Data is the new crude. Data are present in every industry nowadays. You must play with or experiment with raw or structured data regardless of your career, whether you work as a data scientist, marketer, businessperson, data analyst, researcher, or in any other capacity. Because this information is so crucial to us, it is crucial that it is handled and stored correctly and error-free. To process these data and provide the desired outcomes, it is crucial to understand the different forms of data. Data can be categorised into two categories: qualitative and quantitative, which are further divided into four categories: nominal, ordinal, discrete, and continuous.

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Nowadays, businesses rely heavily on data, and the majority of them utilise data-driven insights to establish strategies, introduce new products and services, and test out new ideas. Today, at least 2.5 quintillion bytes of data are generated each day, according to a report.

Types of Data structure

Qualitative or Categorical Data

Data that cannot be quantified or tallied in numerical form is referred to as qualitative or categorical data. These kinds of data are organised by category rather than by quantity. It also goes by the name Categorical Data for this reason. These data can be text, symbols, audio, or images. A person's gender, whether it be male, female, or another, is qualitative information.

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Qualitative data describes how people see things. Market researchers can use this information to better understand the preferences of their target market and then adapt their ideas and approaches.

Quantitative Data

Although numerical values can be used to describe quantitative data, it can be counted and used for statistical data analysis. These data also go by the name of numerical data. It provides a response to inquiries like "how much," "how many," and "how frequently." Quantitative data includes things like the cost of a phone, the RAM of a computer, a person's height or weight, etc.

Quantitative data can be manipulated statistically and are visualised using a wide range of graphs and charts, including bar graphs, histograms, scatter plots, boxplots, pie charts, and line graphs, among others.

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Working with data is important because we need to understand what kind of data it is and how to use it to get useful results. Knowing which type of graphic works best for a certain category of data is also crucial because it facilitates data analysis and visualisation. Data science skills and a thorough knowledge of the various forms of data and how to interact with them are essential for working with data.

Research, analysis, statistics, and data science use a variety of data kinds. This information aids a business in business analysis, strategy development, and the development of an effective data-driven decision-making process.

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