Influence of Data-Driven Product Metrics on Strategic Decision Making in Enterprise Product Management

Authors

Karl Popper Judith
Enterprise Product Strategy & Analytics Manager, United States

Keywords:

Data-driven decision-making, Product metrics, Enterprise product management, Strate-gic alignment, Predictive analytics, Big data, Performance measurement, Business intel-ligence

Synopsis

In the era of digital transformation, data-driven product metrics have emerged as a cornerstone of strategic decision-making in enterprise product management. This paper explores how quantitative and qualitative product performance indicators influence prioritization, resource allocation, innovation, and long-term business competitiveness. The study highlights the evolution from traditional performance measures to real-time data-driven dashboards, predictive analytics, and machine learning-based insights. Furthermore, the research proposes an integrated architecture that connects product analytics to enterprise strategy, illustrated through diagrams, process flows, and infographics. Findings emphasize that organizations leveraging robust product metrics enhance alignment with customer needs, accelerate market responsiveness, and optimize resource efficiency.

References

[1] Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management. International Journal of Production Research, 45(12), 2819-2840.

[2] Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. O. (2011). Integrating energy efficiency into production management. Journal of Cleaner Production, 19(6-7), 667-679.

[3] Anugula Sethupathy, U.K. (2020). Cloud-Native Architectures for Real-Time Retail Inventory and Analytics Platforms. International Journal of Novel Research and Development, 5(6), 339–355. https://doi.org/10.56975/ijnrd.v5i6.309063

[4] Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.

[5] Wang, G., Gunasekaran, A., & Ngai, E. W. T. (2016). Big data analytics in logistics and supply chain management. International Journal of Production Economics, 176, 98–110.

[6] Vassakis, K., Petrakis, E., & Kopanakis, I. (2017). Big data analytics: applications, prospects and challenges. Springer.

[7] Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.

[8] Miragliotta, G., Sianesi, A., Convertini, E., & Distante, R. (2018). Data driven management in Industry 4.0: a method to measure Data Productivity. IFAC-PapersOnLine, 51(11), 19–24.

[9] Anugula Sethupathy, U.K. (2021). Securing cloud-based streaming data platforms best practices and frameworks. International Research Journal of Modernization in Engineering Technology and Science, 3(11), 1516–1526. https://doi.org/10.56726/IRJMETS17179

[10] Hannila, H. (2019). Towards data-driven decision-making in product portfolio management. University of Oulu.

[11] Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making. Government Information Quarterly, 37(3), 101-466.

[12] Sarker, I. H. (2021). Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), 377.

[13] Mgbame, A. C., & Akpe, O. E. E. (2022). Developing low-cost dashboards for business process optimization in SMEs. Management Research Review.

[14] Nwulu, E. O., Elete, T. Y., & Erhueh, O. V. (2022). Integrative project and asset management strategies to maximize gas production: A review of best practices. World Journal of Management.

[15] Harkonen, J., Kuula, S., & Hannila, H. (2022). Digitalisation of a company decision-making system: fact-based product portfolio management. Journal of Decision Systems, 31(4), 321-338.

[16] Chavez, R., Yu, W., Jacobs, M. A., & Feng, M. (2023). Data-driven supply chains, manufacturing capability and customer satisfaction. Production Planning & Control, 34(3), 187-201.

Published

December 4, 2024