The subject of data can be a bit dry, and alone they don’t offer much in the way of meaningful interdisciplinary application, whether planning finances or even trying to decipher what you are being told in the age of bullshit.
But to get there, you have to begin with the basics. So, what are data?
Data can be considered as the observations or measurements. They are what is processed and analysed to gain insight into a particular problem and help develop strategies to solve them.
They can be divided into two broad types; qualitative and quantitative.
Qualitative data
Qualitative data is non-numerical, such as hair colour, species or even market sentiment; it describes rather than counts.
It can appear subjective, particularly in cases where descriptions haven’t been standardised.
Quantitative data
Quantitative data has a numerical value that can be counted or measured.
It could be as simple as weight in kg or the frequency of observations in a local butterfly count.
Quantitative data can be further described as either continuous or discrete.
Continuous data can take any value within a range to measure variables such as temperature or Formula 1 lap times. It can be recorded to various decimal points, creating an effectively infinite range of possible values.
Discrete data, on the other hand, refers to a finite set of values that cannot be subdivided into parts, such as the number of tourists that visit a city. They are whole numbers, as it’s not possible to have one half of a tourist.
Application of data
Data is a universal topic. In marketing it is used to determine and measure KPI, in ethical studies it determines bias in systems and in machine learning it can help develop algorithms to better understand species distribution.
The field in which it is applied and the problem that needs solving will determine the depth of understanding required to make the most of it, which could mean exploring algebra, statistics and probability.
But to describe, compare and draw conclusions from data requires the same underlying foundations; knowing what data is, how it is measured and classified, and how to summarise it in ways that are actually useful.