From Data Lakes to Business Value: A Capability-Based Framework for Analytics Maturity

Abstract

Data lakes are becoming more common in organizations, and while plenty of data is available, companies have a hard time convincing themselves that they are creating business value from that data. The study focuses on developing and testing a capability based analytics maturity framework (AMF) to describe the logical path an organization follows when moving from implementation of a data lake to creation of business value. Based on the resource-based view (RBV), knowledge-based view (KBV) and socio-technical systems theory, we suggest a four-dimensional framework consisting of basic capabilities (data management, technology, culture, and analytics), maturity stages (largesse, transformation, emergence, consolidation, and dissolution), and boundary conditions (industry type, organizational size, and digital culture). Structural equation modelling (SEM) was used for analyzing the survey data received from 105 organizations representing four industry sectors. Results for each of six hypotheses are summarized as follows: A data lake intensity positively influences capability development (β = 0.42, p < 0.001), capability has complementary effects on analytics maturity (β = 0.18, p < 0.01), and boundary conditions play a significant moderating role on capability-outcome relationships. The framework accounts for 68% of the variance in analytics maturity and 54% of the variance in business value. Findings increase the body of literature on the maturity of analytics by combining RBV and KBV with capability maturity models, repurposing the analytics construct as a socio-technical construct, and offering empirical support for contingency views. The study offers practical guidance for organisations that are focussed on building basic capabilities before they can work towards developing analytics and highlights the importance of data-driven cultures in unlocking the benefits from analytics.

REFERENCES

[1] Menukhin, O., Mandungu, C., Shahgholian, A., & Mehandjiev, N. D. (2021). Knowledge-and Resource-based Views Driving the Development of the Analytics Maturity Framework. In Academy of Management Proceedings (Vol. 2021, No. 1, p. 15826). Briarcliff Manor, NY 10510: Academy of Management.

[2] Devalla, S. (2021). Optimizing performance, stability, and cost efficiency in large-scale enterprise migrations to AWS: A data-driven approach. International Journal of Computer Engineering and Technology (IJCET), 12(1), 137-159.

[3] J. S. Armstrong and T. S. Overton, "Estimating nonresponse bias in mail surveys," Journal of Marketing Research, vol. 14, no. 3, pp. 396–402, 1977. doi: 10.1177/002224377701400320.

[4] AdaptiveUS, "The ultimate guide to business capability analysis," AdaptiveUS Blog, Nov. 18, 2017. [Online]. Available: https://www.adaptiveus.com/blog/technique/business-capability-analysis/

[5] Aaker, D. A. (1989). Managing assets and skills: The key to a sustainable competitive advantage. California management review, 31(2), 91-106.

[6] J. Cohen, "A power primer," Psychological Bulletin, vol. 112, no. 1, pp. 155–159, 1992. doi: 10.1037/0033-2909.112.1.155.

[7] T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning. Boston, MA, USA: Harvard Business Review Press, 2007.

[8] White, S. (2006). Higher education and learning technologies an organisational perspective (Doctoral dissertation, University of Southampton).

[9] Omerzel, D. G., & Gulev, R. E. (2011). Knowledge Resources and Competitive Advantage. Managing Global Transitions: International Research Journal, 9(4).

[10] L. J. Groot, K.-J. Kan, and S. Jak, "Checking the inventory: Illustrating different methods for individual participant data meta-analytic structural equation modeling," Research Synthesis Methods, 2024. doi: 10.1002/jrsm.1735.

[11] Idkhan, A. M., & Idris, M. M. R. (2023). The impact of user satisfaction in the use of e-learning systems in higher education: A CB-SEM approach. International Journal of Environment, Engineering and Education, 5(3), 100-110.

[12] Khaled, A., Sabir, M., Qureshi, R., Caruso, C. M., Guarrasi, V., Xiang, S., & Zhou, S. K. (2025). Leveraging MIMIC datasets for better digital health: a review on open problems, progress highlights, and future promises. arXiv preprint arXiv:2506.12808.

[13] Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114-124.

[14] Orlikowski, W. J., & Scott, S. V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. Academy of Management annals, 2(1), 433-474.

[15] Nambiar, A., & Mundra, D. (2022). An overview of data warehouse and data lake in modern enterprise data management. Big data and cognitive computing, 6(4), 132.

[16] Ali, I. M. (2024). A guide for positivist research paradigm: From philosophy to methodology. Idealogy Journal, 9(2).

[17] Pfeffer, J., & Veiga, J. F. (1999). Putting people first for organizational success. Academy of management perspectives, 13(2), 37-48.

[18] ScienceDirect, "Partial least squares structural equation modeling (PLS-SEM) in research," 2022. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S2772766122000246

[19] Al-Sai, Z. A., Husin, M. H., Syed-Mohamad, S. M., Abdullah, R., Zitar, R. A., Abualigah, L., & Gandomi, A. H. (2022). Big data maturity assessment models: a systematic literature review. Big Data and Cognitive Computing, 7(1), 2.

[20] Ehrlinger, L., Werth, B., & Wöß, W. (2018). Automated continuous data quality measurement with QuaIIe. Int. J. Adv. Softw, 11(3), 400-417.

[21] Eindhoven University of Technology, "A model for defining maturity levels for organizational capabilities," May 30, 2024. [Online]. Available: https://research.tue.nl/en/publications/understanding-capability-progression-a-model-for-defining-maturit/

[22] Khatibian, N., Hasan gholoi pour, T., & Abedi Jafari, H. (2010). Measurement of knowledge management maturity level within organizations. Business strategy series, 11(1), 54-70.

[23] Langley, A., & Truax, J. (1994). A process study of new technology adoption in smaller manufacturing firms. Journal of Management Studies, 31(5), 619-652.

[24] M. Asif, M. Abid, and A. Riaz, “Psychological drivers of investment decision making: A multi bias analysis of an emerging market’s retail investors,” Contemporary Journal of Social Science Review, vol. 4, no. 2, pp. 677–688, 2026, doi: 10.63878/cjssr.v4i2.2608.

[25] Beus, J. M., Taylor, E. C., & Solomon, S. J. (2021). Climate-context congruence: Examining context as a boundary condition for climate-performance relationships. Journal of Applied Psychology, 106(9), 1332.

[26] Dewett, T., & Jones, G. R. (2001). The role of information technology in the organization: a review, model, and assessment. Journal of management, 27(3), 313-346.