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What Is Data Visualization, What Are Its Steps, and Why Is It Important?

Data visualization is an analytical method that makes complex datasets easier to understand through charts, tables, maps, and other visuals. In this article, you can explore in detail what data visualization is, its fundamental principles, its implementation stages, and why it is important in the business world. You can also learn about widely used data visualization tools such as Google Looker Studio, Power BI, Tableau, and Python, what they are used for, and how the right visualization techniques contribute to decision-making processes. Start reading now!

What Is Data Visualization, What Are Its Steps, and Why Is It Important?
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1.

What Is Data Visualization?

Data visualization is the presentation of raw data or complex numbers in the form of lists, charts, or tables to make them much easier to understand. This makes it possible to identify trends, errors, or opportunities that may be difficult to notice in large Data sets much more quickly. Line charts, pie charts, bar charts, and heat maps, for example, can all be considered excellent examples of data visualization. While line charts allow you to see changes over time, pie charts enable you to see the relationship between parts and the whole. Bar charts, on the other hand, are highly suitable for comparing different metrics.

2.

What Are the Principles of Data Visualization?

To carry out the data visualization process accurately and effectively, it is necessary to follow certain principles. Below, you can find the principles of data visualization together with their details and explanations.

  • Integrity: It is extremely important to present the data as it is during visualization. The proportions shown in the visual should exactly match those in the dataset. Otherwise, the reader may be misled.
  • Clarity and Simplicity: You should not fill the visual with unnecessary lines, colors, and shapes. Since the human brain is accustomed to consuming information quickly, it wants to understand what it is seeing within the first few seconds of looking at a chart. Therefore, designing a visual that highlights only the information you want to communicate can be critical.
  • Chart Type Selection: Each chart is designed to best serve a different purpose. Therefore, you need to select the chart that best communicates the message you want to convey. For example, a bar chart is an excellent option for displaying the sales volumes of products A, B, and C separately, while a line chart can work perfectly for showing monthly revenue growth. You can display your company's market share with a pie chart and use a scatter plot to show the relationship between your advertising budget and the number of customers acquired.
  • Strategic Use of Colors: Colors in data visualization are used not only for decoration or design purposes but also to communicate information. You should use shades of the same color to display similar categories and shades of different colors for different categories. For example, it may make sense to show positive situations in green and its shades, and negative situations in red and its shades.
  • Text and Labeling: Do not neglect to label every heading and unit of measurement in the visual. People reading the chart should be able to clearly understand what each number represents.
  • Understanding the Target Audience: You should design the data visualization according to the audience for whom it is intended. For example, while you may prepare more detailed tables for experts, simple charts that present only the main result may be sufficient for the general public.
3.

Data Visualization Steps

You can find below the details of the data visualization steps, which generally involve defining the objective, collecting data based on that objective, cleaning the data, selecting the appropriate visual, and creating it.

  • Defining the Objective: The first step in the data visualization process is determining which question needs to be answered. The type of visual to be created, the data to be used, and the analysis method are directly shaped by this objective. For example, if an e-commerce company wants to review its monthly sales performance, the objective may be to analyze the best-selling products or changes in sales over time.
  • Data Collection: Once the objective has been defined, the required data is collected from different sources. These sources may include databases, CRM and ERP (Enterprise Resource Planning) systems, web analytics tools, IoT devices, surveys, or APIs. For example, a company seeking to analyze customer behavior may bring website traffic, sales records, and customer feedback together in the same data pool.
  • Data Cleaning: The collected data may not always be ready for analysis. Missing records, duplicate information, inconsistent formats, or incorrect values are corrected or removed at this stage. This ensures that the visuals to be created produce accurate results. For example, duplicate order records belonging to the same customer can be removed to improve the reliability of the analysis.
  • Chart Selection: A visualization method suitable for the type of data and the message to be communicated is selected. Bar charts may be preferred for comparisons, line charts for changes over time, pie charts for proportions, and scatter plots for displaying relationships. Selecting the right chart makes the data much easier to interpret.
  • Creating the Visual: In the final stage, the cleaned data is transferred to the selected chart type, and colors, axes, labels, and descriptions are arranged to create a clear visual. When necessary, dashboards can be created to bring different charts together on a single screen. This allows decision-makers to analyze data more quickly and take the appropriate actions.

Meanwhile, you can also read our article titled What Is the Internet of Things and How Does It Work? to find answers to your questions about IoT.

4.

What Are Data Visualization Tools?

You can find below a list of the data visualization tools most commonly used in the business world.

  • Google Looker Studio: Among the tools widely used in the data visualization process, Looker Studio, formerly known as Data Studio, is an online tool that can visualize data from Google services free of charge. Therefore, Looker Studio is frequently used as a visualization tool by those who use services such as Google Search Console and Google Analytics.
  • Microsoft Power BI: Developed by Microsoft, Power BI is a business intelligence tool that enables different data sources to be combined on a single platform to create interactive reports and dashboards. It is particularly widely preferred by large-scale companies for analyzing finance, sales, and operations data.
  • Tableau: Tableau is a professional data visualization platform that enables large datasets to be analyzed quickly and compelling visuals to be created using a drag-and-drop approach. Thanks to its real-time analysis capabilities and user-friendly interface, it is frequently used in corporate reporting.
  • Infogram: Infogram is an online platform that enables users to create charts, tables, infographics, and interactive reports without requiring technical knowledge. It is particularly preferred by marketing teams, educational institutions, and content creators.
  • Datawrapper: Datawrapper is a cloud-based data visualization tool that enables professional charts, tables, and maps to be created without the need to write code. It is particularly widely used by journalists, researchers, and news organizations for producing data-driven content.
  • Canva: Although Canva's primary purpose is graphic design, it can also be used for data visualization thanks to its chart, table, and infographic creation features. With its ready-made templates, the tool makes it easier to create visual reports without requiring technical knowledge.
  • Python (Matplotlib / Seaborn / Plotly): Matplotlib, Seaborn, and Plotly libraries within the Python ecosystem are among the most widely used visualization tools in data analysis and scientific studies. These libraries can be used to develop a wide variety of visuals, from simple charts to interactive dashboards.
  • JavaScript (D3.js): D3.js is a powerful JavaScript library used to develop web-based and fully customizable data visualizations. It is particularly preferred by developers who want to create dynamic charts, animations, and interactive data dashboards.

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