What is data? Data types, explained
What is data? It sounds like a simple question, but the answer tends to be extremely complex.
For some, data is information – any kind of information that humans can collect. The eyes collect visual data; the ears collect auditory data; taste buds collect taste data, etc. Long before humans learn to walk, the brain collects this kind of data to help organisms make decisions. Yet these types of data are not easy for the human brain to quantify, and therefore the types of data that the human senses can collect are not particularly practical for making organizational-level decisions in the modern world. .
So, for the most part, data is not 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 delve into the different types of data available to them.
One of the first lessons in a Data Science Basics 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 underlie them.
In general, quantitative data is what most people imagine when discussing data. As the name suggests, quantitative data can be quantified, that is, it can be measured and expressed as numerical values. Thus, it is easy to manipulate quantitative data and represent it using graphs and statistical tables.
Quantitative data usually answers questions such as “How much?” “, ” How much ? “And” How often? Some examples of quantitative data include a person’s height, the time they spend on social media, and their annual income. There are two main 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 a third party from a customer; these numbers must remain whole, in the form of integers.
Continuous data is measured on a scale. It is possible to divide continuous data into different levels of fineness. For example, a company can measure the size of its product in inches, fractions of inches, meters, or micrometers, depending on the accuracy desired. Continuous variables can take any value.
Compared to quantitative data, qualitative data is much more difficult to work with. Qualitative data cannot be expressed as numeric values; it exists in the form of words, images, objects and symbols. Sometimes qualitative data is called categorical data because the information needs to be sorted into categories instead of being represented by numbers or statistical graphs.
Qualitative data tends to answer questions with more nuance, such as “Why did this happen?” Some examples of qualitative data that business leaders may encounter include the names of customers, their preferred 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 tables and graphs. For example, a business can use nominal data such as marital status (married or single) to understand its consumer audience. There is no intrinsic order of nominal data; it doesn’t matter whether “married” or “single” appears first, but it is imperative that companies have such labels to inform their qualitative charts and tables.
Ordinal data must follow a predetermined pattern. The economic status of a customer is an example of ordinal data: upper class individuals are ranked above lower class individuals. Although 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 a sequence. Business leaders can use ordinal data to understand the relationships between qualitative data.
Quantitative data is the easiest data for business leaders to understand, but it’s not the only data worth collecting for the benefit of the business. Those looking for more information on what types of data are important to business practices and how to leverage that data now and in the future should take data science training, which can expand on these topics. and providing advice to business leaders. they need.