The Knowledge Pyramid

The knowledge pyramid and AI

The conceptual difference between AI and IT is that AI is based on knowledge while IT is based on data. So, AI can be said to be a knowledge-based technology, and IT is a data-based technology. And what does that mean?


The concept called the Knowledge Pyramid can help us gain perspective on the difference between IT and AI.


The Knowledge Pyramid does not, as a concept, have anything to do with technology. The concept explains levels of enriched knowledge. Specifically, the pyramid tells us that knowledge can be enriched on four different levels.


As a thorough example that can explain the four levels, we use a weather station. Let’s say you’ve set up a digital weather station on the roof of your house. It measures temperature, humidity, air pressure, wind direction, and wind speed. You can see the weather information on a monitor in your house, and perhaps also on an app on your Smartphone.


Your weather station probably works with all of the four levels of knowledge in the Knowledge Pyramid.


The lower level of the pyramid is called data. Data is unstructured and unprocessed information. In our example, it is the particular weather measurements that the weather station records. So, data is the result of the measurements in raw form. All these measurements are numbers. If you put them in a spreadsheet, you would just get rows of numbers.


If you showed your weather measurement data to other people without telling them where they came from, then nobody would be able to use them for anything. For anyone but you, they would just be numbers in a spreadsheet. Maybe some could say that they were dealing with temperature or wind, but that would be guesswork.


That is the essential characteristic of data in its raw form.  Data in itself has no value because looking at data alone does not make us understand what it is used for. This is not possible for us, because the context of the data you are looking at is not known to you.


That’s why we have information. For now, there is an understanding level that gives data value. Now we put units on our weather data. So, our spreadsheet now consists of data in a structure. For example, the information is divided into time intervals. The temperature measurements are shown with Celsius as the unit; we show the wind direction as degrees, etc.


What we do now is to create structure in our data, and thus we enrich it so that it is no longer data. It’s information. In our new spreadsheet, other people will now have a realistic chance to understand what the numbers in the spreadsheet mean.


The preconditions for extracting knowledge from the spreadsheet are that you know what Celsius and degrees are. People know this because they have learned it. When you look at numbers with these units attached, you decide that it is probably weather numbers because you have knowledge and learning that make it possible to see a logical link from the numbers in the spreadsheet to weather information. What you do is professionally called deductive learning.


In doing so, we move up to the third level of the knowledge pyramid. Here, we find knowledge. By looking at our numbers in the spreadsheet, we know that we are looking at weather observations. It is an understanding that comes from a combination of the information we see in the spreadsheet and our knowledge. We know, for example, that the number 21 degrees Celsius means it is warm enough to wear a T-shirt if you need to get out of the house.


The next – and last – level is wisdom. Now we start acting on our knowledge. If we see a rapid drop in air pressure and changes in wind direction, then we know that there is a high risk of rain. So, we go out and remove the cushions on the patio furniture. Such actions are guided by a reflection of what we can deduct from the weather information. So, we are doing something because we are dealing with the consequences of the knowledge we get from our spreadsheets.


Thus, the lowest level of the pyramid of knowledge is data, and the highest is wisdom. The definitions of the four levels are:


  • Data: A collection of facts in raw or in unorganized form.
  • Information: Organized and structured data that has been cleared of errors. It can, therefore, be measured, analyzed, and visualized.
  • Knowledge: Learning is a central component of knowledge. Here you learn based on insights and understanding of data and information.
  • Wisdom: The highest level is wisdom. Here, reflection is the central component, as well as being an action-oriented stage. You act based on the understanding you have.


The key to knowledge is that you use it as a starting point for making decisions.


But there is a huge difference between a decision-making process based on information or knowledge, especially when it comes to tech solutions.


[1]The concept is described on this Wikipedia page:

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