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- Title:
Understanding Data & Analytics Maturity: A Systematic Review of Maturity Model Composition
- Document type:
- Zeitschriftenaufsatz
- Author(s):
- Langer, Benedict
- Non-TUM Co-author(s):
- nein
- Cooperation:
- -
- Abstract:
- Leveraging data is becoming increasingly important for businesses. However , this transformation can be complex, as it requires a vast array of social and technical capabilities. To generate consensus in this domain, this study examines data & analytics maturity models by analyzing their architectures, maturity levels, and maturity domains. A systematic review based on the PRISMA framework identifies 38 maturity models and inductively derives insights into their composition. Three different content types are differentiated, namely organization-oriented, technology-oriented and data-oriented models. The initial findings provide a comprehensive overview of the status quo in data & analytics maturity models and provide a foundation for further research in this field. The study thus contributes towards enabling businesses to conduct more sophisticated data & analytics maturity assessments and support more effective use of data.
- Keywords:
- Data; Analytics; Maturity; Maturity models; Literature review
- Intellectual Contribution:
- Discipline-based Research
- Journal title:
- Schmalenbach Journal of Business Research
- Journal listet in FT50 ranking:
- nein
- Year:
- 2025
- Journal volume:
- 77
- Journal issue:
- 2
- Pages contribution:
- 205-227
- Fulltext / DOI:
- doi:10.1007/s41471-024-00205-2
- Publisher:
- Springer Science and Business Media LLC
- E-ISSN:
- 0341-2687; 2366-6153
- Scimago Quartil:
- Q2
- Date of publication:
- 29.01.2025
- Judgement review:
- None
- Key publication:
- Nein
- Peer reviewed:
- Ja
- Commissioned:
- not commissioned
- Technology:
- Ja
- Interdisciplinarity:
- Ja
- Mission statement:
- ;
- Ethics and Sustainability:
- Nein
- SDG:
- ;
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