About pyramids and snow – the difference between data, information and knowledge

Pyramids and snow? Yes, you didn’t misread the headline. Welcome back on my blog to “Knowledge Wednesday”. Within my first post, I introduced you to the the aphorism “knowledge is power” and investigated what impact helping others has on intrinsic motivation and life satisfaction. Within this blog post I would like to focus your attention on knowledge itself, in particular the different forms of knowledge.

The truth is: hardly any other resource is as diverse and complex as knowledge. Yet, if I had to ask you right now …

“What is knowledge?”

… it would probably be difficult for you to put the idea you have into words, wouldn’t it? You might be wondering, who would actually ask these kinds of questions, but be assured, if you take a class on Research Theory and Ethics and you come across the term “epistemology” and you will definitely remember reading this blog post. Epistemology is the theory of knowledge. If you want to know more about that philosophical approach, this six minutes Youtube video explains it pretty well (Spoiler Alert: it even contains cat content).

The Difference between Data, Information and Knowledge

How does data transform into knowledge? Have you ever even thought about the difference between these terms? One way to distinguish between data, information and knowledge is to think of it in terms of blocks which build upon each other, as does the DIKW hierarchy model.

At the bottom of the pyramid, there is data, loosely gathered  numbers, symbols and signs without contextual information – an unorganized and unprocessed accumulation of … nothing? Companies are thirsty for data as it represents the basis for developing knowledge. Data can be considered one of the biggest trends in both tech and business and companies are therefore responding to this trend by advertising new positions such as “Data Scientist (m/w/d)” – a person who is both good at statistics and software engineering. The good news: you don’t need to be a Data Scientist to manage knowledge (at least not from a technical perspective). Once you start making use of the data by for example classifying it i.e. by putting the data into context and assigning meaning to it, you gather information. This can be done by asking the W-questions: Who, What, When and Where?

Moving one step further up the pyramid, we create knowledge by structuring information in a way that it is useful for our purposes. If people make reasonable use of the data  available to them, they first discover relationships between variables (= information) and later detect patterns (= knowledge). At the top of the pyramid is wisdom – a state within which we deeply comprehend the “Why” behind patterns and can make educated assumptions about the future.

Let us look at the example of snow to better understand the DIKW model.

• Data: It’s snowing
• Information: The temperature dropped below 0 degrees, the humidity went up by 5% within the last two hours and it started snowing at 5 p.m.
• Knowledge: Due to the drop in temperature and the increase in humidity, water particles in the cloud froze to ice on tiny grains of dust. More and more particles gathered together which renders the atmosphere unable to hold them anymore. On their way down to earth, the remaining water vapor freezes and crystals form which connect and hook together thereby creating a snowflake.
• Wisdom: Based on previous observations and statistical models, we are able to predict the weather in the future. We developed an understanding of the different interactions between temperature changes, evaporation and snow.

Tacit vs. Explicit – Forms of Knowledge

Managing knowledge requires to exactly know in which form the knowledge is present as this determines the approach to storing, retrieving and sharing  it. Knowledge that is present within the mind of a person is called tacit or embodied knowledge and is based on all ideals, values and feelings a person holds (North, 2011). On the contrary, explicit knowledge exists unconnectedly for example in databases, documents and other notes and is therefore called disembodied knowledge. Managing explicit knowledge requires the help of IT which ensures that the right people have access to the right kind of explicit knowledge at the right time.

But I don’t want to bore you with any further definitions or models. Let us go back to the beginning of this article. Would you now be able to answer the question “What is knowledge?”? Let me know what you think in the comments section below!  Stay tuned for more knowledge insights! 

Bibliography

Figueroa, A. (2019). Data Demystified — DIKW model. Retrieved 12 March 2020, from https://towardsdatascience.com/rootstrap-dikw-model-32cef9ae6dfb

Hajric, E. (2018). Knowledge Information Data. Retrieved 12 March 2020, from http://www.knowledge-management-tools.net/knowledge-information-data.php

North, K., (2011). Wissensorientierte Unternehmensführung: Wertschöpfung durch Wissen. Wiesbaden: Gabler-Verlag.

Würsten, F. (2017). SimplyScience – Wie entsteht Schnee?. Retrieved 12 March 2020, from https://www.simplyscience.ch/teens-liesnach-archiv/articles/wie-entsteht-schnee.html