Lecture Pod 2 – Data Types

lecture 2 image

*This image was taken directly from the lecture.

This lecture pod was about one thing and one thing only – Data Types. There are four different types of data listed in this lecture. First was Nominal, then Ordinal, Interval and lastly Ratio.

Nominal data (pertaining to names) is named categories that can be counted and used to calculate percentages but you cannot take averages from them. The best example of this would be a supermarkets section (e.g. dairy, produce, canned & frozen), each category would be classes as nominal data.

Ordinal data is all about order. This type of data can be counted and be used to calculate percentages. There is also a debate about whether this data can be used to take averages, but I won’t go into it (my opinion is yes, it can). This data has no true mathematical value with numbers being assigned to make data analysis easy. The numbers just need to be in order. The best example of this data would be calculating which line will get you out of the supermarket quickest (long line, medium line, short line etc.).

Interval data refers to data in which the interval between each point is fixed. This type of data is numeric. The best example of this would be time, there is always a set amount of seconds in each minute making it interval data. In this type of data, 0 does not mean that nothing is there (0˚C does not mean that there is not temperature).

Lastly there is ratio data. This type of data is numeric, like interval data but differs from it in one key aspect; it has a meaningful 0 point. To put it clearly, in this type of data, 0 is the absence of anything. Examples of this data would be height, age, weight and money.

I believe however that the most important point made in this lecture was made early on, about whether data types matter. The answer is obviously yes, otherwise we wouldn’t use them but the reason for this is exceptionally simple. We use data types to prevent mistakes, like a postcode being interpreted as a pin code or something like that. This is the most important point because it drives home one simple point to me, it is easy to make mistakes, but it is easier to avoid them if you label your data correctly.


Waterson, S. (2016). DataVis POD02- Data Types. Retrieved from https://vimeo.com/176274669


4X4 Model for Knowledge Content

The 4×4 Model for Knowledge Content is a guide to getting people to engage with your website or online content brought about by the fact that many people will only spend 10 seconds on your website and even then most will only skim it. So your content needs a way to stand out.

The 4 models here were:

  1. The Water Cooler – Typically a headline. Content is succinct, direct and compelling. Its purpose is to grab your attention.
  2. The Cafe – Where the content is explored with more details. NOT a deep study. A progression from water cooler that explains the ideas not just introduce them.
  3. The Research Library – This is where you dig deep. Contains research and data to back up the water cooler and the cafe.
  4. The Lab – Users interact with the data from the research library. Rarest form of content but also the most powerful. Gives the users access to the data to interpret any way they like.

There are also four components involved here.

  1. Visualisation
  2. Story-Telling
  3. Interactivity
  4. Shareability


This following diagram was quickly created by me to better show this system. There was a visualisation similar to this was used in the video, but I modified it to better represent this method.


All information for this post was retrived from Bill, S. (2014). The 4X4 Model for Winning Knowledge Content. Vimeo. Retrieved 26th July 2016, from https://vimeo.com/100429442

Lecture Pod 1 – Introduction to Data Visualisation

There were a few important points made throughout this first lecture. First was that there is more data now that at any point throughout history. 23 exabytes (1 exabyte = 1 billion gigabytes) of data was recorded and replicated in 2002 (UC Berkeley’s School of Information Management and Systems, 2003). We now do that in seven days . Richard Saul Wurman (1997) said “There is a tsunami of data that is crashing onto the beaches of the civilised world. This is a tidal wave or unrelated, growing data formed in bits and bytes, coming in an unorganised, uncontrolled, incoherent cacophony of foam. None of it is easily related, none of it comes with any organisation methodology…”. Another important point made is that data itself has no meaning. It does not become information until someone interprets it.

But out of all these I think the most important point was the first one made. Data Visualisation is a mass medium. It has millions of viewers, award shows and even celebrities. It is an essential part of the communication medium, a data driven story without some form of visualisation is like a fashion story without a photo. The reason I think this is the most important point is because it made me realise just how big data visualisation is. There is so much data around today that it now blends into the background and this point made me realise just how much data we consume every day without even registering it.


The featured image here is a data visualisation of the most popular running routes in major cities, this one is New York (Yu, 2014). The data was pulled from the workout app Runkeeper. Darker means there is more traffic, lighter is less travelled.


UC Berkeley’s School of Information Management and Systems,. (2003). How much Information?. University of California, USA. Retrieved from http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/execsum.htm

Waterson, S. (2016). DataVis POD01- What is Data Vis?. Retrieved from https://vimeo.com/175177926

Wurman, R.S (1997) Information Architects, Graphis Inc; USA

Yu, N. (2014). 10 Cool Big Data Visualizations | MastersinDataScience.org. Master’s in Data Science. Retrieved 25 July 2016, from http://www.mastersindatascience.org/blog/10-cool-big-data-visualizations/