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Understanding Data & Analytics Maturity: A Systematic Review of Maturity Model Composition

Benedict Langer

Schmalenbach Journal of Business Research2025https://doi.org/10.1007/s41471-024-00205-2review
AJG 2
Weight
0.58

What the paper says

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.

12 citations

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https://doi.org/https://doi.org/10.1007/s41471-024-00205-2

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@article{benedict2025,
  title        = {{Understanding Data & Analytics Maturity: A Systematic Review of Maturity Model Composition}},
  author       = {Benedict Langer},
  journal      = {Schmalenbach Journal of Business Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s41471-024-00205-2},
}

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Understanding Data & Analytics Maturity: A Systematic Review of Maturity Model Composition

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Evidence weight

0.58

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.58 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.