Santa Lemsa


Significance to understand the advanced analytics ecosystem maturity is increasing caused by constantly growing data volumes and demand for advanced analytics including automated decision making based on data or process automation. The analytics maturity assessment helps to identify strengths and weaknesses of the organization’s analytics ecosystem and can provide detailed action plan to move to the next level. The focus of the paper is to review and analyse analytics maturity models to assess their application as frame to build a new analytics maturity model or replicate with time adjustment any of reviewed models. The literature review and publicly available assessment models provided by analytics sector were used to review and analyse analytics maturity models.  Fifteen models were reviewed and four of them analysed by twelve characteristics. Summary of four models includes analytics maturity levels, domains, accessibility of questionnaire, discloser of maturity level detection and authors assessment of several characteristics. Comprehensive descriptions of analytics maturity levels were available for many models. Solid recommendation sets for each maturity level provided for the most disclosed models. One of the most important components, approach to detect specific maturity level, was not transparent or disclosed with limitations. However, it is possible to develop a new model or replicate in some extent based on models reviewed in this paper, but it requires extensive professional experience in advanced analytics and related functions.



advanced analytics, analytics maturity, maturity models, maturity assessment

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A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, pp. 137–144, 2015.

United States Government Accountability Office, “Data and Analytics Innovation. Emerging Opportunities and Challenges,” September 2016. [Online]. Available: [Accessed: Mar. 21, 2021].

C.V. Apte, S.J. Hong, R. Natarajan, E.P.D. Pednault, F.A. Tipu and S.M. Weiss, “Data-intensive analytics for predictive modeling,” IBM Journal of Research & Development, vol. 47 (1), pp. 17-23, 2003.

T.H. Davenport and J.G.S. Harris, Competing on Analytics: The New Science of Winning. Harvard Business Press, 2007.

H.J. Watson, “Recent Developments in Data Warehousing,” Communications of AIS, vol. 8, pp. 1-25, 2002. [Online]. Available: [Accessed March 18, 2021]

M. Comuzzi and A. Patel, “How organisations leverage Big Data: a maturity model,” Industrial Management & Data Systems, vol. 116(8), pp. 1468-1492, 2016.

K. Krol and D. Zdonek, ”Analytics Maturity Models: An Overview,” Information vol. 11, p. 142, March 2020. [Online]. Available: [Accessed October 18, 2020]

T.H. Davenport, J.G. Harris, R. Morison, Analytics at Work: Smarter Decisions, Better Results. Harvard Business School Publishing, 2010.

T.H. Davenport, J.G. Harris, Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. Harvard Business Press, 2017.

R. Cosic, G. Shanks and S. Maynard, Towards a Business Analytics Capability Maturity Model. In Proceedings of the 23rd Australasian Conference on Information Systems Business Analytics Capability, Dec 2012, Geelong.

J. Becker, R. Knackstedt and J. Pöppelbuß, “Developing Maturity Models for IT Management - A Procedure Model and its Application,” Business & Information Systems Engineering, vol. 1(3), pp. 213-222, 2009.

R.L. Grossman, “A framework for evaluating the analytic maturity of an organization,” International Journal of Information Management, vol. 38, pp. 45–51, 2018.

J. Piyanka, “The Analytics Maturity Quotient Framework,” 2019. [Online]. Available: [Accessed: Mar. 22, 2021].

Blast Analytics & Marketing, “Analytics Maturity Assessment”, Blast Analytics & Marketing, 2021. [Online]. Available: [Accessed: Mar. 19, 2021].

Association Analytics, “5 Areas to Assess Using the DAMM—Data Analytics Maturity Model,” Association Analytics, 2017. [Online]. Available: [Accessed: Mar. 19, 2021].

T.H. Davenport, “DELTA Plus Model & Five Stages of Analytics Maturity: A Primer,” International Institute for Analytics, 2018. [E-book] Available: [Accessed Mar. 20, 2021].

Logi Analytics, “The 5 Levels of Analytics Maturity: From Basic BI to Sophisticated Differentiators,” Logi Analytics, 2017. [E-book] Available: [Accessed: Mar. 19, 2021].

Cardinal Path, “How mature are your organization’s digital analytics?,” Cardinal Path, 20. [E-book] Available: [Accessed: Mar.19, 2021].

PharmaVOICE & SAS, “Five Steps to Analytical Maturity. A Guide for Pharma Commercial Operations,” 2014. [Online]. Available: [Accessed: Mar.19, 2021].

F. Halper, “TDWI Analytics Maturity Model. Assessment Guide”, 2020. [Online]. Available: [Accessed: Mar. 22, 2021].

S. Hamel, “The Web Analytics Maturity Model. A Strategic Approach Based on Business Maturity and Critical Success Factors,” 2009. [Online]. Available: [Accessed: Mar. 19, 2021].

J. Lismonta, J. Vanthienen, B. Baesens and W. Lemahieua, “Defining analytics maturity indicators: A survey approach,” International Journal of Information Management, vol. 37, pp. 114-124, Jun. 2017. [Online]. Available: [Accessed October 18, 2020],

TDWI Assessment, “Analytics Maturity Model Assessment,” 2020. [Online]. Available: [Accessed: Mar. 22, 2021].

International Institute for Analytics, “Estimate Your Analytics Maturity With Our Free Tool”, 2018. [Online]. Available: [Accessed: Mar. 22, 2021].



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