{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:27:11Z","timestamp":1781882831303,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper aims to review, characterize and comparatively analyze selected organizations\u2019 analytics maturity models. Eleven various organizations\u2019 analytics maturity models (AMMs) were characterized. The models\u2019 characteristics were developed based on an academic literature review as well as reports and publications shared by analytics sector operators. Most of the analyzed models comprised five analytics maturity levels. Comprehensive descriptions of an organization\u2019s analytics maturity levels were available for all models. However, no detailed description of the assessment process or criteria for placing an organization at a specific analytics development level were available in all cases. Selected analytics maturity models were described in such a detailed manner that their application in an independent assessment of an organization\u2019s analytics maturity was possible. In the future, an increase is expected in both the number and availability of new analytics maturity models, in particular those personalized and dedicated to a specific sector or business, and the number of entities involved in an assessment of an organization\u2019s analytics maturity and the implementation of data analytics in organizations. The article presents and summarizes selected features of eleven various organizations\u2019 analytics maturity models. This is the firstever such extensive review of those models.<\/jats:p>","DOI":"10.3390\/info11030142","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T13:06:23Z","timestamp":1583240783000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Analytics Maturity Models: An Overview"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0534-8471","authenticated-orcid":false,"given":"Karol","family":"Kr\u00f3l","sequence":"first","affiliation":[{"name":"Department of Land Management and Landscape Architecture, Faculty of Environmental Engineering and Land Surveying; University of Agriculture in Krak\u00f3w; Balicka 253c, 30-149 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6190-9643","authenticated-orcid":false,"given":"Dariusz","family":"Zdonek","sequence":"additional","affiliation":[{"name":"Institute of Economics and Informatics, Faculty of Organization and Management, Silesian University of Technology in Gliwice; Akademicka 2A, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"ref_1","unstructured":"Logi Analytics (2018). 2018 State of Embedded Analytics. 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