Michael Grieves, recognized for pioneering the concept of digital twins, critiques the traditional model for going from data to wisdom in a new preprint article published on Preprints.org.
In the piece, DIKW as a General and Digital Twin Action Framework: Data, Information, and Wisdom, Grieves argues for a refined DIKW model that’s more practical and applicable to digital twins and AI, emphasizing goal-oriented tasks and the functional roles of data, information, knowledge, and wisdom.
Grieves says there are two core problems with the DIKW pyramid model that undermine its usefulness in real-world applications.
“The first is that there is little or no consensus as the what the DIKW elements, data, information, knowledge, and wisdom, mean,” he writes. “The second problem is that there is no explanation of how these elements are of any use or create value. These two problems are related. It’s impossible to create value when there is no agreement on what the individual elements are or what they do.”
In the context of digital twins and AI, Grieves proposes a simplified DIKW hierarchy:
- Data is collected facts
- Knowledge is the storage of this data and information
- Wisdom is choosing the best info for a task
Using this model, Grieves argues, AI-enhanced digital twins can efficiently simulate and predict outcomes.
Read the full article below.