What is information?

Information is stimuli that has meaning in some context for its receiver. When information is entered into and stored in a computer, it is generally referred to as data. After processing -- such as formatting and printing -- output data can again be perceived as information. When information is compiled or used to better understand something or to do something, it becomes knowledge.

The data-information-knowledge-wisdom model illustrates this hierarchy. Structured as a pyramid, the model was created to show that data can be captured in different formats, analyzed and converted into different forms. Each level of the pyramid represents a different perspective or level of abstraction as follows:

  • The discrete, raw facts about a given situation with no analysis or interpretation applied.
  • Applying description and meaning to data to make it useful.
  • Information that has insight, context and a frame of reference applied so it can be interpreted.
  • Knowledge is converted into wisdom by applying judgment and action to the information.
Real-world example of the data-information-knowledge-wisdom pyramid
See how a real-world example of the data-information-knowledge-wisdom pyramid works.

What is data?

Data refers to the raw information. In the context of information technology (IT) and computing, it is information that a software application collects and records. Data is typically stored in a database and includes the fields, records and other information that make up the database. It can be accessed and manipulated digitally, and it is quick and easy to transfer among computers.

Data is collected from a variety of sources, such as computers, sensors and devices. It is typically used in business, science and engineering. Data is often presented in the form of numbers, but it can also come as text, visuals, graphics and sounds. Data can also be analyzed and used to create information that could not be obtained by just looking at the original data.

The most common types of data in data science are the following:

  • Quantitative data is numerical data, or data that can be expressed mathematically. Discreet and continuous data are types of quantitative data.
  • Qualitative data is data that cannot be measured, counted or easily expressed with numbers. It is data that comes from text, audio or images. It can be shared using data visualization tools, such as timelines, infographics and word clouds.
  • Nominal data is the simplest form of data in statistics. It is data that is used to name or label a variable; it isn't used to measure things or put them in any order. Examples of nominal data include ethnicity, gender, eye color.
  • Ordinal data is data that takes on values within a known range and follows a natural order. A common example of ordinal data is income levels where incomes are ranked in specific ranges, such as $0-$50K, $50K-$75K, $75K-$100K, etc. The purpose of ordinal data is to rank items in order of priority or value. The numbers are not used for calculations.
  • Discrete data, also called categorical data, is data that is divided into discrete categories, or groups, that are distinctly different from each other. With discrete data, only a specific number of values are possible, and those values cannot be subdivided. For example, the number of people a company employs is a discrete data point.
  • Continuous data is a term used to describe data that is measurable and observable in real time. It can be measured on a scale or a continuum and subdivided into finer values. Continuous data is often recorded at set intervals and then analyzed using statistical software. The amount of time it takes to complete a task is an example of continuous data.

What is the data processing cycle?

The data processing cycle is the framework that data center managers use to make data accessible and useful to users. It is a portion of the data lifecycle. Data enters the data center where it is processed, and then it is sent to the user who makes use of it in a business application.

The part of the data lifecycle referred to as the data processing cycle is divided into the following three stages:

  1. This is the stage where data is collected from multiple sources -- point-of-sale locations, call centers and sensors, for example.
  2. The data is sorted, organized, cleansed and entered into a database or system. It is then transformed into a format that users can understand and make use of.
  3. The newly processed and transformed data is sent to users or stored in a way that they will have access to it when needed.
6 steps of the data lifecycle
Get to know the six steps of the data lifecycle.

Converting data to information

Data and information are not the same. Data refers to numerical and qualitative observations. Information is created when data is presented in a way that has meaning to the recipient. To turn data into information, it must be processed and organized. Presenting data in a way that has meaning and value is called information design, and it is an important field in both Information architecture and human-computer interaction.

Five characteristics of data quality and high-quality information in a database include the following:

  • Information must come from a reliable source of information.
  • Information cannot be partial or have details missing.
  • Mechanisms must be in place to ensure that new data doesn't contradict existing data.
  • Information must be distinctive and add value to a database.
  • Information in a database must be timely and up to date.

Converting information to knowledge and wisdom

Knowledge is information that has been processed, analyzed and interpreted, and can be used to make decisions. The concept of knowledge involves not just the information, but the ability to access it, as well. For example, most applications, including models and simulations, include a form of stored knowledge.

Wisdom is the synthesis of information, knowledge and experience in a way that applies knowledge to real-life situations. The concept of wisdom enables the understanding of patterns and their driving factors. It ultimately enables the prediction of future events.

Artificial intelligence (AI) has enabled computers to learn, problem-solve and perform tasks that usually require human intelligence. These technologies enable computers to take actions based on what the data provided indicates is the best course of action. AI is used in expert systems to diagnose disease, buy and sell stock and play chess better than a human. However, IT has not yet attained a level of human wisdom.

Learn how AI technology is evolving to combine symbolic reasoning and deep learning to capitalize on the power of  neural networks.

This was last updated in May 2021

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