Information Definition

Introducing information

Information is the result of the process of extracting meaning from data. It is the second level of the DIKW pyramid and represents the most complex process within the DIKW framework. Extracting information involves identifying patterns and relationships within data, connecting the different pieces, and contextualizing them to make the data relevant and useful.

Types of information

There are three types of information:

  • Information: This refers to factual and accurate data. It is accurate, reliable, and based on evidence.

  • Misinformation: This is well-intentioned but inaccurate information. It is inaccurate, incomplete, or out-of-context information that can spread without the intention to deceive. Misinformation can cause confusion, misunderstanding, or harm, especially if it leads people to make wrong decisions or misbehave.

  • Disinformation: This is intentionally misleading information. It is deliberately false or misleading information spread to deceive or manipulate people for political, financial, or ideological gain. Disinformation aims to shape public opinion or discredit a person, group, or organization.

When we extract meaning from data, we must always build on the information the data is trying to communicate.

How to extract information from data

To turn data into information, we’ll consider the following steps:

  • Select only relevant data: This involves identifying and focusing on the data that is most important for answering our research question or solving our problem. This may involve filtering out certain data points or variables unnecessary for our analysis.

  • Aggregate less important data: This process simplifies the analysis and makes identifying patterns or trends in the data easier. For example, we can group data by period, geography, or demographic characteristics to highlight key insights.

  • Focus only on what is important: This highlights the key insights and avoids clutter or unnecessary information. We can use annotations or callouts to draw attention to specific data points or trends.

  • Choose the right chart: This involves choosing an appropriate chart for the type of data we are presenting and the message we want to communicate.

  • Calibrate the chart to the audience and the message to communicate: Here, we consider the audience when designing the chart. For example, we need to avoid complex charts or technical jargon if our audience is unfamiliar with statistical concepts. We also need to consider the message that needs to be communicated and choose a chart that best supports that message.

  • Remove unnecessary noise from the chart: This includes gridlines, borders, or other visual elements that do not add value to our analysis. A clean and simple chart emphasizes the key insights and makes our data easier to understand.

We can divide these steps into two categories: the first three steps from a data perspective and the last three steps from a visual perspective.

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