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Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA\u2019s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.<\/jats:p>","DOI":"10.1007\/s10462-024-10811-5","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T11:02:33Z","timestamp":1718622153000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Big data and predictive analytics: A systematic review of applications"],"prefix":"10.1007","volume":"57","author":[{"given":"Amirhossein","family":"Jamarani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeid","family":"Haddadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raheleh","family":"Sarvizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Haghi Kashani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Akbari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Moradi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"10811_CR1","doi-asserted-by":"publisher","unstructured":"Abeza G, O\u2019Reilly N, Nadeau J, Abdourazakou Y (2022) Big data in professional sport: the perspective of practitioners in the NFL, MLB, NBA, and NHL,\" Journal of Strategic Marketing, pp. 1\u201321, 2022, https:\/\/doi.org\/10.1080\/0965254X.2022.2108881","DOI":"10.1080\/0965254X.2022.2108881"},{"key":"10811_CR2","unstructured":"Abkenar SB, Kashani MH, Akbari M, Mahdipour E (2020) Twitter spam detection: A systematic review. arXiv preprint arXiv:2011.14754"},{"key":"10811_CR3","doi-asserted-by":"publisher","unstructured":"Aboutorabi SH, Rezapour M, Moradi M, Ghadiri N (2015) Performance evaluation of SQL and MongoDB databases for big e-commerce data,\" in 2015 International Symposium on Computer Science and Software Engineering (CSSE), 18\u201319 Aug. 2015 2015, pp. 1\u20137, https:\/\/doi.org\/10.1109\/CSICSSE.2015.7369245","DOI":"10.1109\/CSICSSE.2015.7369245"},{"key":"10811_CR4","doi-asserted-by":"publisher","first-page":"102713","DOI":"10.1016\/j.technovation.2023.102713","volume":"123","author":"C Acciarini","year":"2023","unstructured":"Acciarini C, Cappa F, Boccardelli P, Oriani R (2023) How can organizations leverage big data to innovate their business models? 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