{"id":265527,"date":"2025-05-31T15:25:35","date_gmt":"2025-05-31T15:25:35","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/microsofts-free-ai-tool-for-data-analysis\/"},"modified":"2025-05-31T15:25:35","modified_gmt":"2025-05-31T15:25:35","slug":"microsofts-free-ai-tool-for-data-analysis","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=265527","title":{"rendered":"Microsoft&#8217;s Free AI Tool for Data Analysis"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p><span style=\"font-weight: 400;\">In today\u2019s data-driven world, every researcher and analyst requires the ability to yield prompt information from raw data and present it in visual form. That\u2019s exactly what Microsoft\u2019s new AI tool, Data Formulator, can help you with. It simplifies data visualization by presenting the data as interesting charts and graphs, especially for those without much knowledge of data manipulation and visualization tools. In this article, we\u2019ll dive deep into Microsoft\u2019s Data Formulator tool and learn how to use it.<\/span><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-data-formulator\"><span style=\"font-weight: 400;\">What is Data Formulator?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data Formulator is an open-source application developed by Microsoft Research that uses LLMs as a means to transform data and facilitate faster <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/04\/a-complete-beginners-guide-to-data-visualization\/\" target=\"_blank\" rel=\"noreferrer noopener\">data visualization<\/a>. What differentiates Data Formulator from traditional chat-based AI tools is its hybrid interactions. It has an intuitive user interface that supplements natural language inputs and simple drag-and-drop interactions.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1999\" height=\"998\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3.webp\" alt=\"\" class=\"wp-image-236467\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3.webp 1999w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3-300x150.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3-768x383.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3-1536x767.webp 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image9-3-150x75.webp 150w\" sizes=\"(max-width: 1999px) 100vw, 1999px\"\/><figcaption class=\"wp-element-caption\"><span style=\"font-weight: 400;\">Source: <\/span><a href=\"https:\/\/github.com\/microsoft\/data-formulator?tab=readme-ov-file\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">Microsoft<\/span><\/a><\/figcaption><\/figure>\n<p><span style=\"font-weight: 400;\">At its core, the tool was designed to bridge the huge gap between having a visualization idea and actually creating it. Typical tools either force users to write complicated code or choose from an endless list of menu-driven options to visually represent data. In contrast, Data formulator offers immediate interaction with the user to express visualization intent, while the heavy transformation work is taken care of by AI, behind the scenes.<\/span><\/p>\n<h3 class=\"wp-block-heading\" id=\"h-key-features-of-microsoft-data-formulator\"><span style=\"font-weight: 400;\">Key Features of Microsoft Data Formulator<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Some of the key features of Data Formulator are:<\/span><\/p>\n<ul class=\"wp-block-list\">\n<li><b>Hybrid Interaction Model: <\/b><span style=\"font-weight: 400;\">It offers the best of both worlds: precision through direct manipulation (drag and drop), and flexibility through natural language conversational prompts. This helps users add chart-type visualizations directly and then clarify hard-to-express requirements via text.<\/span><\/li>\n<li><b>AI-Powered Data Transformation: <\/b><span style=\"font-weight: 400;\">When users ask for fields that do not exist in its dataset, the AI will create new calculated fields. It will also aggregate the data or apply filters to meet the visualization specifications.<\/span><\/li>\n<li><b>Multiple Data Source Support: <\/b><span style=\"font-weight: 400;\">Data Formulator supports a wide range of data sources, such as CSV files, databases (MySQL, DuckDB), and cloud services such as Azure Data Explorer. The external data loaders enable easy integration even with expensive enterprise data sources.<\/span><\/li>\n<li><b>Large Dataset Handling: <\/b><span style=\"font-weight: 400;\">Since version 0.2, Data Formulator has been handling large datasets efficiently by uploading data to a local DuckDB. Then it starts fetching just enough data for the visualization, drastically minimizing the waiting time.<\/span><\/li>\n<li><b>Data Threading and Anchoring: <\/b><span style=\"font-weight: 400;\">The tool records all the visualization attempts under \u2018Data Threads\u2019, allowing users to retrace their path during exploration. It can save intermediate datasets as anchoring points to be further pursued as analyses, thereby eliminating unnecessary confusion and improving efficiency.<\/span><\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-architecture-of-data-formulator\"><span style=\"font-weight: 400;\">Architecture of Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The modular architecture of Data Formulator provides flexibility and extensibility through the following layers:<\/span><\/p>\n<ul class=\"wp-block-list\">\n<li><b>Frontend Layer: <\/b><span style=\"font-weight: 400;\">The frontend, built with modern web technologies like TypeScript and React, is what allows users to upload or preview datasets. It lets users add visual encodings via drag and drop, input natural language prompts, and view generated visualizations and code.<\/span><\/li>\n<li><b>Backend Processing Engine: <\/b><span style=\"font-weight: 400;\">This Python-based part of the backend system loads &amp; preprocesses the data and communicates with various LLM providers. Then, accordingly, it generates the code involved in transforming the data and renders visualizations through Altair\/Vega-Lite libraries.<\/span><\/li>\n<li><b>AI-Integration Layer: <\/b><span style=\"font-weight: 400;\">This layer of the framework is involved in LLM prompt engineering, response processing, code validation, and execution. It also handles error handling and debugging assistance, as well as context management for iterative conversations.<\/span><\/li>\n<li><b>Data Management Layer: <\/b><span style=\"font-weight: 400;\">It deals with connecting the tool to multiple data sources and operating on a local database (DuckDB). It allows for caching and eventually optimizing data and implementations of external data loaders.<\/span><\/li>\n<\/ul>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1999\" height=\"748\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1.webp\" alt=\"\" class=\"wp-image-236466\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1.webp 1999w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1-300x112.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1-768x287.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1-1536x575.webp 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image12-1-150x56.webp 150w\" sizes=\"auto, (max-width: 1999px) 100vw, 1999px\"\/><figcaption class=\"wp-element-caption\"><span style=\"font-weight: 400;\">Source: <\/span><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">Microsoft<\/span><\/a><\/figcaption><\/figure>\n<h3 class=\"wp-block-heading\" id=\"h-how-does-data-formulator-work\"><span style=\"font-weight: 400;\">How Does Data Formulator Work?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Formulator blends interactions from users with AI-powered data processing following the process below:<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-1-intent-specification\"><span style=\"font-weight: 400;\">Step 1: Intent Specification<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Users select a chart type and drag data fields to visual properties (x-axis, y-axis, color, size, etc.). If the reference fields do not exist in the original dataset, they are tasked as a flair for requiring data transformation.<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-2-ai-interpretation\"><span style=\"font-weight: 400;\">Step 2: AI Interpretation<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The system observes the user\u2019s specifications of visual encodings along with any free-text natural language prompts. It tries to understand exactly what the user wants to visualize by analysing the data types and the relationship between the fields.<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-3-code-generation\"><span style=\"font-weight: 400;\">Step 3: Code Generation<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Once interpreted, Data Formulator produces the data transformation code needed. In most cases, it uses Python with Pandas or Polars, to build the necessary derived fields, aggregations, and filtering operations.<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-4-execution-and-validation\"><span style=\"font-weight: 400;\">Step 4: Execution and Validation<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The generated code is then executed using the dataset, with built-in error handling to find and fix common errors. If it cannot do so, the AI goes back and iteratively reworks the code.<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-5-visualization-creation\"><span style=\"font-weight: 400;\">Step 5: Visualization Creation<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The system generates a visualization specification once the data has been properly transformed and proceeds to produce a final chart out of it.<\/span><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-step-6-iterative-refinement\"><span style=\"font-weight: 400;\">Step 6: Iterative Refinement<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Users can provide feedback, ask follow-up questions, or change encodings iteratively to refine the visualization over time, thus creating a natural iterative workflow.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1999\" height=\"715\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6.webp\" alt=\"\" class=\"wp-image-236468\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6.webp 1999w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6-300x107.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6-768x275.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6-1536x549.webp 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image2-6-150x54.webp 150w\" sizes=\"auto, (max-width: 1999px) 100vw, 1999px\"\/><figcaption class=\"wp-element-caption\"><span style=\"font-weight: 400;\">Source: <\/span><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">Microsoft<\/span><\/a><\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-getting-started-with-data-formulator\"><span style=\"font-weight: 400;\">Getting Started with Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are three ways to start using Data Formulator.<\/span><\/p>\n<h3 class=\"wp-block-heading\" id=\"h-method-1-through-python-installation\"><span style=\"font-weight: 400;\">Method 1: Through Python Installation<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">One of the easiest ways to get started with Data Formulator is via installation through PIP. For this:<\/span><\/p>\n<ol class=\"wp-block-list\">\n<li><span style=\"font-weight: 400;\">Install the Data Formulator in a virtual environment.<\/span><\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code><span style=\"font-weight: 400;\">pip install data_formulator<\/span><\/code><\/pre>\n<ol start=\"2\" class=\"wp-block-list\">\n<li><span style=\"font-weight: 400;\">You can start the application using any of the following commands:<\/span><\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code><span style=\"font-weight: 400;\">data_formulator<\/span><\/code><\/pre>\n<p><span style=\"font-weight: 400;\">OR<\/span><\/p>\n<pre class=\"wp-block-code\"><code><span style=\"font-weight: 400;\">python -m data_formulator<\/span><\/code><\/pre>\n<ol start=\"3\" class=\"wp-block-list\">\n<li><span style=\"font-weight: 400;\">You can also specify the custom port if required.<\/span><\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code><span style=\"font-weight: 400;\">python -m data_formulator --port <\/span><span style=\"font-weight: 400;\">8080<\/span><\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-method-2-through-github-codespaces\"><span style=\"font-weight: 400;\">Method 2: Through GitHub Codespaces<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The Data Formulator tool can be run in a completely zero-setup environment in GitHub Codespaces:<\/span><\/p>\n<ol class=\"wp-block-list\">\n<li><span style=\"font-weight: 400;\">Visit the Data Formulator Github <\/span><a href=\"https:\/\/github.com\/microsoft\/data-formulator?tab=readme-ov-file\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">repository<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Click \u201cOpen in GitHub Codespaces\u201d.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Wait for this environment to initialize (~5 minutes).<\/span><\/li>\n<li><span style=\"font-weight: 400;\">You can start using Data Formulator immediately.<\/span><\/li>\n<\/ol>\n<h3 class=\"wp-block-heading\" id=\"h-method-3-through-developer-mode\"><span style=\"font-weight: 400;\">Method 3: Through Developer Mode<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">For users who want the entire development environment in their hands, they can do so by following these steps:<\/span><\/p>\n<ol class=\"wp-block-list\">\n<li><span style=\"font-weight: 400;\">Create a git clone of the repository:\u00a0 <\/span><a href=\"https:\/\/github.com\/microsoft\/data-formulator\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">https:\/\/github.com\/microsoft\/data-formulator<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Follow the instructions in <\/span><a href=\"https:\/\/github.com\/microsoft\/data-formulator\/blob\/main\/DEVELOPMENT.md\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">DEVELOPMENT.md<\/span><\/a><span style=\"font-weight: 400;\"> thoroughly for the setup.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Set up your favourite development environment.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Configure the AI model by choosing a policy for entering API keys for your preferred LLM.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Upload your data in the form of a CSV file, or connect it to a data source.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Start making visualisations from the user interface.<\/span><\/li>\n<\/ol>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"605\" height=\"478\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image8-4.webp\" alt=\"\" class=\"wp-image-236469\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image8-4.webp 605w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image8-4-300x237.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image8-4-150x119.webp 150w\" sizes=\"auto, (max-width: 605px) 100vw, 605px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-hands-on-application-of-data-formulator\"><span style=\"font-weight: 400;\">Hands-on Application of Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Now, let\u2019s try building a sales performance dashboard using the Data Formulator. For this task, we\u2019ll be using GitHub CodeSpaces to launch a dedicated development environment.<\/span><\/p>\n<p><b>Step 1:<\/b><span style=\"font-weight: 400;\"> Open GitHub CodeSpaces and click on the green button on the GitHub repository, which will create a separate workspace for you.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"865\" height=\"160\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image3-5.webp\" alt=\"\" class=\"wp-image-236470\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image3-5.webp 865w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image3-5-300x55.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image3-5-768x142.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image3-5-150x28.webp 150w\" sizes=\"auto, (max-width: 865px) 100vw, 865px\"\/><\/figure>\n<p><b>Step 2:<\/b><span style=\"font-weight: 400;\"> Let the CodeSpace initialize, which usually takes ~ 2-5 minutes. Once the Github CodeSpace is created, it will look like this:<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1797\" height=\"805\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2.webp\" alt=\"\" class=\"wp-image-236471\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2.webp 1797w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2-300x134.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2-768x344.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2-1536x688.webp 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image11-2-150x67.webp 150w\" sizes=\"auto, (max-width: 1797px) 100vw, 1797px\"\/><\/figure>\n<p><b>Step 3:<\/b><span style=\"font-weight: 400;\"> In the terminal of the Codespace, run the following command:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">python3 -m data_formulator<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Which will show an output like:<\/span><\/p>\n<pre class=\"wp-block-code\"><code><span style=\"font-weight: 400;\">Starting server on port 3000<\/span>\n<span style=\"font-weight: 400;\">...<\/span>\n<span style=\"font-weight: 400;\">Open http:\/\/localhost:3000<\/span><\/code><\/pre>\n<p><b>Step 4:<\/b><span style=\"font-weight: 400;\"> In the CodeSpaces toolbar, click on \u2018Port\u2019<\/span>. This<span style=\"font-weight: 400;\"> will open your interface in a separate browser window.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1842\" height=\"892\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2.webp\" alt=\"\" class=\"wp-image-236472\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2.webp 1842w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2-300x145.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2-768x372.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2-1536x744.webp 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image10-2-150x73.webp 150w\" sizes=\"auto, (max-width: 1842px) 100vw, 1842px\"\/><\/figure>\n<p><b>Step 5:<\/b><span style=\"font-weight: 400;\"> Here, you can select your preferred key type, model name, and set the secret key for the creation of the dashboard.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1140\" height=\"216\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image7-5.webp\" alt=\"\" class=\"wp-image-236473\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image7-5.webp 1140w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image7-5-300x57.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image7-5-768x146.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image7-5-150x28.webp 150w\" sizes=\"auto, (max-width: 1140px) 100vw, 1140px\"\/><\/figure>\n<p><b>Step 6: <\/b><span style=\"font-weight: 400;\">Upload the dataset. For our example, I am uploading <\/span><a href=\"https:\/\/github.com\/selva86\/datasets\/blob\/master\/supermarket_sales.csv\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><span style=\"font-weight: 400;\">supermarket_sales.csv<\/span><\/a><span style=\"font-weight: 400;\"> data for analysis.<\/span><\/p>\n<p><b>Step 7: <\/b><span style=\"font-weight: 400;\">For the basic visualization, you can choose a bar chart out of all the options, and then assign the x-axis and y-axis values. For our analysis, I have assigned the branch to the x-axis and the total to the y-axis. Here\u2019s the chart Data Formulator created for me.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1352\" height=\"633\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image5-5.webp\" alt=\"\" class=\"wp-image-236474\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image5-5.webp 1352w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image5-5-300x140.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image5-5-768x360.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image5-5-150x70.webp 150w\" sizes=\"auto, (max-width: 1352px) 100vw, 1352px\"\/><\/figure>\n<p><b>Step 8: <\/b><span style=\"font-weight: 400;\">For a different AI-powered calculation, you can choose other fields on the x-axis and y-axis. Then add your prompt and formulate. For instance, here I\u2019m going to type in the prompt box \u201cSum the total sales for each city\u201d and click on \u201cFormulate\u201d.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1360\" height=\"643\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image6-4.webp\" alt=\"\" class=\"wp-image-236475\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image6-4.webp 1360w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image6-4-300x142.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image6-4-768x363.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image6-4-150x71.webp 150w\" sizes=\"auto, (max-width: 1360px) 100vw, 1360px\"\/><\/figure>\n<p><b>Step 9: <\/b><span style=\"font-weight: 400;\">You can create various other types of charts and visualizations using the customized dashboard and come up with amazing analyses of your data.<\/span><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"725\" height=\"283\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image4-5.webp\" alt=\"\" class=\"wp-image-236476\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image4-5.webp 725w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image4-5-300x117.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/05\/image4-5-150x59.webp 150w\" sizes=\"auto, (max-width: 725px) 100vw, 725px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-use-cases-of-data-formulator\"><span style=\"font-weight: 400;\">Use Cases of Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Microsoft\u2019s Data Formulator is of great use across various domains as it enables AI-powered explorations and visualizations. Some of its most prominent use cases are:<\/span><\/p>\n<ul class=\"wp-block-list\">\n<li><b>Business Intelligence and Reporting: <\/b><span style=\"font-weight: 400;\">Fueled by executive dashboards and operational reports, Data Formulator stands out. Business analysts can instantly transform sales data, financial metrics, or operational KPIs into visualizations and representations without exercising any technical expertise.<\/span><\/li>\n<li><b>Academic Research and Analysis: <\/b><span style=\"font-weight: 400;\">In the research context, Data Formulator assists in the investigation of complicated datasets and the generation of publication-ready visualizations. Because of its iterative nature, the tool supports exploratory data analysis workflows common in academic research.<\/span><\/li>\n<li><b>Marketing Analytics: <\/b><span style=\"font-weight: 400;\">With Data Formulator, marketing professionals analyze campaign performances, customer segmentations, and conversion funnels. The presence of calculated fields makes it easy to compute the metrics. For example, customer lifetime value, retention rates, and campaign ROI can be computed without any convoluted formulas.<\/span><\/li>\n<li><b>Financial Analysis: <\/b><span style=\"font-weight: 400;\">Financial analysts can build complex models for risk measurement, portfolio analysis, and performance tracking. It can handle large data sets and connect to real-time data. Therefore, it can be used in analyzing market data, trade patterns, and financial forecasts.<\/span><\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-advantages-of-data-formulator\"><span style=\"font-weight: 400;\">Advantages of Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The Data Formulator is headed toward maximizing the accessibility of data, speed, and intelligent data processing.<\/span><\/p>\n<ul class=\"wp-block-list\">\n<li><b>Democratization of Data Analysis: <\/b><span style=\"font-weight: 400;\">The strength of Data Formulator is its largest in making advanced data visualization truly accessible to non-technical users. It eliminates the need for coding skills to analyze data directly, without having to go through technical resources.<\/span><\/li>\n<li><b>Rapid Prototyping and Iteration: <\/b><span style=\"font-weight: 400;\">The conversational interface allows users to consider various visualization approaches quickly. Users can analyze ideas briefly, put the finishing touches on a chart, and view alternative ways to look at their data. The tool significantly reduces the time it takes to go from question to insight.<\/span><\/li>\n<li><b>Intelligent Data Transformations: <\/b><span style=\"font-weight: 400;\">While an ordinary tool expects users to prepare their data, Data Formulator handles complex transformations and aggregations. It does calculations from users\u2019 instructions automatically, which helps save hours otherwise spent in manual data wrangling.<\/span><\/li>\n<li><b>Transparency and Explainability: <\/b><span style=\"font-weight: 400;\">This system generates human-readable code for all transformations. It makes it easier for users as they may safely ascertain the logic of their visualizations to build trust and learn.<\/span><\/li>\n<li><b>Cost-Effective Solution: <\/b><span style=\"font-weight: 400;\">Being an open-source tool, Data Formulator provides enterprise-grade capabilities at zero licensing cost. The organizations may also deploy the tool internally, keeping total control over the data and any customizations.<\/span><\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-limitations-of-data-formulator\"><span style=\"font-weight: 400;\">Limitations of Data Formulator<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">While Data Formulator is overcoming some of the greatest challenges, it does not come without some constraints, namely:<\/span><\/p>\n<ul class=\"wp-block-list\">\n<li><b>AI Model Dependencies: <\/b><span style=\"font-weight: 400;\">The efficacy of Data Formulator depends on the particular ability of an AI Model. Complex analytical tasks may require the intervention of expensive high-end models, which could even entail the very best models.<\/span><\/li>\n<li><b>Limited Visualization Types: <\/b><span style=\"font-weight: 400;\">It supports standard chart types and specialized visualizations such as network analysis, geospatial mapping, and other statistical plotting.<\/span><\/li>\n<li><b>Workability on Large Datasets:<\/b><span style=\"font-weight: 400;\"> While it performs better on large datasets, the implementation using DuckDB continues to face bottlenecks on very large datasets. They are usually measured in terabytes in its early phases of data loading.<\/span><\/li>\n<li><b>Ambiguity of Natural Language:<\/b><span style=\"font-weight: 400;\"> Complicated analytical requests may be interpreted wrongly by the AI and thus subjected to wrong transformations. Clear, precise prompts should be given by the users, which may usually be a tough task for those lacking technical skills.<\/span><\/li>\n<li><b>Privacy and Security Considerations:<\/b><span style=\"font-weight: 400;\"> Cloud-based AI models may pose the risk of transmission of sensitive data to external services. Organizations with a strict data governance policy may prefer to deploy local models or adopt necessary security measures.<\/span><\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\"><span style=\"font-weight: 400;\">Conclusion<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Microsoft marks a landmark in enhancing accessibility to data analysis and data visualization through the Data Formulator tool. By merging AI with intuitive user interfaces, the research group has been able to develop a tool that bridges the gaps between data complexity and analytical insights. By automatic conversion of complicated data transformations through code generation, it caters equally well to all users.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data Formulator presents a compelling, cost-effective solution for organizations that want to do data analytics and visualization on their own. As AI evolves, tools like Data Formulator will further reduce the time between posing a question about data and receiving an answer in return.<\/span><\/p>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/riyab20021618492\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_5X1DGT2.webp\" width=\"48\" height=\"48\" alt=\"Riya Bansal.\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Gen AI Intern at Analytics Vidhya\u00a0<br \/>Department of Computer Science, Vellore Institute of Technology, Vellore, India\u00a0<\/p>\n<p>I am currently working as a Gen AI Intern at Analytics Vidhya, where I contribute to innovative AI-driven solutions that empower businesses to leverage data effectively. As a final-year Computer Science student at Vellore Institute of Technology, I bring a solid foundation in software development, data analytics, and machine learning to my role.\u00a0<\/p>\n<p>Feel free to connect with me at <a href=\"https:\/\/www.analyticsvidhya.com\/cdn-cgi\/l\/email-protection\" class=\"__cf_email__\" data-cfemail=\"34465d4d551a56555a47555874555a55584d405d5747425d505c4d551a575b59\">[email\u00a0protected]<\/a>\u00a0<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>In today\u2019s data-driven world, every researcher and analyst requires the ability to yield prompt information from raw data and present it in visual form. That\u2019s exactly what Microsoft\u2019s new AI tool, Data Formulator, can help you with. It simplifies data visualization by presenting the data as interesting charts and graphs, especially for those without much [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":265528,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[10990,11603,807,11536,1447],"dealstore":[],"offerexpiration":[],"class_list":["post-265527","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analysis","tag-data","tag-free","tag-microsofts","tag-tool"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Microsoft&#039;s Free AI Tool for Data Analysis - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=265527\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microsoft&#039;s Free AI Tool for Data Analysis - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"In today\u2019s data-driven world, every researcher and analyst requires the ability to yield prompt information from raw data and present it in visual form. 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