What is data? It seems like a simple question, but the answer tends to be exceedingly complex.
To some, data is information — any type of information humans can gather. Eyes gather visual data; ears gather auditory data; taste buds gather gustatory data, etc. Since long before humans learned to walk, brains have been collecting these kinds of data to help organisms make decisions. Yet, these types of data are not easy for the human brain to quantify, and as a result, the types of data human senses can collect aren’t particularly practical for making organization-level decisions in the modern world.
Thus, to most, data isn’t just information; it is information that has been translated into a usable form. What makes this kind of data usable, and how can business leaders use it to improve their business practices? To understand the answers to these questions, business leaders need to dive into the different types of data at their disposal.
Types of Data
One of the first lessons of a data science essentials course is that there are two types of data: qualitative data and quantitative data. Understanding these broad categories of data is essential to understanding the more specific types of data that fall beneath them.
Generally, quantitative data is what most people imagine when they are discussing data. As the name suggests, quantitative data can be quantified — that is, it can be measured and expressed in numerical values. Thus, it is easy to manipulate quantitative data and represent it through statistical graphs and charts.
Quantitative data usually answers questions like “How much?”, “How many?” and “How often?” Some examples of quantitative data include a person’s height, the amount of time they spend on social media and their annual income. There are two key types of quantitative data: discrete and continuous.
Discrete data exists as whole numbers. Discrete data cannot be subdivided into smaller parts. For example, a business cannot receive half an order or serve one-third of a customer; these numbers must remain whole, as integers.
Continuous data is measured on a scale. It is possible to divide continuous data into different levels of fineness. For example, a business might measure the size of its product in inches, in fractions of inches, in meters or in micrometers, depending on how precise they want to be. Continuous variables can take any value.
Compared to quantitative data, qualitative data is much more difficult to work with. Qualitative data cannot be expressed in numerical values; it exists as words, images, objects and symbols. Sometimes qualitative data is called categorical data because the information must be sorted into categories instead of represented by numbers or statistical charts.
Qualitative data tends to answer questions with more nuance, like “Why did this happen?” Some examples of qualitative data business leaders might encounter include customers’ names, their favorite colors and their ethnicity. The two most common types of qualitative data are nominal data and ordinal data.
Nominal data is a name. In data science, nominal data is most often used as labels for quantitative charts and graphs. For example, a business might use nominal data like marital status (married or single) to understand its consumer audience. There is no intrinsic ordering of nominal data; it doesn’t matter whether “married” or “single” appears first, but it is imperative that businesses have such labels to inform their qualitative graphs and charts.
Ordinal data must follow a predetermined pattern. A customer’s economic status is an example of ordinal data: High-class individuals are ranked above lower-class individuals. Though ordinal data can sometimes use numbers, it is impossible to perform statistical analysis as one would with quantitative data because ordinal information can only show sequence. Business leaders can use ordinal data to understand relationships amongst qualitative data.
Quantitative data is the easiest data for business leaders to understand, but it isn’t the only data worth collecting for business benefit. Those looking for more information about the types of data important to business practices and how to leverage that data now and into the future should pursue some education in the field of data science, which can dig deeper into these topics and provide business leaders with the guidance they need.