Data is everywhere. Currently, the world produces an average of 205 exabytes of data every day. Analyzing these large data sets with traditional data processing techniques has become challenging for organizations. Data visualization plays an essential role in processing big data and giving powerful insights to businesses.
Data visualization combines arts and science to represent data in visual formats and convey the story. This article explains about data visualization and storytelling. It also explains how to choose the right data visualization tools and techniques for storytelling.
Table of Contents
1. Data Visualization
The visualization enables the users to understand and interpret knowledge from the large information set. The visualization technique represents the information in a graphical form so that it can give insights.
The visualization technique analyzes, illustrates, explores, discovers, and communicates information. Multiple methods are available to represent data in a visual format, such as data visualization, information visualization, compound visualization, concept visualization, strategy visualization, and metaphor visualization.
2. Data Visualization Tools
Spreadsheets, data visualization software, and programming libraries are the three categories of data visualization tools. The tools are categorised based on basic to advanced features so that users can choose the right data visualization tool as per need.
- Spreadsheets are the most common tool for data visualization as they display data in graphs, charts, and plots.
- Data visualization software like Tableau, Visme, Datawrappers, chart blocks, infographics, Power BI, Oracle Visual Analyzer, SAP Lumira, Google Charts, MicroStrategy, and others provides advanced visualization capabilities for business organizations.
- Programming Libraries like Matplotlip, D3.js, ggplot2, digraph, and chart js are high-level customizable data visualization tools that allow users or firms to customize features according to need. However, it requires a high level of expertise to get accurate results.
3. Data Visualization Techniques
Data visualization helps organizations to make data-driven decisions by gaining insights into big data. It represents the data information in a graphical format to grasp information quickly. Business organizations use automated data visualization tools and techniques to make effective and quick decisions. Data visualization software automates the visualization process for data engineers and makes conversation engaging. The visualization techniques use both structured and unstructured data.
Some data visualization techniques are box plots, histograms, heat maps, charts, treemaps, and network diagrams. These techniques are used for different purposes. They are again divided further into line charts, bar charts, area charts, pie charts, scatter plots, mekko charts, bubble charts, word clouds, and others.
4. Data Visualization And Storytelling
Data visualization is used in storytelling to communicate complex data using graphics. Storytelling is a technique that relies on data and human communication. Shortcomings in data visualization and storytelling are confusion, missing data, and gaining the attention of an audience, which leads the company to communicate wrong information.
Marketers use the storytelling technique to communicate about the company, its brand, and its products effectively with the targeted consumers. Many data analytics officers have only quantitative knowledge, due to which they fail to use storytelling effectively.
To use storytelling techniques to communicate about the brand and products, the analytical team has to concentrate on the context to communicate with the consumers. The organization should know the factors like who, what, and how before creating a story. In storytelling, data plays an important role.
Storytelling provides many benefits to the organization, such as increasing the attention of the audience, attracting the consumers, engaging the targeted consumers, and communicating the brand message effectively. Storytelling is used in marketing and business meetings to communicate complex data effectively.
Data visualization in storytelling aims to maximize the accuracy of the information and attract consumers to grasp information from the graphics. The shortcomings of data visualization in storytelling is that humans do not understand the 3D graphics quickly.
It creates confusion and distracts the audience from the actual story. People do not understand the pie chart as it is difficult to measure the difference between different segments of the pie chart. To overcome this, use bar charts that are easy to understand.
Drafting a story is not enough. It must be designed using graphics to communicate effectively. To design graphics, the designer has to concentrate on the components like affordance, accessibility, and aesthetics. The designer has to eliminate the elements and data that distract the audience. It is not necessary to include every piece of data in the graphics.
Using multiple colours and different sizes of text will distract and make the graphics less attentive. Storytelling is the best tool to communicate the data in a visual format. It helps to gain the attention of the targeted audience only when the story is crafted as per the rules. The storytelling technique can be used in marketing to attract an audience or to communicate the project ideas to investors and top management.
Storytelling with data visualization is the future trend that becomes more dynamic and interactive with the integration of powerful technologies, like Augmented reality, Artificial intelligence, data democratization, and virtual reality.
5. How To Choose The Right Data Visualization Tools And Techniques For Storytelling
There are various data visualization tools and techniques available that would help organizations and ordinary users to represent data visually. However, choosing the right visualization techniques is essential for reducing misconceptions and errors in the final report. So, before choosing the data visualization technique or tool, the organization has to create a plan. The following framework helps to ease the data visualization process.
5.1. Create Checklist
Create a checklist of data visualization that helps in understanding the purpose of visualization. The checklist will help to choose the right data sets, visual techniques, and tools to process the data. The checklist aims to explain the process, compare and contrast the data sets, and show the change and relationship between the data sets.
5.2. Choose Visualization Technique
Know which visualization technique is suitable as per the data sets.
- Processes: flow charts, timelines, infographics, illustrations, and Gantt charts explain the processes or methods.
- Comparisons: Similarly, Bar charts, bubble charts, Line charts, stacked areas, box and whisker plots, histograms, pyramid charts, and pictogram charts compare or contrast the data sets.
- Relationships: Network diagrams, Venn diagrams, heatmaps, radar charts, mosaic charts, and chord diagrams explain the relationship among the data sets.
5.3. Select Audience:
The other tool is to choose the audience for representing the data visually. Different audiences understand different visual formats.
- Public Audiences: The public audience needs information in a simple format. So, data visualization techniques like infographics, videos, illustrations, maps, graphs, and bar charts will be helpful.
- Business Audiences: A business audience that uses visualization to make business decisions needs eye-catching visual reports. Bar graphs, pie charts, line graphs, and tables with advanced features will be helpful.
- Scientific Audiences: For scientific audiences like academic communities, laboratory members, and grant reviewers who need a clear design to communicate results, one can use bar graphs, line graphs, heatmaps, box & whisker, and scientific illustrations.
5.4. Storytelling framework
Use a storytelling framework to create a compelling story with characters, settings, conflicts, and resolution elements.
6. Conclusion
Data visualization is a vast concept accepted by every industry. Data visualization may provide inaccurate results but valuable insights. These valuable insights help business organizations make proper and timely decisions. Storytelling is a popular technique to connect consumers with the brand’s emotions and missions. Good storytelling will help increase the company’s brand image and revenue. The article provides the process of choosing the right data visualization tools and techniques to craft a compelling story and reach targeted audiences.