Home / Current Issue / Paper 1714429
Data-Driven Product Strategy and Business Analytics Research
Subject area: Management and Commerce · Area of research: Business Analytics and Product Strategy
DOI: https://doi.org/10.64388/IREV8I5-1714429
Abstract
The paper discussed the application of data-driven product strategy and business analytics within contemporary product development and innovation. This paper, through an integrative review of academic literature and industry framework, integrates knowledge on the usage of data-based product planning, the product lifecycle management supported by big data, and the strategic decision-making based on analytics. Findings revealed that data radically changed the way that organizations felt the opportunities in the market, their vision of products, and maximized their offerings. This paper also highlighted key challenges, such as data fragmentation, organizational resistance, and model reliability limitations that restricted the complete implementation of analytics-based models. Hence, an integrative framework was created to show how analytics can be integrated into the visioning, planning, and ongoing optimization processes. Finally, this paper concluded that data-driven product strategy is an important organizational capability that enables more adaptive, customer-centric, and competitive product outcomes.
Keywords
Product Strategy; Business Analytics; Product Lifecycle Management; Predictive Analytics; Cyber-Physical Systems; Big Data; Digital Transformation; Decision Support Systems.
References
[1] Broy, M. (Ed.). (2010). Cyber-physical systems: Innovation durch softwareintensive eingebettete systeme. Springer-Verlag.
[2] Cardellini, V., Lo Presti, F., Nardelli, M., & Russo Russo, G. (2022). Runtime adaptation of data stream processing systems: The state of the art. ACM Computing Surveys, 54(11s), Article 237, 1-36. https://doi.org/10.1145/3514496
[3] Chen, J., Chen, Y., & Wang, Q. (2020). Machine learning and big data analytics in business decision-making: A review of applications and future trends. Journal of Business Research, 115, 326-339. https://doi.org/10.1016/j.jbusres.2019.12.005
[4] Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), Article 13. https://doi.org/10.1145/2843948
[5] Harvard Business Review Analytic Services. (2018). The evolution of decision making: How leading organizations are using data to achieve superior performance. Harvard Business Publishing.
[6] Holler, M., Stark, R., & Kassner, L. (2016). Virtual product creation: Foundations, tools, and processes. Springer.
[7] Kassner, L., Triep, M., & Stark, R. (2015). Lifecycle data integration in engineering. Procedia CIRP, 36, 205-210. https://doi.org/10.1016/j.procir.2015.01.068
[8] Kühn, P., Keller, R., & Wöhner, T. (2018). Analytics Canvas: A framework for data-driven decision support. Journal of Decision Systems, 27(1), 54-72. https://doi.org/10.1080/12460125.2018.1468697
[9] Massmann, M., Kersten, W., & Schröder, M. (2020). A layered model for usage data-driven product planning. Procedia CIRP, 91, 760-765. https://doi.org/10.1016/j.procir.2020.02.229
[10] Meyer, L., Schwenke, F., & Stark, R. (2021). Usage data as a driver of product innovation: Challenges and success factors. Advanced Engineering Informatics, 48, Article 101294. https://doi.org/10.1016/j.aei.2021.101294
[11] Nguyen, T. T., Hui, P. M., Harper, F. M., Terveen, L., & Konstan, J. A. (2014). Exploring the filter bubble: The effect of using recommender systems on content diversity. In Proceedings of the 23rd International Conference on World Wide Web (pp. 677-686). ACM. https://doi.org/10.1145/2566486.2568012
[12] Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
[13] Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5, 13-22.
[14] Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
[15] Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006
[16] Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007
[17] Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404-428. https://doi.org/10.1177/1534484316671606
[18] Tyagi, P. (2021). Diagnostic, descriptive, predictive and prescriptive analytics with geospatial data. International Journal of Computer Trends and Technology, 69(1), 18-22. https://doi.org/10.14445/22312803/IJCTT-V69I1P104
[19] Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from big data: A multiple-case study analysis of provider companies. Technovation, 84-85, 21-36. https://doi.org/10.1016/j.technovation.2018.07.004
[20] Wamba-Taguimdje, A. B., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Artificial intelligence in business: A literature review and research agenda. Journal of Business Research, 113, 283-297. https://doi.org/10.1016/j.jbusres.2019.10.001
[21] Zhang, Y., Qu, T., Ho, O. M. L., Chen, X., Guo, C. C., & Ma, H. (2017). IoT-enabled smart workshop for manufacturing: A case study. Robotics and Computer-Integrated Manufacturing, 49, 171-183. https://doi.org/10.1016/j.rcim.2017.08.003
How to cite this paper
@article{1714429,
author = {Kehinde Arigbolo},
title = {Data-Driven Product Strategy and Business Analytics Research},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {1499-1504},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714429.pdf},
abstract = {The paper discussed the application of data-driven product strategy and business analytics within contemporary product development and innovation. This paper, through an integrative review of academic literature and industry framework, integrates knowledge on the usage of data-based product planning, the product lifecycle management supported by big data, and the strategic decision-making based on analytics. Findings revealed that data radically changed the way that organizations felt the opportunities in the market, their vision of products, and maximized their offerings. This paper also highlighted key challenges, such as data fragmentation, organizational resistance, and model reliability limitations that restricted the complete implementation of analytics-based models. Hence, an integrative framework was created to show how analytics can be integrated into the visioning, planning, and ongoing optimization processes. Finally, this paper concluded that data-driven product strategy is an important organizational capability that enables more adaptive, customer-centric, and competitive product outcomes.},
keywords = {Product Strategy; Business Analytics; Product Lifecycle Management; Predictive Analytics; Cyber-Physical Systems; Big Data; Digital Transformation; Decision Support Systems.},
month = {November},
doi = {https://doi.org/10.64388/IREV8I5-1714429}
}