{"id":72304,"date":"2023-02-28T13:48:01","date_gmt":"2023-02-28T12:48:01","guid":{"rendered":"https:\/\/www.clickworker.com\/?page_id=72304"},"modified":"2023-03-02T13:03:09","modified_gmt":"2023-03-02T12:03:09","slug":"synthetic-data","status":"publish","type":"page","link":"https:\/\/www.clickworker.com\/ai-glossary\/synthetic-data\/","title":{"rendered":"Synthetic Data"},"content":{"rendered":"<section class=\"white section-space top_grey_separation_sm\">\r\n<div class=\"container pb-5\">\r\n<div class=\"row align-items-center justify-content-center justify-content-lg-between\">\r\n<div class=\"col-lg-6 pb-2\">\r\n\r\n<span class=\"gl_letter_offcanvas gl_single\">S<\/span>\r\n<h1 class=\"section-headline pt-4 mt-1 mb-4\"><span class=\"gl_single_headline\">Synthetic Data<\/span><\/h1>\r\n<ul>\r\n \t<li><a href=\"#explanation\">Synthetic Data \u2013 Short Explanation<\/a><\/li>\r\n \t<li><a href=\"#advantages\">Advantages of Synthetic Data<\/a><\/li>\r\n\t<li><a href=\"#types\">Types of Synthetic Data<\/a><\/li>\r\n\t<li><a href=\"#computervision\">How Can Synthetic Data Help Computer Vision?<\/a><\/li>\r\n\t<li><a href=\"#challenges\">Challenges of Synthetic Data<\/a><\/li>\r\n \t<li><a href=\"#applications\">Applications of Synthetic Data<\/a><\/li>\r\n\t<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\r\n\t<li><a href=\"#faq\">FAQ<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n\r\n<div class=\"col-lg-6\">\r\n\t<img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/uploads\/2023\/02\/Synthetic-Data-1.png\" alt=\"synthetic data\" class=\"gl_image max-width-100\"\/><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n\r\n<section class=\"gray section-space\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"explanation\">Synthetic Data \u2013 Short Explanation<\/h2>\r\n<p>Any information that has been created artificially and does not accurately reflect events or things in the real world is considered Synthetic Data. Synthetic data produced by algorithms is utilized in model datasets for validation or training. In order to test or train <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/machine-learning\/\">machine learning<\/a> (ML) models, synthetic data can simulate operational or production data.<\/p>\r\n\r\n<p><strong>Video on Synthetic Data<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<iframe class=\"rounded\" width=\"100%\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/oGPTwqSKMss\"\u00a0frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\r\n<\/iframe>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"white section-space bottom_grey_separation_sm\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"idea\">Advantages of Synthetic Data<\/h2>\r\n<p>Real or artificial data doesn&#8217;t matter to data professionals. The qualities, balance, and bias of the data\u2014their traits and patterns\u2014are what actually count. Your ability to refine and enhance your data with synthetic data unlocks a number of significant advantages.<\/p>\r\n<ul><li><strong>Increased Quality of Data<\/strong><br>\r\n<p>Real-world data is not only expensive but also difficult to obtain. However, it is subject to <a href=\"https:\/\/www.statice.ai\/post\/data-bias-types\" target=\"_blank\" rel=\"noopener\">bias<\/a>, errors, and other flaws that might negatively affect the accuracy of your machine learning model.<br>\r\nDue to synthetic data generation, there is a better quality, diversity, and balance of data.<\/p><\/li>\r\n<li><strong>Privacy Concerns of Handling Sensitive Data<\/strong><br>\r\n<p>When storing, distributing, and annotating <a href=\"https:\/\/www.investopedia.com\/terms\/p\/personally-identifiable-information-pii.asp\" target=\"_blank\" rel=\"noopener\">Personally Identifying Information (PII)<\/a> or other types of sensitive data, the collection of real-world datasets is frequently connected with significant privacy hazards.\r\nIn these cases, creating datasets using synthetic data can be a practical way to do so while maintaining the statistical features needed to train and test a model without having direct access to sensitive information.<\/p><\/li>\r\n<li><strong>Cost and Speed of Data Acquisition<\/strong><br>\r\n<p>Real-world data collection is typically time-consuming and expensive.<br>\r\nSynthetic data synthesis\u00a0is reasonably quick and affordable to generate in large quantities.<\/p><\/li><\/ul>\r\n\r\n\r\n<blockquote class=rounded><p class=\"pb-3\"><strong>Tip:<\/strong><\/p>\r\n<p class=\"pb-3\">High-quality Training Data can be easily available from clickworker in all quantities to train your machine learning models\r\n<\/p><a href=\"https:\/\/www.clickworker.com\/ai-datasets-for-machine-learning\/\" class=\"btn btn-primary\" role=\"button\">More About AI Datasets for Machine Learning Services<\/a><\/blockquote>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"types\">Types of Synthetic Data<\/h2>\r\n<p>Synthetic data is broadly classified into three categories:<\/p>\r\n<ol><li><strong>Fully Synthetic Data<\/strong><br>\r\n<p>This data is purely synthetic and contains no raw data. If only a small portion of the real data&#8217;s features are chosen to be replaced by synthetic data, the protected series of those features is then mapped to the remaining real data&#8217;s features in order to rank the protected and real series in the same order. Bootstrap approaches and multiple imputations are two examples of traditional techniques that can be used to generate totally synthetic data. This method has great privacy protection with a fallback on the veracity of the data because the data is entirely synthetic and no real data exists.<\/p><\/li>\r\n<li><strong>Partially Synthetic Data<\/strong><br>\r\n<p>Only some selected sensitive feature values are replaced with synthetic values in this dataset. In this scenario, the actual values are only changed if there is a substantial risk of disclosure. Privacy in the newly generated data is maintained by doing this.<\/p><\/li>\r\n<li><strong>Hybrid Synthetic Data<\/strong><br>\r\n<p>Both genuine and artificial data are used to generate this dataset. A similar record from the synthetic data is chosen for each random actual data record, and both are then mixed to create hybrid data. Benefits of both fully and partially synthetic data are offered. Hence, it is well renowned for offering good privacy preservation with greater utility than the other two, but at the cost of additional memory and processing time.<\/p><\/li><\/ol>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"computervision\">How Can Synthetic Data Help Computer Vision?<\/h2>\r\n<p>In <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/computer-vision\/\">computer vision<\/a>, images that are created by algorithms rather than being photographed are referred to as &#8220;synthetic data.&#8221; Typically, these photos are produced to train artificial intelligence (AI) models.\r\nWhen compared to real data, using synthetic data has a number of benefits.<\/p>\r\n<ul><li>First, obtaining a lot of synthetic data is simpler than obtaining a lot of actual data.<\/li>\r\n<li>Second, it is possible to create synthetic data with particular characteristics that are challenging to locate in actual data. For instance, it is possible to create images of things that are partially obscured from view, or occluded. For developing object identification models, this is helpful.<\/li>\r\n<li>Controlled variations can be used to create synthetic data. By changing the color of the items in the photographs, for instance, it is implied that the generated data can be altered routinely. This is helpful for developing models that are resistant to aesthetic alterations.<\/li>\r\n<li>Fourth, labels can be used to create artificial data. This implies that the image&#8217;s subject is named and identified, for instance, as a &#8220;vehicle,&#8221; a &#8220;lion,&#8221; or a &#8220;person.&#8221;<\/li><\/ul>\r\n\r\n<p><strong>Video on Computer Vision<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<iframe class=\"rounded\" width=\"100%\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/-4E2-0sxVUM\"\u00a0frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\r\n<\/iframe>\r\n<\/div>\r\n<\/div>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"challenges\">Challenges of Synthetic Data<\/h2>\r\n<p>Besides a variety of benefits, there are some challenges with using synthetic data.<\/p>\r\n<ul><li><strong>Lack of accuracy:<\/strong> The fact that synthetic data is frequently produced by computer algorithms, which are not necessarily reliable, presents a challenge. As a result, outcomes from using synthetic data might prove to be incorrect.<\/li>\r\n<li><strong>Dependency on the real data:<\/strong> The quality of synthetic data frequently depends on the dataset and real model that were generated for it. Many synthetic datasets that are produced in large quantities utilizing the original dataset can end up working ineffectively and occasionally even inaccurately in the absence of a suitable and high-quality genuine dataset.<\/li>\r\n<li><strong>Biased results:<\/strong> Due to its lack of variability and correlation, synthetic data can be deceptive, constrained, or discriminatory.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"applications\">Applications of Synthetic Data<\/h2>\r\n<p>Artificially created data, also known as synthetic data, offers answers to issues like data privacy and limited data size that are frequently faced in data science applications. Here is a list of the capabilities and most typical applications for synthetic data across many sectors, departments, and business units.<\/p>\r\n<ul><li><strong>Fraud identification<\/strong><br> \r\n<p>Fraud identification is a major part of any financial service. With synthetic fraud data, new fraud detection methods can be tested and evaluated for their effectiveness.<\/p><\/li>\r\n<li><strong>Healthcare analytics<\/strong><br> \r\n<p>Healthcare data specialists can permit both internal and external use of record data while still protecting patient privacy due to synthetic data.<\/p><\/li>\r\n<li><strong>Training AI Models<\/strong><br> \r\n<p>It is challenging to predict uncommon events like fraud or manufacturing flaws since limited data sets make ML models inaccurate. Accuracy of the model is increased by creating synthetic examples of similar situations.<\/p><\/li>\r\n<li><strong>Customer Analysis<\/strong><br> \r\n<p>In order to analyze customer data and comprehend consumer behavior, synthetic customer transaction data may be used.<\/p><\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"conclusion\">Conclusion<\/h2>\r\n<p>Synthetic data will transform the field of machine learning and artificial intelligence (AI). In order to create precise, extensible AI models, access to better annotated data might be a useful addition to or substitute for real data. Synthetic data can often be used to improve genuine data when coupled with it, hence reducing its flaws.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n\r\n<section class=\"gray section-space\">\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"container\">\t\t\t\r\n\t\t<h2 id=\"faq\">FAQs on Synthetic Data<\/h2>\t\r\n\t\t<!--<div class=\"h2 d-block faq-snippet-heading cw-dark-gray\" data-toggle=\"collapse\" data-target=\"#faqCrowdsourcingSnippet\" role=\"button\" aria-expanded=\"false\" aria-controls=\"faqCrowdsourcingSnippet\">\r\n\t\t\t\tCrowdsourcing - Goolge FAQ Snippets\r\n\t\t\t<\/div>-->\r\n\t\t<div id=\"faqDataAnnotationSnippet\">\r\n\t\t\t\t<div class=\"row\">\r\n\t\t\t\t\t\t<div class=\"col-lg-12\">\r\n\t\t\t\t\t\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is Synthetic Data?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tSynthetic data is artificial data that is generated from original data and a model that is trained to reproduce the characteristics and structure of the original data.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What are the types of Synthetic Data?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\t<ol><li>Fully Synthetic Data<\/li>\r\n\t\t\t\t\t\t\t\t<li>Partially Synthetic Data<\/li>\r\n\t\t\t\t\t\t\t\t<li>Hybrid Synthetic Data<\/li><\/ol>\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What are the benefits of synthetic data over real data? <\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tCompared to real-world data, synthetic data generation is faster, more flexible, and more scalable.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\n<script type=\"application\/ld+json\">\n\t{\n\t\t\"@context\": \"https:\/\/schema.org\",\n\t\t\"@type\": \"FAQPage\",\n\t\t\"mainEntity\": [\n\t\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is Synthetic Data?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Synthetic data is artificial data that is generated from original data and a model that is trained to reproduce the characteristics and structure of the original data.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What are the types of Synthetic Data?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<ol><li>Fully Synthetic Data<\/li><li>Partially Synthetic Data<\/li><li>Hybrid Synthetic Data<\/li><\/ol>\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What are the benefits of synthetic data over real data? \",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Compared to real-world data, synthetic data generation is faster, more flexible, and more scalable.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t\t\t\t]\n\t}\n<\/script>\n\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<\/div>\r\n\r\n\t<\/section>","protected":false},"excerpt":{"rendered":"<p>S Synthetic Data Synthetic Data \u2013 Short Explanation Advantages of Synthetic Data Types of Synthetic Data How Can Synthetic Data Help Computer Vision? Challenges of Synthetic Data Applications of Synthetic Data Conclusion FAQ Synthetic Data \u2013 Short Explanation Any information that has been created artificially and does not accurately reflect events or things in the [&hellip;]<\/p>\n","protected":false},"author":65,"featured_media":0,"parent":54483,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blank_with_breadcrumb.php","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Synthetic Data - AI glossary \/ term explanation<\/title>\n<meta name=\"description\" content=\"Synthetic data produced by algorithms are utilized in model datasets for validation or training. 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