{"id":3818,"date":"2021-01-28T10:57:00","date_gmt":"2021-01-28T09:57:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/what-is-data-visualization\/"},"modified":"2023-08-31T17:53:52","modified_gmt":"2023-08-31T15:53:52","slug":"what-is-data-visualization","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-visualization\/","title":{"rendered":"What Is Data Visualization and Why Is It Important? A Complete Introduction"},"content":{"rendered":"

They say a picture is worth a thousand words, and this is especially true for data analytics.<\/strong><\/p>\n

Data visualization is all about presenting data in a visual format, using charts, graphs, and maps to tell a meaningful story. It\u2019s a crucial step in the data analysis process\u2014and a technique (or art form!) that all areas of business can benefit from.<\/p>\n

In this guide, we\u2019ll tell you everything you need to know about data visualization (also known as data viz). We\u2019ll explain what it is, why it matters, some of the most common types, as well as the tools you can use to create them.<\/p>\n

This guide is ideal for anyone who wants to present, communicate, and share data-driven insights.<\/p>\n

If you’d like to learn more data analytics skills, try this free data short course<\/strong><\/a>.<\/span><\/p>\n

    \n
  1. What is data visualization?<\/a><\/li>\n
  2. Why is data visualization important?<\/a><\/li>\n
  3. When should you visualize your data?\u00a0<\/a><\/li>\n
  4. Different types of data visualization and when to use them<\/a><\/li>\n
  5. Top data visualization tools<\/a><\/li>\n
  6. Best practices and principles for effective data visualization<\/a><\/li>\n
  7. Getting started with data visualization<\/a><\/li>\n<\/ol>\n

    So: What is data visualization?<\/strong> Let\u2019s start with a definition.<\/p>\n

    1. What is data visualization? A definition<\/h2>\n

    Data visualization is the graphical or visual representation of data. It helps to highlight the most useful insights from a dataset, making it easier to spot trends, patterns, outliers, and correlations.<\/p>\n

    Imagine you\u2019re presented with a spreadsheet containing rows and rows of data. You probably won\u2019t be able to decipher the data without delving into it, and it\u2019s unlikely that you\u2019ll be able to spot trends and patterns at first glance.<\/p>\n

    Now imagine seeing the same data presented as a bar chart, or on a color-coded map. It\u2019s much easier to see what the data is telling you, right?<\/p>\n

    \"A
    Some data visualizations taken from the Fitbit app.<\/figcaption><\/figure>\n

    That\u2019s the whole point of data visualization. It makes insights visible to the naked eye, so that virtually anyone can see and understand what\u2019s going on. When done well, data visualization tells a story.<\/p>\n

    This storytelling aspect is crucial as it makes your data actionable. There\u2019s a huge difference between simply having lots of data versus actually understanding how to use it to drive actions and decisions\u2014and data visualization bridges that gap.<\/p>\n

    There are two broad categories of data visualization: exploration and explanation. Let\u2019s take a look at those now.<\/p>\n

    What are the two main types of data visualization? Exploration vs. explanation<\/h3>\n

    We\u2019ll look at specific types of data visualization later on, but for now, it\u2019s important to distinguish between exploratory and explanatory data visualization.<\/p>\n

    In a nutshell, exploratory data visualization helps you figure out what\u2019s in your data, while explanatory visualization helps you to communicate what you\u2019ve found. Exploration takes place while you\u2019re still analyzing the data, while explanation comes towards the end of the process when you\u2019re ready to share your findings.<\/p>\n

    Exploration<\/h4>\n

    When faced with a new dataset, one of the first things you\u2019ll do is carry out an exploratory data analysis<\/a>. This is where you investigate the dataset and identify some of its main features, laying the foundation for more thorough analysis.<\/p>\n

    At this stage, visualizations can make it easier to get a sense of what\u2019s in your dataset and to spot any noteworthy trends or anomalies. Ultimately, you\u2019re getting an initial lay of the land and finding clues as to what the data might be trying to tell you.<\/p>\n

    Explanation<\/h4>\n

    Once you\u2019ve conducted your analysis and have figured out what the data is telling you, you\u2019ll want to share these insights with others.<\/p>\n

    These could be key business stakeholders who can take action based on the data, for example, or public audiences who have an interest in your topic area.<\/p>\n

    Explanatory data visualizations help you tell this story, and it\u2019s up to you to determine which visualizations will help you to do so most effectively. We\u2019ll introduce some of the most common types of data visualization (and when to use them) in section four.<\/p>\n

    Want to learn more about data visualization, and try your hand at creating visualizations of your own?\u00a0<\/strong>Give this free introductory tutorial<\/a> a go. We’ll show you, step by step, how to create bar charts, line graphs, and more for a real dataset in Google Sheets.<\/p>\n

    2. Why is data visualization important?<\/h2>\n

    The importance of effective data visualization is rooted in the importance of data analytics in general.<\/p>\n

    We\u2019re living in an increasingly data-rich world; at the start of 2020, the digital universe comprised approximately 44 zettabytes of data<\/a>. For perspective, one zettabyte is roughly equal to a trillion gigabytes. By 2025, it\u2019s estimated that around 463 exabytes of data will be created every 24 hours across the globe. An exabyte is equivalent to one billion gigabytes. Basically, we\u2019re producing tons and tons of data all the time.<\/p>\n

    Data analytics allows us to make sense of (at least some of) that data. From a business perspective, it enables companies to learn from the past and plan ahead for the future. In fields like healthcare, it can help to improve patient care and treatment. In finance and insurance, it can help to assess risk and combat fraudulent activity. Essentially, we need data analytics in order to make smart decisions\u2014and data visualization is a crucial part of that.<\/p>\n

    Data visualization helps us to understand what certain data is telling us, presenting it in a way that\u2019s accessible to a range of audiences\u2014not just data experts. It\u2019s how you bridge the gap between your expertise as a data analyst or data scientist, and those people who can use or act upon the insights you discover.<\/p>\n

    \"A<\/p>\n

    A line graph and a bar chart taken from the Fitbit app.<\/em><\/p>\n

    The advantages and benefits of effective data visualization at a glance<\/h3>\n

    Data visualization allows you to:<\/p>\n