{"id":1082868,"date":"2024-10-01T09:00:00","date_gmt":"2024-10-01T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations\/"},"modified":"2024-11-05T06:42:56","modified_gmt":"2024-11-05T14:42:56","slug":"data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations\/","title":{"rendered":"Data Formulator: Exploring how AI can help analysts create rich data visualizations\u00a0"},"content":{"rendered":"\n
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Transforming raw data into meaningful visuals, such as charts, is key to uncovering hidden trends and valuable insights, but even with advances in AI-powered tools, this process remains complex. Integrating AI into the iterative nature of the data visualization process is particularly challenging, as data analysts often struggle to describe complicated tasks in a single text prompt while lacking the direct control of traditional tools. This highlights the need for smarter, more intuitive solutions that combine AI\u2019s precision with the flexibility of hands-on methods.<\/p>\n\n\n\n

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\n\t\t\t\t\t\tPublication<\/span>\n\t\t\tData Formulator 2: Iteratively Creating Rich Visualizations with AI<\/span> <\/span><\/a>\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n\n\n\n

To address this, we\u2019re excited to release Data Formulator<\/a> as an open-source (opens in new tab)<\/span><\/a> research project. This update builds on last year\u2019s release<\/a> by combining user interface (UI) interactions for designing charts with natural language input for refining details. Unlike the previous version, which required users to choose between two methods, this unified approach allows them to iteratively solve complex tasks and with less effort.<\/p>\n\n\n\n

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\n\t\t\t\t\t\tTool<\/span>\n\t\t\tData Formulator<\/span> <\/span><\/a>\t\t\t\t\t\t\t

Transform data and create rich visualizations iteratively with AI.<\/p>\n\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n\n\n\n

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Figure 1. Data Formulator\u2019s UI<\/figcaption><\/figure>\n\n\n\n

Creating and refining charts with the Concept Encoding Shelf and data threads<\/h2>\n\n\n\n

With Data Formulator, data analysts can now create charts from scratch or select from existing designs through\u202fdata threads<\/em>. The UI features a pane called the \u201cConcept Encoding Shelf,\u201d where users can build their chart by dragging various data fields into it and defining them\u00a0or by creating\u00a0new ones. A large language model (LLM) on the backend processes this input, generating the necessary code to produce the visual and updating the data threads for\u00a0future use. This process is illustrated in Figure 2.<\/p>\n\n\n\n

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Figure 2. To create a new chart, users can select a previously created chart from the data threads and then use a combination of UI elements and language to describe their intent.<\/figcaption><\/figure>\n\n\n\n

Data threads enable users to review and modify charts they created previously. This iterative process streamlines the editing and refinement process, as the LLM adapts past code to new contexts. Without this feature, users would need to provide more detailed prompts to recreate designs from scratch. This iterative mechanism also allows users to continue updating their charts until they\u2019re satisfied.<\/p>\n\n\n\n

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Figure 3: Data Formulator\u2019s data threads support complex navigation, quick editing, and the rerunning of previous instructions. <\/figcaption><\/figure>\n\n\n\n
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