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What is Data Visualization?

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Data visualization is the process of using visual elements like charts, graphs, or maps to represent data. It translates complex, high-volume, or numerical data into a visual representation that is easier to process. Data visualization tools improve and automate the visual communication process for accuracy and detail. You can use the visual representations to extract actionable insights from raw data.","id":"ams#c1","heading":"What is data visualization?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

Modern businesses typically process large volumes of data from various data sources, such as the following:\n

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  • Internal and external websites\n
  • Smart devices\n
  • Internal data collection systems\n
  • Social media\n\n

    But raw data can be hard to comprehend and use. Hence, data scientists prepare and present data in the right context. They give it a visual form so that decision-makers can identify the relationships between data and detect hidden patterns or trends. Data visualization creates stories that advance business intelligence and support data-driven decision-making and strategic planning.","id":"ams#c2","heading":"Why is data visualization important?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

    Some benefits of data visualization are as follows:\n

    Strategic decision-making\n

    Key stakeholders and top management use data visualization to interpret data meaningfully. They save time through faster data analysis and the ability to visualize the bigger picture. For example, they can identify patterns, discover trends, and gain insights to remain ahead of the competition.\n

    Improved customer service\n

    Data visualization highlights customer needs and wants through graphical representation. You can identify gaps in your customer service, strategically improve products or services, and reduce operational inefficiencies.\n

    Increased employee engagement\n

    Data visualization techniques are useful for communicating data analysis results to a large team. The entire group can visualize data together to develop common goals and plans. They can use visual analytics to measure goals and progress and improve team motivation. For example, a sales team works together to increase the height of their sales bar chart in one quarter.","id":"ams#c3","heading":"What are the benefits of data visualization?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

    Data scientists combine three main components for visualizing data.\n

    Story\n

    The story represents the purpose behind data visualizations. The data scientist communicates with several stakeholders regarding what they want to achieve by analyzing data. For example, they may want to measure key performance indicators or predict sales volumes. Data scientists and business users collaborate to identify the story they want the data to tell them.\n

    Data\n

    Data analysts then identify the appropriate datasets that will help them narrate the data story. They modify existing data formats, clean the data, remove outliers, and perform further analysis. After data preparation, they plan the different methods of visual exploration.\n

    Visuals\n

    Data scientists then select the visualization methods best suited to share new insights. They create charts and graphs highlighting key data points and simplifying complex datasets. They think of efficient ways to systematically present data for business intelligence.","id":"ams#c4","heading":"What are the components of data visualization?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

    There are five steps for effective data visualization.\n

    Define the goal\n

    You can define a data visualization goal by identifying questions that your existing dataset can potentially answer. A clear goal helps determine the type of:\n

      \n
    • Data you use\n
    • Analysis you do\n
    • Visuals you use to communicate your findings effectively\n\n

       For example, a retailer may seek to understand which type of product packaging gets the most sales.\n

      Collect the data\n

      Data collection involves identifying internal and external data sources. There are massive datasets available online for purchase and use. Your company may also have existing data archives available for analytics. For example, you could collect historical sales volume, marketing campaigns, and product packaging data to find the best packaging.\n

      Clean the data\n

      Data cleaning involves removing redundant data, performing mathematical operations for further analysis, or filtering and converting data to meet the question criteria. For instance, you may remove sales volume data from the holiday months and after marketing campaigns to identify average sales by packaging type.\n

      Select the data visuals\n

      You can choose from several different chart types for efficient visual discovery. Relationships between the data points and the insights you want to communicate will determine the best graphical representations. For example, you may use a bar graph to represent packaging sales by color in the last month. However, a pie chart may be better suited to show the percentage of colored packaging in your inventory. There are two main types of data visualizations.\n

      Static visualization\n

      A static visualization provides only a single view of a specific data story. An infographic is an example of a static visualization.\n

      Interactive visualization\n

      Interactive visualization allows users to interact with graphs and charts. Viewers can change variables in visualization parameters to find new insights or access in-depth information. Data visualization software typically includes a dashboard for user interaction with the system.\n

      Create the data visuals\n

      You can create the data visuals you need by using data visualization tools. Most tools import your final dataset and automatically generate the required reports. Some design principles for effective data visualization include the following:\n

        \n
      • Draw audience attention to important details using sizes, colors, fonts, and graphics\n
      • Provide context to data using visual cues\n
      • Choose the right color combinations\n
      • Use explanatory titles to provide key insights to the audience and help them focus on the right questions\n
      • Add clear labels and numbers  \n","id":"ams#c5","heading":"What are the steps in the data visualization process?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

        While charts and graphs are the most common, you can use several different data visualization methods. Five main types of data visualization methods are provided below.\n

        Temporal data visualization\n

        Temporal data visualizations are used to represent linear one-dimensional objects like a line graph, line chart, or a timeline. For example, you can use line charts to show changes that occur continuously over a given period. Several lines in the line chart demonstrate variations of different factors over the same period.\n

        Hierarchical data visualization\n

        Hierarchical data visualization refers to a group or a set of items that have common links to a parent item. You can use these data trees to display clusters of information. For example, you can show inventory data quantities as a tree with a parent node (clothes) and child nodes (shirts, trousers, and socks).\n

        Network data visualization\n

        Network data visualization is useful for representing the complex relationship between different types of co-related data. For example:\n