When we explore how to visually represent data, then we are delving into a whole new world of advanced mathematics and interpretive schematics. Because at its heart in order to deconstruct data into a visual matrix, it is first necessary to crunch a hell of a lot of numbers and see how all of the different parts slot together.
In a way being able to create a methodology for looking at data visually is very much a back to front science. Because it takes something that starts off quite complex, such as the raw data, and then attempts to transform it into something that is not only visually appealing, but which also manages to convey a message.
The most famous example of this that we all use on a daily basis is Google.
They are essentially a web data company, who mathematically crunch the data of the raw websites that their robots look over, and then with the aid of their famous algorithm turn that data into something that we can all interpret as the answer to the questions that all of us pose Google on a daily basis.
There are a number of different ways that data visualization can work in practice, especially once the number crunching has been done.
When you are left with the raw data then a whole array of opportunities opens up because then depending on what you want to achieve, you can present the data in different ways.
So, for example, this may be in the form of:
- Bar Charts
- Mind Maps
- Mathematical Algorithms
Or any number of other ways.
The key thing to remember with visualizing data is that the method that you choose is integral and important to how the data will end up being used.
So for example, while you can present the structure of the internet as a mathematical algorithm (and Google do internally to the people working on it), when they are trying to show how the internet looks to the world at large then they present their data visualization techniques in a totally different way, and show the world the list of results that we all use every day.
This has the effect of allowing a movement from a macro way of viewing the world to a micro view, as we hone in from the raw data itself to a presentation of what it means that we can all understand and appreciate.
The data itself has not changed, but the way that it has been presented has changed, and in that process it ends up revealing a whole lot more about its meaning.
The ability to transform raw data into formats that we can better understand and interpret allows data crunching to turn from a functional into a creative process.
Because the way that the data is presented can have the effect of totally changing the way it is viewed, even if the facts are the same.