Data Types: 7 Key Data Types | eWEEK (2023)

You've probably heard the term "data" several times before clicking this guide. Data is one of the most valuable resources for any business, especially for those who work in IT. Why? Its possibilities are endless.

Data is used across industries for many innovative use cases. data helplearn machinesand inspires potential customers to convert. It predicts machine failures,Enable AI chatbots, and even improves health. It is also plentiful. Accordinglyrecent statistics, global data volume is expected to reach 180 zettabytes by 2025.

But what is data? This question can conjure up images of spreadsheets with infinite rows and columns of numbers. However, data is much more than that. We need to understand the different types, categories and use cases of data in order to get the most out of it.

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What is data?

Defining dates is easy. AccordinglyMerriam-Websterdata is "factual information (such as measurements or statistics) that serves as a basis for consideration, discussion or calculation" or "information in digital form that can be transmitted or processed".

The term "data" can refer to either raw data, which is the initial, unprocessed, uncleaned version of the data, or processed data, which is data that has been translated into a usable format.

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Data is organized into two main types:qualitativeeCrowd.

Within these two types there are different categories of data ranging from nominal data to interval data.

qualitative data

Qualitative data is data that is not usually expressed in numerical form, but is most commonly expressed in words. It is descriptive in nature and describes qualities that cannot be measured.

Examples of qualitative data can include anything from color to gender. Qualitative data can be collected in a number of ways, e.g. B. through surveys, customer interaction or observation. It is then analyzed through a process known as qualitative data analysis.

Within the qualitative data type there are two main categories:nominaleOrdinal-Data.


Nominal data is a form of qualitative data that is perhaps the simplest data type. Nominal data can be grouped into several different categories, but is not ordered. Examples of nominal dates are the state you live in or even your favorite movie genre.

ordinal data

Ordinal data is a form of qualitative data grouped into categories. However, this data is organized by category. Some common examples of ordinal data are the letter rating system or a customer satisfaction index.

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quantitative data

Unlike qualitative data, quantitative data can be expressed in numerical form and easily measured. Examples of quantitative data include financial data, distance, age, and number of products sold. When data can be quantified, it is quantitative.

Within the quantitative data type there are three main categories:discreet,continually, zIntervalData.

discrete data

Discrete data is quantitative data that can be counted using integers (integers). It cannot be expressed as a fraction or decimal. For example, the number of monthly returning customers is an example of discrete data because you can't have half or a third of a customer.

Continuous data

Continuous data is quantitative data that can be expressed using decimals and fractions because accuracy is critical. It's not necessarily countable, but measurable. While discrete data stays the same over a period of time, continuous data over the same period can change. Examples of continuous data are characteristics such as altitude.

The interval date

Interval data is quantitative data measured along a scale using intervals or points that are equidistant from each other. A common type of interval data is the time where the spacing between points (e.g., 12:00 to 1:00 and 1:00 to 2:00) is equal.

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Where does the data come from?

Data is collected from many sources. Some common data sources are:

Social Media Activity:Public data is one of the most common data sources. And a huge amount of public data comes from what we take in on social media accounts like Facebook, Twitter, and Instagram. Data may also be obtained from the websites we visit frequently, such as Google.

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financial transactions:Companies often use customer purchase data to inform future product development and marketing initiatives. Data collected from these transactions includes payment methods, products purchased, consumer demographics, and more.

Sensors for the Internet of Things (IoT):ÖInternet of Thingsrefers to the network of connected devices such as smartphones, wearables and IoT sensors. These sensors are widely used in industries such as manufacturing to collect data about machine operation and production.

Customer Interactions:Businesses typically collect data from customer interactions, be it via chatbot, email, or phone call. The data can include anything from customer names to what was discussed during the interaction. This data can be used to improve customer service, training and a variety of other initiatives. Customer data can also be collected directly through surveys and questionnaires.

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Once data is collected, it can be analyzed and used in a variety of ways. This applies regardless of the industry. In fact, data analytics is making waves in every market, from healthcare to manufacturing.

machine learning

machine learningrefers to the ability of a machine to “learn” based on historical data. By inputting data, algorithms can be trained to find patterns in future data to detect anomalies and make decisions.

Machine learning has endless use cases across all industries. For example, financial institutions can better protect consumers from fraud by using machine learning models that automatically detect fraudulent account activity.

Artificial intelligence

Artificial intelligence is now popular and used in many ways. However, AI is not possible without huge amounts of data. For example,ChatGPT, an innovative AI language model, requires huge amounts of language data to function. This data comes from sources such as internet articles and other texts.

Other examples of AI include virtual assistants, robotics, facial recognition, and even the automatic correction we use every day. All of these examples require data in its various forms in order to work.

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Business Intelligence

Business Intelligence refers to the process of using data to make informed business decisions. For example, financial data can be used by companies to inform annual budgets, while customer data can help develop more effective marketing campaigns.

Customer transactions and CRM software are two main sources of business intelligence data. it's donedata visualization platformsSpecially designed for business intelligence, insights are easily visualized in real time through simple graphs and reports.

predictive analytics

Predictive analytics is the process of using data to predict outcomes and events. In manufacturing, for example, this process can be used to predict when machines will suffer unplanned downtime due to a malfunction before it occurs. This allows manufacturers to carry out preventive maintenance or repairs before production is stopped.

Predictive analytics can also be used in businesses to predict consumer behavior or in healthcare to predict patient outcomes, which can lead to better care.

How to use the full potential of data

The data can appear complex. However, in today's data-driven world, data analysis is easier than ever. Data science platforms can nowextract, load and transformData and present actionable insights without your input. The future is bright for data in all its forms and for the organizations that use it.

Would you like to delve even deeper into big data and analytics? Check out our guide:What is data analysis?


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