{"id":967218,"date":"2023-11-08T14:36:00","date_gmt":"2023-11-08T22:36:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=967218"},"modified":"2026-09-10T15:35:57","modified_gmt":"2026-09-10T22:35:57","slug":"self-service-data-preparation","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/self-service-data-preparation\/","title":{"rendered":"Self-service Data Preparation"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background bg-gray-200 has-background- card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 \">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"self-service-data-preparation\" class=\"wp-block-heading\"><em>Self-service Data Preparation<\/em><\/h1>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">It is often cited that data scientists spend a significant portion of their time (up to 80%), cleaning and preparing data. For less-technical users, who may be less proficient in writing code (e.g., in Excel, Power-BI and Tableau), the tasks of preparing and cleaning data are not just time-consuming, but also technically challenging. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In the &#8220;<em>Self-service Data Preparation<\/em>&#8221; project, our goal is to develop technologies that can automate common data-preparation tasks in the context of data science and business intelligence workflows, including by leveraging recent advances in AI and large language models (LLMs). We aim to empower technical and non-technical users alike, towards the democratization of data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Our research has been recognized with best paper awards at VLDB and SIGMOD. Some of our technologies have been integrated into Microsoft products such as <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/docs.microsoft.com\/en-us\/power-query\/power-query-what-is-power-query\">Power Query<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> for <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/powerbi.microsoft.com\/en-us\/\">Power BI<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (program synthesis, operator recommendations, fuzzy join, fuzzy deduplication), <a href=\"https:\/\/www.microsoft.com\/en-us\/microsoft-365\/excel\">Excel<\/a> (error detection, data cleansing), <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/docs.microsoft.com\/en-us\/python\/api\/overview\/azure\/dataprep\/intro?view=azure-dataprep-py\">Azure Machine Learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (data prep sdk), <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/azure.microsoft.com\/en-us\/services\/purview\/\">Azure Purview<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (auto-tagging in data lake), <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/azure.microsoft.com\/en-us\/products\/data-factory\">Azure Data Factory<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (fuzzy join), and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/dynamics.microsoft.com\/en-us\/ai\/customer-insights\/\">Dynamics 365 Customer Insights<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (fuzzy join, fuzzy deduplication). <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n","protected":false},"excerpt":{"rendered":"<p>It is often cited that data scientists spend a significant portion of their time (up to 80%), cleaning and preparing data. For less-technical users, who may be less proficient in writing code (e.g., in Excel, Power-BI and Tableau), the tasks of preparing and cleaning data are not just time-consuming, but also technically challenging. In the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13563],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-967218","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[847864,1150129,1150005,1149273,1140259,1136589,1136581,1094523,1026357,1026345,957138,950241,950232,940866,329798,762010,739477,732355,698740,654228,610266,578671,575673,496655,481248,480054,372359],"related-downloads":[],"related-videos":[],"related-groups":[957177],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"related-researchers":[{"type":"user_nicename","display_name":"Yeye He","user_id":34992,"people_section":"Related people","alias":"yeyehe"},{"type":"user_nicename","display_name":"Vivek Narasayya","user_id":34602,"people_section":"Related people","alias":"viveknar"},{"type":"user_nicename","display_name":"Surajit Chaudhuri","user_id":33764,"people_section":"Related people","alias":"surajitc"},{"type":"user_nicename","display_name":"Junjie Xing","user_id":43890,"people_section":"Related people","alias":"junjiexing"}],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/967218","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":15,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/967218\/revisions"}],"predecessor-version":[{"id":1185922,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/967218\/revisions\/1185922"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=967218"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=967218"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=967218"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=967218"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=967218"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}