{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:21:22Z","timestamp":1774797682785,"version":"3.50.1"},"reference-count":40,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2017,4,3]],"date-time":"2017-04-03T00:00:00Z","timestamp":1491177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PROG"],"published-print":{"date-parts":[[2017,4,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Many organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors\u2019 pursuit of these elusive mythical creatures.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Qualitative data were collected through semi-structured interviews with managers\/directors from nine Australian state and federal government agencies with relatively mature data science functions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Although the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>Given that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/prog-07-2016-0053","type":"journal-article","created":{"date-parts":[[2017,3,22]],"date-time":"2017-03-22T11:04:33Z","timestamp":1490180673000},"page":"65-74","source":"Crossref","is-referenced-by-count":33,"title":["Unicorn data scientist: the rarest of breeds"],"prefix":"10.1108","volume":"51","author":[{"given":"Sa\u0161a","family":"Ba\u0161karada","sequence":"first","affiliation":[]},{"given":"Andy","family":"Koronios","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020120805462599000_ref001","first-page":"145","article-title":"An undergraduate degree in data science: curriculum and a decade of implementation experience","year":"2014"},{"issue":"40","key":"key2020120805462599000_ref002","first-page":"1","article-title":"Qualitative case study guidelines","volume":"19","year":"2014","journal-title":"The Qualitative Report"},{"issue":"1","key":"key2020120805462599000_ref003","first-page":"5","article-title":"Data, information, knowledge, wisdom (DIKW): a semiotic theoretical and empirical exploration of the hierarchy and its quality dimension","volume":"18","year":"2013","journal-title":"Australasian Journal of Information Systems"},{"issue":"4","key":"key2020120805462599000_ref004","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/10580530.2014.958023","article-title":"A critical success factor framework for information quality management","volume":"31","year":"2014","journal-title":"Information Systems Management"},{"issue":"4","key":"key2020120805462599000_ref005","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/00031305.2015.1081105","article-title":"A data science course for undergraduates: thinking with data","volume":"69","year":"2015","journal-title":"The American Statistician"},{"key":"key2020120805462599000_ref006","unstructured":"Bertolucci, J. 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