Is a Lengthy Iterative Data Visualization Design Process Relevant in the Era of Generative AI?

Is a Lengthy Iterative Data Visualization Design Process Relevant in the Era of Generative AI?
Reading Time: 3 minutes

Data visualization and design have been essential skills for anyone who wants to communicate insights from data effectively.

Creating engaging and informative visuals and reports often requires a lot of time and effort, however; and involves multiple iterations of data analysis, design choices, and feedback loops.

Is this lengthy process still relevant in the era of generative AI, where we can now automatically generate visuals and reports from data?

Even in its infancy, generative AI has upended the way we work. AI is now used for diverse purposes such as generating realistic images, synthesizing speech or music, writing articles, and summarizing text, images, or videos. Generative AI can also be applied to data visualization and report design, where it can help you create visuals and reports from your data in a fast and easy way.

One of the main benefits of using AI for data visualization design is that it can save time. Instead of spending hours or days on manually analyzing data, choosing the right visualizations, and designing reports, you can use generative AI to accelerate much of the work. AI can automatically analyze data, select the most relevant and interesting insights, generate appropriate visualizations, and write clear and concise summarizations. Designers can then focus on refining the higher-level aspects of communicating with data.

Another benefit of using AI for data visualization design is that it can improve the quality and diversity of output in minimal time. Generative AI can analyze large amounts of data and has demonstrated passable examples of core data visualization and report design. It can largely generate visuals and reports that are accurate, consistent, and arguably appealing for regular business consumers. Generative AI can also offer you multiple options and variations for your visuals and reports, allowing you to explore different perspectives and styles. This can help you discover new insights and ideas from your data that you might have missed otherwise.

So is a lengthy human-driven iterative design process still necessary? Should organizations still promote and pay for weeks or even months of design development time when they can settle for passable output in minutes or hours?


Generative AI for data visualization and report design also comes with some challenges and limitations. One of the main challenges is to ensure the trustworthiness and reliability of the generated output. Generative AI is not perfect, and it can sometimes produce errors or biases in the analysis, visualization, or reporting of your data. For example, generative AI might misinterpret your data in a way that a domain expert would not, it could generate misleading or inappropriate visualizations, or it could write inaccurate or vague summarizations that are not valuable ways to communicate the data. As a result, it is important to always verify and validate the output of AI while using them for your work.

Another challenge of AI is to maintain the human element and creativity in your data visualization design. AI can help you automate some of the tedious and repetitive tasks in your workflow, but it cannot replace your human judgment and intuition. It doesn’t understand the nuances of Bill from Marketing or Barbara from Finance asking for iterative tweaks and refinement. Generative AI creates, but it doesn’t immediately capture purpose, tone, or emotion in a design the way a human designer does. You have to guide it.

Therefore, it is important to leverage the potential to use generative AI as a tool to support data visualization design and communication, but not act as a complete substitute for it. Maybe you will no longer be spending weeks or months on a single effort, but you also won’t have complete value ready in seconds.

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