{"id":5157,"date":"2026-02-25T08:43:39","date_gmt":"2026-02-25T07:43:39","guid":{"rendered":"https:\/\/www.beeminds.nl\/insights\/what-is-synthetic-data"},"modified":"2026-02-25T08:43:39","modified_gmt":"2026-02-25T07:43:39","slug":"what-is-synthetic-data","status":"publish","type":"knowledge_pt","link":"https:\/\/www.beeminds.nl\/en\/insights\/what-is-synthetic-data","title":{"rendered":"What is synthetic data?"},"content":{"rendered":"<p>Synthetic data is artificially generated data designed to mimic the characteristics and patterns of real data. It is often used to protect privacy-sensitive data or to supplement data sets that are too small to be used in specific situations. <\/p>\n<p>Synthetic data is generated by sophisticated algorithms using machine learning techniques. These algorithms analyze the characteristics and patterns of real data and then create new synthetic data points that reflect these characteristics and patterns.   <\/p>\n<p>Synthetic data can be generated for different types of data, including text, images, videos and sounds.<\/p>\n<p>There are several advantages to using synthetic data. First, it can be used to protect privacy-sensitive information because synthetic data does not contain real personal data. Second, it can be used to increase the size of data sets because generating synthetic data can be faster and cheaper than collecting real data. Moreover, the use of synthetic data can help reduce bias (prejudices) in data sets because it allows the creation of new data points that are not affected by biases in the real data.   <\/p>\n<p>While synthetic data has many advantages, it also has some disadvantages. For example, synthetic data may not reflect all the characteristics of real data, and it can be difficult to guarantee that it is accurate enough. Moreover, using synthetic data can lead to problems with interpretation because it is not always clear exactly how this data was created.  <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Synthetic data is artificially generated data designed to mimic the characteristics and patterns of real data.<\/p>\n","protected":false},"featured_media":0,"template":"","knowledge_type":[58],"knowledge_category":[],"class_list":["post-5157","knowledge_pt","type-knowledge_pt","status-publish","hentry","knowledge_type-simplified"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_pt\/5157","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_pt"}],"about":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/types\/knowledge_pt"}],"wp:attachment":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/media?parent=5157"}],"wp:term":[{"taxonomy":"knowledge_type","embeddable":true,"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_type?post=5157"},{"taxonomy":"knowledge_category","embeddable":true,"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_category?post=5157"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}