A very basic topic of UGC NET Library and Information Science which is easy to understand. Give it a read (5 minute read) post.
The DIKW hierarchy (Data, Information, Knowledge, Wisdom) explains how raw data transforms into actionable wisdom through processing, understanding, and application. It is central to information science, knowledge management, and decision-making.
Data
Examples:
Data has no meaning or value because it is without context and interpretation (Jessup andValacich, 2003;Bocij et al., 2003;Groff and Jones, 2003)
Data is raw, unorganised and doesn't have any particular structure.
Answers: "What."
Examples: Dates, numbers, and isolated facts like;
Characters in a book.Bits in computer memory.Street signs.
Information
Information is data which adds value to the understanding of a subject (Chaffey and Wood).
Information is organised and structured data, which adds meaning to the data and gives it context and significance.
Answers: "Who," "What," "Where," and "When."
Reading a book.Watching a movie.
Knowledge
Knowledge is the combination of data and information, to which is added expert opinion, skills, and experience, to result in a valuable asset which can be used to aid decision making (Chaffey and Wood, 2005, p. 223).
Knowledge is the ability to use information strategically to achieve one’s objectives.
Answers: How?
Example: The Sun is at the centre of our solarsystem.
Wisdom
Wisdom is accumulated knowledge, which allows you to understand how to apply concepts from one domain to new situations or problems (Jessup and Valacich, 2003).
Wisdom is the capacity to choose objectives consistent with one’s values within a larger social context.
Answers: Why?
Example: We should reduce carbon emissions.
Note: The DIKW pyramid shall be enhanced with the 5th step – the decision itself.
Historical Development of DIKW and Extended Models:
Core DIKW Model:
Origin:
Russell L. Ackoff (1989): Formalized the DIKW model in his paper "From Data to Wisdom."
Early Influences:
Michael Polanyi (1958): Differentiated tacit and explicit knowledge, influencing DIKW.
T. S. Eliot (1934): Referenced the wisdom hierarchy in Choruses from ‘The Rock.’
> “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”
Extended Models and Their Contributors:
1. DIKWP (Data-Information-Knowledge-Wisdom-Processing):
Introduced By: Researchers in Artificial Intelligence and Big Data.
Purpose: Highlights the role of computational processing between levels, particularly in automated systems.
Date: First formalized in the 2000s during the rise of data-driven technologies.
2. DIKWH (Data-Information-Knowledge-Wisdom-Holism):
Introduced By: Researchers in Systems Thinking.
Purpose: Adds "Holism" to stress interconnectedness and the systemic view of knowledge.
Date: Emerged in the late 1990s and early 2000s.
3. IKW (Information-Knowledge-Wisdom):
Introduced By: Practitioners who argued data is implied in the hierarchy.
Purpose: Focuses directly on the transition from information to wisdom.
Date: Gained prominence in the 1990s.
4. DIKUW (Data-Information-Knowledge-Understanding-Wisdom):
Introduced By: Gene Bellinger and colleagues in their work on systemic thinking.
Purpose: Adds "Understanding" as a separate stage to emphasize the cognitive leap between knowledge and wisdom.
Date: Early 2000s.
5. DIKHE (Data-Information-Knowledge-Human Experience):
Introduced By: Researchers in Human-Centered Design.
Purpose: Focuses on the integration of human experience in applying knowledge to achieve wisdom.
Date: Developed in the 2010s.
Quotes Related to DIKW:
1. T. S. Eliot (1934):
From Choruses from ‘The Rock.’
“Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”
2. Russell Ackoff (1989):
“Data are symbols; information is data that are processed to be useful; knowledge is the application of data and information; wisdom is evaluated understanding.”
3. Peter Drucker:
"Knowledge has to be improved, challenged, and increased constantly, or it vanishes."
Applications:
Library and Information Science: Organizing and managing resources.
Knowledge Management: Framework for organizational decision-making.
Artificial Intelligence: Automating the DIKW transitions.
Education: Guiding curriculum development and critical thinking.
Criticism and Alternatives:
1. Criticism:
Linear progression oversimplifies complex relationships.
Overlooks feedback loops and dynamic processes.
2. Alternatives:
Cyclic Models: Incorporate interdependent relationships between levels.
DIKWP and DIKWH: Address criticisms by emphasizing processing and systems thinking.
Modern Usage:
Big Data Era: Focus on transitioning unstructured data into wisdom.
AI Integration: Automates stages from data to knowledge and beyond.
Future Variants: DIKW+ models include creativity, ethics, and sustainability.