
"You can't share data on US citizens outside of US soil, but how do you check and make sure?"
To what extent does this statement from the reporter contradicts the report from the UN in activity #1 above?
What data do you think competing nations should be allowed to access and use about each other's citizens?
To what extent should allied nations be given access to each other's data on their citizen population?
Concept: 2.4 (Power) and 2.7 (Ethics)
Content: 3.1.I. (Data dilemmas)
Context: 4.2.1. (Business) and 4.6 (Political) and 4.7 (Social)
Activity #3.
Activity #3.
Big Data's Big Trust Issues.
Big Data's Big Trust Issues.
Source. Raconteur. 2022.
In order to restore consumers' trust, do businesses need help from government or other sectors? What strategy(ies) can you recommend?
Concept: 2.7 (Ethics)
Content: 3.1.I. (Data dilemmas)
Context: 4.2.1. (Business) and 4.7 (Social)
Page updated
Google Sites
Report abuse