When asked the question “What is information?” many people might give an answer similar to it being “information structured in a certain way”.
However, while Data and Information are closely related, the difference usually lies in complexity.
You see, data is a collection of facts. Out of context, they don’t mean much.
Information, on the other hand, is basically organised data within a certain context.
For example:
“SEVEN” — this is data. Seven what exactly? We don’t know the context. It could be someone’s age, the amount of something, or simply someone’s favourite number.
As soon as we put the data into context, it becomes information. So, “seven years” is information, even though the context is still very broad.
So, what is knowledge?
Knowledge is basically refined and useful information, gained through experience. As opposed to general information, it cannot be transferred through simple means — it requires learning.
All of this article is information. While it certainly is profound to me, and it might be to you, it is relatively easy to transfer to you, the reader.
However, something like cooking, for example, requires you to learn the process through experience.
You may say: “Wait a minute! I can read a cookbook!”
And you’d be right. The cookbook contains information. You process this information (how much flour to add, when to lower the heat, when to add what ingredient…) and then you are ready to cook, right?
Well, if you’ve never cooked before (and you lack the knowledge) you are probably going to have a hard time getting everything just right. Of course, you might be naturally talented and everything goes smoothly.
Either way, through this process you have acquired knowledge. It is this learning process that lets you transform the information at hand into new knowledge.
If you are naturally talented and you nailed it the first time, it simply means that it took you just one attempt to successfully do so.
Data Visualisation: The structuring of data
Of course, if you intend to transform data into information (and then possibly into knowledge) you need to put it into context.
When the data is in larger quantities, it becomes hard to keep track of it. This is why you want to organise and structure it.
Now it is important to distinguish between who or what you are visualising the data for.
Machines and computers have an easier time navigating through simpler means of visualisation, such as data tables (think Excel).
On the other hand, the human eye is much happier to go through visual charts and graphs. We understand them better because process visual stimuli (pictures, images…) much better and faster than written information.
If you ever heard of the phrase ‘One picture is worth a thousand words’ or ‘Why don’t I just show you, instead of telling you?’ then this is probably the reason why.
A very useful thing to know is the influence of pre-attentive attributes in your data visualisations. They are the following:
- Shape
- Orientation
- Colour (Hue & Value)
- Size
- Position
- Order
These attributes are essential in telling the right “story” with your visualisation. They shape how your audience perceives what you are conveying to them.
You can use these attributes to highlight certain information and make it stand out, or organise the information in a certain order to make it more comprehensible to your audience.
Conclusion
We have looked at the difference between data, information, and knowledge. While all of them cover facts, each of them is at a different level of complexity. All information is structured and organised data and all knowledge is data that is specifically acquired through experience and learning.
We perceive information in a certain way and one of the easiest to understand is in the form of visualised data. There are certain factors called pre-attentive attributes which influence the way we look at information visualised in this way.
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