IJSSER

Title: USE OF BIG DATA AND AI TOOLS TO EVALUATE AND ASSIST STARTUPS
Authors: Athanasios Davalas
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Athanasios Davalas Aegean University
MLA 8 Davalas, Athanasios. "USE OF BIG DATA AND AI TOOLS TO EVALUATE AND ASSIST STARTUPS." Int. j. of Social Science and Economic Research, vol. 5, no. 11, Nov. 2020, pp. 3615-3624, doi:10.46609/IJSSER.2020.v05i11.019. Accessed Nov. 2020. APA 6 Davalas, A. (2020, November). USE OF BIG DATA AND AI TOOLS TO EVALUATE AND ASSIST STARTUPS. Int. j. of Social Science and Economic Research, 5(11), 3615-3624. doi:10.46609/IJSSER.2020.v05i11.019 Chicago Davalas, Athanasios. "USE OF BIG DATA AND AI TOOLS TO EVALUATE AND ASSIST STARTUPS." Int. j. of Social Science and Economic Research 5, no. 11 (November 2020), 3615-3624. Accessed November, 2020. doi:10.46609/IJSSER.2020.v05i11.019.
References [1]. Bredt, S. (2019). Artificial Intelligence (AI) in the Financial Sector–Potential and Public Strategies. Frontiers in Artificial Intelligence, 2, 16. https://www.frontiersin.org/articles/10.3389/frai.2019.00016/full?utm_source=S-TWT&utm_medium=SNET&utm_campaign=ECO_FRAI_FDATA_XXXXXXXX_auto-dlvrit [2]. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188. https://pdfs.semanticscholar.org/f5fe/b79e04b2e7b61d17a6df79a44faf358e60cd.pdf%3E. [3]. Garbuio, M., & Lin, N. (2019). Artificial intelligence as a growth engine for health care startup: emerging business models. California Management Review, 61(2), 59-83. https://pdfs.semanticscholar.org/78ce/7d0cfecf37c0e079afb4150b097e11c61109.pdf [4]. Hilbig, R., Etsiwah, B., & Hecht, S. (2018, December). Berlin Start-ups–The Rise of Data-Driven Business Models. In ISPIM Innovation Symposium (pp. 1-19). The International Society for Professional Innovation Management (ISPIM). https://www.researchgate.net/profile/Romy_Hilbig/publication/329529109_Berlin_Start-ups_-_The_Rise_of_Data-Driven_Business_Models/links/5c0e43f34585157ac1b73648/Berlin-Start-ups-The-Rise-of-Data-Driven-Business-Models.pdf [5]. Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44. https://www.mdpi.com/2199-8531/5/3/44/pdf [6]. Lin, J., Oentaryo, R., Lim, E. P., Vu, C., Vu, A., &Kwee, A. (2016, July). Where is the goldmine? Finding promising business locations through Facebook data analytics. In Proceedings of the 27th ACM Conference on Hypertext and Social Media (pp. 93-102). https://arxiv.org/pdf/1609.02839 [7]. Mitomo, H. (2017). Data network effects: Implications for data business. https://www.econstor.eu/bitstream/10419/169484/1/Mitomo.pdf [8]. Nick, C. (2019). How to Use Google Trends for Keyword Research: 7 Effective Ways. https://ahrefs.com/blog/how-to-use-google-trends-for-keyword-research/ [9]. Obschonka, M., &Audretsch, D. B. (2019). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 1-11. https://arxiv.org/pdf/1906.00553 [10]. Reim, W., Åström, J., & Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. AI, 1(2), 180-191. https://www.mdpi.com/2673-2688/1/2/11/pdf [11]. Saura, J. R., Palos-Sanchez, P., &Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability, 11(3), 917. https://www.mdpi.com/2071-105
Abstract: Startup businesses are striving from the very beginning to understand their customers’ needs and gain competitive advantage over their competitors. As they are running out of budget, time and other resources sooner than corporates they need to rely to disrupting technologies such as Artificial Intelligence (AI) and Big Data to progress to a next round. In this report, we present those dimensions of these technologies which empower startups on understanding and modelling customers’ behavior and customers’ needs. These are presented from the point of view of prospects and challenges considering, in one hand, the benefits of bringing data analytics into production or service delivery, but also considering the challenges for the startups to prove that they can deliver the right products or services. The confluence of the AI and Big Data technologies transcends all the sectors of even a startup business (manufacturing, information technology, finance, technical operations, customer services, logistics, etc). However, the challenge of a startup is to harvest the best possible business intelligence and implement targeted and earlier innovations.

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