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| Version | Summary | Created by | Modification | Content Size | Created at | Operation |
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| 1 | Harry Ph. Sophocleous | -- | 682 | 2025-08-15 07:55:28 | | | |
| 2 | Perry Fu | Meta information modification | 682 | 2025-08-15 07:59:35 | | |
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Sophocleous, H.P. Harnessing Big Data and Artificial Intelligence for Entrepreneurial Innovation: Opportunities, Challenges, and Strategic Implications. Encyclopedia. Available online: https://encyclopedia.pub/entry/58760 (accessed on 05 December 2025).
Sophocleous HP. Harnessing Big Data and Artificial Intelligence for Entrepreneurial Innovation: Opportunities, Challenges, and Strategic Implications. Encyclopedia. Available at: https://encyclopedia.pub/entry/58760. Accessed December 05, 2025.
Sophocleous, Harry P.. "Harnessing Big Data and Artificial Intelligence for Entrepreneurial Innovation: Opportunities, Challenges, and Strategic Implications" Encyclopedia, https://encyclopedia.pub/entry/58760 (accessed December 05, 2025).
Sophocleous, H.P. (2025, August 15). Harnessing Big Data and Artificial Intelligence for Entrepreneurial Innovation: Opportunities, Challenges, and Strategic Implications. In Encyclopedia. https://encyclopedia.pub/entry/58760
Sophocleous, Harry P.. "Harnessing Big Data and Artificial Intelligence for Entrepreneurial Innovation: Opportunities, Challenges, and Strategic Implications." Encyclopedia. Web. 15 August, 2025.
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Big Data and Artificial Intelligence (AI) are transforming the entrepreneurial landscape by reshaping how start-ups and established firms ideate, operate, and compete. This entry explores the intersection of these technologies with entrepreneurship, highlighting how they enhance decision-making, customer insights, and operational efficiency. While the benefits are substantial, integrating these technologies presents challenges related to data ethics, privacy, algorithmic bias, sustainability, accessibility, and implementation complexity. Through analysis of current literature, critical perspectives, and illustrative case studies, this entry emphasises the need for strategic alignment, ethical considerations, and adaptive organisational cultures. The entry contributes to existing research by synthesising trends, critiquing over-optimistic narratives, and suggesting a conceptual framework for future empirical inquiry.
artificial intelligence
big data
entrepreneurship
innovation
scalability
ethics
digital transformation
The integration of Big Data and Artificial Intelligence (AI) is increasingly shaping entrepreneurial strategies, particularly in the post-pandemic digital economy. Entrepreneurs who adopted data-driven technologies demonstrated resilience, adaptability, and competitiveness. For instance, tech-based start-ups and e-commerce entrepreneurs used AI-powered analytics to pivot quickly and respond to dynamic market conditions. However, despite anecdotal success stories, academic inquiry into the strategic and operational dimensions of Big Data and AI in entrepreneurship remains fragmented. This entry aims to bridge this gap by examining the strategic role of these technologies across the entrepreneurial process—from opportunity recognition and product development to customer engagement and scaling. The motivation stems from both the growing ubiquity of these technologies and the need for more nuanced understanding of their implications for innovation, ethics, and sustainable business models. By drawing upon multidisciplinary literature and case-based examples, the author seeks to inform both scholars and practitioners.
These technologies work together to give entrepreneurs the power to spot new opportunities, make operations more efficient, improve customer engagement, and grow their businesses more easily than ever before [1]. Big data and AI serve as enablers of agility and innovation, which are critical attributes for start-ups operating under resource constraints and high uncertainty. In particular, they help to level the playing field by providing small- and medium-sized enterprises (SMEs) with access to sophisticated capabilities that were once the exclusive domain of large corporations.
In the wake of the pandemic, the need for digital strength and rapid change has made these technologies more popular with businesses [2]. The COVID-19 crisis highlighted the importance of flexible, tech-enabled business models that can respond swiftly to disruptions. Entrepreneurs who leveraged big data and AI during the pandemic were better equipped to pivot, meet shifting consumer needs, and optimise digital channels. This trend continues today, as AI- and data-driven approaches become foundational to entrepreneurial success in an increasingly digital and interconnected global economy.
As illustrated in Figure 1 [3][4], the perceived impact of big data and AI is evident across five critical dimensions of start-up development: decision-making, innovation, operational efficiency, customer personalisation, and global scalability. Among these, the dimensions with the greatest perceived impact are operational efficiency and decision-making, which is no surprise given that they are the most important factors in data-driven entrepreneurship. The high scores across all the categories highlight the wide-ranging usefulness of these technologies. Start-ups use AI and big data not only to optimise internal processes, but also to innovate and scale up in increasingly competitive markets. The broad applicability of these tools makes them essential for survival and sustainable growth in a digitally driven business environment.
Literature Review
Recent research has identified Big Data as a catalyst for opportunity recognition [5], while AI supports predictive decision-making and automation [6]. Start-ups increasingly leverage machine learning algorithms to refine customer segmentation, automate processes, and forecast trends. At the same time, scholars caution against overreliance on these technologies, highlighting risks such as algorithmic opacity and surveillance capitalism [7].
Moreover, this entry adopts a conceptual and exploratory approach, grounded in secondary research and content analysis. The author reviews peer-reviewed articles, industry reports, and case studies published between 2015 and 2024. While not empirical in nature, the approach enables synthesis across disciplines and sectors, offering a comprehensive perspective. The entry’s scope focuses primarily on early-stage start-ups and digital entrepreneurs operating in tech-intensive sectors.
References
- Marr, B. Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things; Kogan Page Publishers: London, UK, 2018.
- Ghosh, R. How AI and Big Data Are Powering Post-Pandemic Entrepreneurship; Forbes: Jersey City, NJ, USA, 2022; Available online: https://www.forbes.com (accessed on 10 June 2025).
- Marr, B. Artificial Intelligence in Practice: How 50 Companies Are Already Using AI to Drive Value and Productivity; Wiley: Hoboken, NJ, USA, 2020.
- Prior, D.D.; Buttle, F.; Maklan, S. Customer Relationship Management: Concepts, Applications and Technologies, 5th ed.; Routledge: London, UK, 2024.
- George, G.; Haas, M.R.; Pentland, A. Big Data and Management. Acad. Manag. J. 2016, 59, 321–330.
- Cockburn, I.M.; Henderson, R.; Stern, S. The Impact of Artificial Intelligence on Innovation; NBER Working Paper No. 24449; National Bureau of Economic Research: Cambridge, MA, USA, 2018.
- Zuboff, S. The Age of Surveillance Capitalism; Public Affairs: New York, NY, USA, 2019.
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