Research Paper

Data-Driven Product Strategy:Evaluating the Impact of Data Analytics on Product Success

Authors:Sandeep Mahajan
Volume:Volume 13, Issue II
Published:July-Dec, 2025
Pages:867-879

ABSTRACT

In today’s data-driven business environment, analytics has emerged as a critical enabler of product success, yet organizations often struggle to convert vast data into actionable strategic insights. This study addresses this challenge by developing and qualitatively validating an integrative framework that explicates how analytics capabilities influence product strategy across the product lifecycle. Using a systematic literature review and four semi-structured expert interviews, the study captures perspectives from both academic and industry practitioners, ensuring methodological rigor and practitioner relevance. The resulting framework comprises foundational technological analytics capabilities, organizational readiness and cross-functional integration, ethical governance, and performance-oriented mechanisms. Findings demonstrate that analytics enhances iterative product refinement, predictive modeling, behavioral telemetry, adaptive experimentation, and AI-enabled simulation, while complementing human judgment, leadership prioritization, and cross-functional collaboration. Experts emphasized leading indicators, North-Star metrics, and continuous learning as mechanisms translating data into measurable improvements in adoption, engagement, and financial performance. Ethical governance emerged as essential for maintaining trust, regulatory compliance, and sustainable analytics practices. Practically, the framework offers organizations a structured pathway to embed analytics systematically into product development, balancing empirical evidence with strategic creativity. The study contributes theoretically by advancing understanding of the socio-technical dynamics of data-driven product innovation and provides actionable guidance for firms seeking to achieve measurable product and market outcomes.

KEYWORDS

Data analytics; Data-driven product strategy; Data analytics capabilities; Product performance; Strategic decision-making; Data-driven innovation; Organizational alignment.

REFERENCES

  • [1] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  • [2] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
  • [3] Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
  • [4] Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning (Updated ed.). Harvard Business Review Press.
  • [5] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... Vayena, E. (2018). AI4People – An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  • [6] Floridi, L., Taddeo, M., & Vaglianese, V. (2018). Artificial intelligence and ethics: An urgent issue and recommendations. Philosophy & Technology, 31(1), 13–20. https://doi.org/10.1007/s13347-017-0288-5
  • [7] Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.009
  • [8] Janssen, M., van der Voort, H., & Wahyudi, A. (2017). A framework for analyzing governance in the big data era. Public Management Review, 20(8), 1111–1134. https://doi.org/10.1080/14719037.2017.1293145
  • [9] Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–346. https://doi.org/10.1016/j.jbusres.2016.08.010
  • [10] Kallio, H., Pietilä, A.-M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031
  • [11] LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–31.
  • [12] Lee, E., Kim, S., & Park, J. (2019). Leadership and data analytics integration in product development: Balancing innovation and operationalization. Journal of Product Innovation Management, 36(3), 287–302. https://doi.org/10.1111/jpim.12445
  • [13] Martin, K., & Murphy, P. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4
  • [14] Martin, G., Smith, L., & White, R. (2020). Role of leadership in facilitating data-driven innovation in new product development. Journal of Business Research, 112, 1–11. https://doi.org/10.1016/j.jbusres.2019.10.020
  • [15] McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
  • [16] Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2019). Investigating the effects of big data analytics capabilities on firm performance: The mediating role of dynamic capabilities. Information & Management, 57(8), 103167. https://doi.org/10.1016/j.im.2019.103167
  • [17] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
  • [18] Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work, and organization. The Academy of Management Annals, 2(1), 433–474. https://doi.org/10.1080/19416520802211644
  • [19] Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis. Administration and Policy in Mental Health, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y
  • [20] Provost, F., & Fawcett, T. (2013). Data science for business. O’Reilly Media.
  • [21] Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review & BCG.
  • [22] Shankar, V. (2018). How artificial intelligence is reshaping market research. Journal of the Academy of Marketing Science, 46(1), 8–27. https://doi.org/10.1007/s11747-017-0545-1
  • [23] Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
  • [24] Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2017). How "big data" can make big impact. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.035
  • [25] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
  • [26] Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099
  • [27] Zhao, X., & Anand, G. (2021). Data analytics and intuition: Reconciling human judgment and analytics in strategic decision making. Decision Sciences, 52(2), 339–368. https://doi.org/10.1111/deci.12438

Citation Info

Journal: JIMS8I IJICCT

Volume: 13, Issue II

Year: 2025

Pages: 867-879

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