A long time before I was embedded in the agricultural industry, I started my career aiming to be in IT. One of the lessons that I have taken from my studies in IT and frequently refer to is the ‘Data to Wisdom’ pyramid.
My work throughout the years is grounded in this framework.This post has a brief description of it.
The “Data to Wisdom (DIKW)” pyramid was one of the rudimentary hierarchies that I learnt in one of the introductory courses in IT.
However it’s importance has stuck with me, and its ability to be applied to different situations. In my instance, analysing agricultural markets.
The DIKW framework is a simple hierarchical model which sows the journey from data to wisdom. Although in my usage, I adopt decisions instead of wisdom, as wisdom seems somewhat pretentious.
The four components generally used are data, information, knowledge and wisdom.
My rudimentary interpretation of the hierarchy is below:
Data: Data is raw, unorganised signals. At this stage, there is no meaning to the data. It’s just numbers.
Information: At this stage, the data has been organised into a useable structure. This organisation should answer the questions of what, when or who. The information now has a useful meaning.
Knowledge: Knowledge is the use of learnings to turn information into contextualised insights. This allows the generation of ideas and analysis.
Wisdom/Decisions: This is where knowledge ‘this is what is happening’ is turned into a decision ‘ this is what I am going to do’.
Wisdom/decisions are the pinnacle where all the work is converted into ‘data-based decisions’.
The summary is that data is only the first step. An organisation can have significant datasets however if they lack the experience or analytical skills to turn that data into something meaningful then it is irrelevant.