Data visualization is the art and science of presenting data in a visual form that reveals patterns, trends, and insights. But how can you choose the best visualization techniques for your data and your audience? In this article, you will learn some basic principles and tips to help you make effective and ethical data visualizations.
The first step to choosing the best visualization techniques is to understand your data. What kind of data do you have? How many variables, dimensions, and categories are involved? How are they related to each other? What are the main messages or questions you want to convey or answer with your data? Depending on the type and complexity of your data, you may need different visualization techniques to show it clearly and accurately. For example, if you have numerical data, you can use charts, graphs, or maps. If you have categorical data, you can use tables, diagrams, or icons. If you have hierarchical data, you can use trees, networks, or sunbursts.
The art of data-driven storytelling involves choosing the right visualization techniques for a compelling narrative. Start by understanding your data and defining your objectives. Consider data attributes like variables and distribution to pick the best visualization type. Bar charts, line charts, scatter plots, and more are at your disposal. Adding interaction can engage your audience. Maintain data integrity and simplicity, test, and iterate. Ensure accessibility and provide clear documentation. Choose the right tools and evaluate effectiveness. Your visualization is the embodiment of your data's essence—a narrative on the canvas of understanding in the data-driven world.
The statistical and morphological characteristics of data-driven systems can often be crucial for developing a crea undrrstanding of their applicability and subsequent research into optimization of such systems in real-world scenarios. Thus visualization is important both in context of the underlying nature of data (statistical characteristics explored through scatter plots, histograms, heatmaps, violin plots etc.) as well as the examination of that data and the design of inferential data-driven systems which rely extensively on the data to generate insights, recommendations or estimates (tree-based visualizations or SHAP plots for model evaluation). Thus it is vitally important to evaluate/apply visualizations for appropriate scenarios.
The second step to choosing the best visualization techniques is to know your audience. Who are they? What is their background, level of expertise, and interest in your data? How will they access and interact with your visualization? What are their expectations and needs? Depending on the characteristics and preferences of your audience, you may need to adjust your visualization techniques to suit them. For example, if your audience is familiar with your data and your domain, you can use more advanced or specialized visualization techniques. If your audience is diverse or general, you may need to use more simple or universal visualization techniques. If your audience is viewing your visualization on a mobile device, you may need to use more responsive or adaptive visualization techniques.
Tailoring visualization to the audience is paramount in my approach. When faced with an audience less acquainted with data intricacies, simplicity reigns, while upholding professionalism. User experience takes center stage – seamlessness is key. Enhancing automation over manual input streamlines usage, ensuring accessibility. Adapting to diverse needs, I strive for a balance between sophistication and user-friendliness. Through these strategies, I ensure data insights are effectively conveyed, catering to varying levels of expertise and facilitating effortless engagement.
The third step to choosing the best visualization techniques is to know your purpose. What is the goal or objective of your visualization? What do you want to achieve or communicate with your data? How do you want to influence or persuade your audience? Depending on the purpose and intention of your visualization, you may need to choose different visualization techniques to support it. For example, if your purpose is to explore or analyze your data, you may need to use more interactive or dynamic visualization techniques. If your purpose is to explain or inform your audience, you may need to use more descriptive or narrative visualization techniques. If your purpose is to persuade or influence your audience, you may need to use more emotive or rhetorical visualization techniques.
The fourth step to choosing the best visualization techniques is to know your ethics. What are the ethical implications and responsibilities of your visualization? How will your visualization affect or impact your data, your audience, and your society? How can you ensure that your visualization is fair, honest, and respectful? Depending on the ethics and values of your visualization, you may need to follow some principles and guidelines to avoid misleading, manipulating, or harming anyone with your data. For example, you should respect the integrity and quality of your data, and avoid distorting, omitting, or fabricating it. You should respect the diversity and dignity of your audience, and avoid stereotyping, discriminating, or offending them. You should respect the context and consequences of your visualization, and avoid misrepresenting, oversimplifying, or exaggerating it.
The fifth step to choosing the best visualization techniques is to know your tools. What are the tools and technologies that you can use to create and share your visualization? How do they work and what are their advantages and limitations? How can you use them effectively and efficiently? Depending on the tools and resources that you have access to, you may need to learn some skills and techniques to use them properly. For example, you may need to use some software or applications that can help you process, analyze, and visualize your data. You may need to use some languages or codes that can help you customize, enhance, and animate your visualization. You may need to use some platforms or formats that can help you publish, distribute, and embed your visualization.
The sixth step to choosing the best visualization techniques is to know your options. What are the different visualization techniques that are available and suitable for your data, your audience, your purpose, your ethics, and your tools? How can you compare and evaluate them? How can you combine and adapt them? Depending on the options and alternatives that you have, you may need to experiment and test them to find the best fit. For example, you can use some online resources or libraries that can provide you with examples, templates, and tutorials of different visualization techniques. You can use some criteria or frameworks that can help you assess the effectiveness, efficiency, and aesthetics of different visualization techniques. You can use some feedback or reviews that can help you improve and refine your visualization techniques.
As cleared in the article there is no right and wrong in data analysis and all depend on the case, purpose and data used, I would recommend to use data scientist to be able to get the best analysis out of your data and use the proper graph and dashboard. Data scientist may use several standard methodologies to achieve the target, these methodologies are systematic approaches to solving data science problems using data and analytical techniques.