Big Data Analytics: Technologies, Applications, and Future Prospects

References

[1] Abidi, H., 2018. Big data analytics in smart city contexts: A two-stage implementation framework. Journal of Urban Technology, 25(4), pp.3–20.

[2] Agarwal, R., Gao, G., DesRoches, C. and Jha, A.K., 2010. Research commentary—the digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), pp.796–809.

[3] Ahmad, A., Paul, A. and Rathore, M.M., 2016. An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication. Neurocomputing, 174, pp.439-453.

[4] Akter, S. and Wamba, S.F., 2016. Big data analytics in e-commerce: a systematic review. Electronic Markets, 26(2), pp.173–187.

[5] Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J., 2016. How to improve firm performance using big data analytics capability and business strategy alignment?. International journal of production economics, 182, pp.113-131.

[6] Al-Ali, A.R., Zualkernan, I.A., Rashid, M., Gupta, R. and Alikarar, M., 2018. A smart home energy management system using IoT and big data analytics approach. IEEE Transactions on Consumer Electronics, 63(4), pp.426-434.

[7] Al-Ali, H.A., Zualkernan, I.A., Aloul, F.I. and Ayyash, M.M., 2019. A smart home design and implementation based on the integration of internet-of-things and big data analytics. IEEE Transactions on Consumer Electronics, 65(2), pp.120–130.

[8] Almirall, E. and Wareham, J., 2011. Living labs: Arbiters of mid- and ground-level innovation. Technology Analysis & Strategic Management, 23(1), pp.87–102.

[9] Appelbaum, D., Kogan, A. and Vasarhelyi, M.A., 2017. Big data and analytics in the modern audit engagement: research needs. Auditing: A Journal of Practice & Theory, 36(4), pp.1–27.

[10] Araujo, F., Freschi, F.M., da Costa, C.A. and Granville, L.Z., 2017. A systematic review of storage data mechanisms in big data environments. Journal of Information and Data Management, 8(3), pp.294–310.

[11] Arya, V., Mittal, S. and Saxena, A., 2019. Role of big data analytics in sustainable e-waste management. Sustainable Production and Consumption, 18, pp.31–41.

[12] Atzori, L., Iera, A. and Morabito, G., 2010. The internet of things: A survey. Computer Networks, 54(15), pp.2787–2805.

[13] Barka, E., Szczepański, M.S., Park, J.H. and Bisaillon, T., 2018. Towards integrating big data and modelling for sustainability assessments. Journal of Cleaner Production, 201, pp.473–488.

[14] Belhadi, A., Zkik, K., Cherrafi, A. and Yusof, S.R.M., 2019. Understanding big data analytics for manufacturing processes: insights from literature review and multiple case studies. Computers & Industrial Engineering, 137, p.106099.

[15] Bendich, P.F., Buck, D.F., Mushlin, R.C. and Coffin, G., 2015. Visual analytics pursuit: combining computational and interactive techniques for healthcare decision support. Journal of Biomedical Informatics, 55, pp.129–142.

[16] Bonthu, M.K. and Bindu, M., 2018. Distributed processing frameworks: a comparative review of Hadoop and Spark. International Journal of Innovative Research and Advanced Studies, 5(3), pp.10–18.

[17] Botta, A., De Donato, W., Persico, V. and Pescapé, A., 2016. Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems, 56, pp.684–700.

[18] Bradlow, E.T., Gangwar, M., Kopalle, P. and Voleti, S., 2017. The role of big data and predictive analytics in retailing. Journal of retailing, 93(1), pp.79-95.

[19] Cao, L., 2017. Data science: A comprehensive overview. ACM Computing Surveys, 50(3), pp.1–42.

[20] Carpenter, C.T., Bernasek, A. and Wickert, V., 2016. Competition in a global economy with especial reference to information asymmetry and financial disclosure. International Business Review, 25(5), pp.1162–1174.

[21] Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, pp.1165-1188.

[22] Chen, H., Chiang, R.H.L. and Storey, V.C., 2012. Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), pp.1165–1188.

[23] Chen, M., Mao, S. and Liu, Y., 2014. Big data: A survey. Mobile Networks and Applications, 19(2), pp.171–209.

[24] Chiu, L.-F. and Chao, K.-M., 2019. Big data driven mobile learning and education: Challenges and opportunities. Computers & Education, 128, pp.365–377.

[25] Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations management. Production and operations management, 27(10), pp.1868-1883.

[26] Chong, D. and Shi, H., 2015. Big data analytics: a literature review. Journal of Management Analytics, 2(3), pp.175-201.

[27] Davenport, T.H. and Harris, J.G., 2007. Competing on Analytics: The New Science of Winning. Harvard Business Review Press.

[28] Davenport, T.H., Barth, P. and Bean, R., 2012. How'big data'is different.

[29] De Mauro, A., Greco, M. and Grimaldi, M., 2016. A formal definition of Big Data: a systematic review of the literature. Information Processing & Management, 52(2), pp.518–526.

[30] Demchenko, Y., De Laat, C. and Membrey, P., 2014. Defining architecture components of the big data ecosystem. International Conference on Collaboration and Internet Computing, pp.104–112.

[31] Dhillon, G. and Backhouse, J., 2000. Information system security management in the new millennium. Communications of the ACM, 43(7), pp.125–128.

[32] Dong, Y., Liu, Y. and Wu, J., 2018. Big data analytics for smart energy systems: A review. Renewable and Sustainable Energy Reviews, 100, pp.276–298.

[33] Dubey, R., Gunasekaran, A. and Papadopoulos, T., 2018. Big data analytics capability in supply chain agility: the moderating effect of organizational flexibility. Management Decision, 56(8), pp.1728–1746.

[34] Dubois, E. and McKelvey, B., 2017. Complexity and organizational learning: An extended triple helix perspective. Technological Forecasting and Social Change, 119, pp.266–278.

[35] Dutta, S. and Bilbao-Osorio, B., 2012. The Global Information Technology Report 2012: Living in a Hyperconnected World. World Economic Forum.

[36] Dwivedi, Y.K., Shareef, M.A., Simintiras, A.C. and Lal, B., 2017. Big Data Analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, 70, pp.356–365.

[37] Eckerson, W.W., 2017. Predictive Analytics: Extending the Value of Your Data. Technics Publications.

[38] Ertem, O., Arian, R., Papa, A. and Getchell, R., 2017. Optimization of renewable energy with big data analytics. IEEE Power and Energy Magazine, 15(3), pp.83–89.

[39] Fan, J., Han, F. and Liu, H., 2014. Challenges of big data analysis. National Science Review, 1(2), pp.293–314.

[40] Fang, X., 2012. Toward an integrated data-driven smart city architecture: A survey. IEEE Access, 4, pp.975–987.

[41] Fay, M. and Kazantsev, N., 2018. When smart gets smarter: How big data analytics creates business value in smart manufacturing. In: International Conference on Information Systems (ICIS) 2018.

[42] Ferraris, A., Santoro, G. and Papa, A., 2018. The impact of big data analytics capabilities on firm performance: evidence from Italy. Technological Forecasting and Social Change, 136, pp.234–248.

[43] Gandomi, A. and Haider, M., 2015. Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp.137–144.

[44] Gartner, 2019. Gartner Hype Cycle for Emerging Technologies. Gartner Research.

[45] Gawer, A. and Cusumano, M.A., 2014. Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), pp.417–433.

[46] Ghasemaghaei, M. and Calic, G., 2019. The role of big data in digital transformation: a case study from the banking sector. Information Systems Journal, 29(3), pp.449–470.

[47] Ghosh, S. and Scott, J., 2019. Big data analytics in marketing: Framework and review. International Journal of Internet Marketing and Advertising, 13(3), pp.209–226.

[48] Gil-Garcia, J.R. and Pardo, T.A., 2005. E-government success factors: Mapping practical tools to theoretical foundations. Government Information Quarterly, 22(2), pp.187–216.

[49] Gilchrist, A., 2016. Industry 4.0: The Industrial Internet of Things. Apress.

[50] Guba, E.G. and Lincoln, Y.S., 1994. Competing paradigms in qualitative research. Handbook of Qualitative Research, pp.105–117.

[51] Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S., 2017. Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, pp.308-317.

[52] Gupta, M., Bhaskar, T. and Iroju, O., 2019. Big data analytics for healthcare systems: a literature review. Health Information Science and Systems, 7(1), p.12.

[53] Gupta, S., Gunasekaran, A. and Ngai, E.W.T., 2017. Increasing firm agility through data analytics: the role of fit. Decision Support Systems, 123, pp.113–124.

[54] Günther, W.A., Rezazade Mehrizi, M.H., Huysman, M. and Feldberg, F., 2017. Debating big data: A research agenda for information systems. Information & Management, 54(1), pp.44–58.

[55] Haddara, M. and Baroudi, B., 2016. Factors affecting the adoption of agile systems in banking sector firms. International Journal of Strategic Information Technology and Applications, 7(1), pp.50–72.

[56] Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Ullah Khan, S., 2015. The rise of “big data” on cloud computing: review and open research issues. Information Systems, 47, pp.98–115.

[57] Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W. and Calantone, R.J., 2014. Common beliefs and reality about partial least squares: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), pp.182–209

[58] Hickman, L., Huang, K., Jones, O., Mitchell, V.-W.J. and Baines, T., 2019. The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 62(3), pp.307–314.

[59] Jabbour, C.J.C., Jabbour, A.B.L.S. and Cruz-Machado, V.A., 2013. Environmental training and environmental performance: a study in the Brazilian context. Journal of Cleaner Production, 47, pp.192–201.

[60] Kankanhalli, A., Hahn, J. and Tan, S., 2016. Role of big data analytics in healthcare. IEEE Journal of Biomedical and Health Informatics, 20(5), pp.1235–1245.

[61] Khan, N., Ali, S., Khan, S.U. and Khan, M.K., 2019. Edge computing for real-time analytics: a survey. IEEE Internet of Things Journal, 6(2), pp.454–467.

[62] Kolajo, T., Mohamed, Z.A. and Hamid, H.A., 2019. Stream processing for sustainable cities: needs and architectures. Environmental Modelling & Software, 115, pp.11–20.

[63] Laney, D., 2001. 3D data management: controlling data volume, velocity, and variety. META Group Research Note.

[64] Laney, D., 2019. The 5 V’s of Big Data analytics. Journal of Big Data Evolution, 3(1), p.11–29.

[65] Ling, X., Rahmani, A.M. and Liljeberg, P., 2019. A big data analytics framework for smart cities. Future Generation Computer Systems, 99, pp.256–276.

[66] Mikalef, P., Boura, M., Lekakos, G. and Krogstie, J., 2019. Configurations of big data analytics for firm performance: an fsQCA approach. AMCIS 2019 Conference Proceedings.

[67] Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S. and Floridi, L., 2016. The ethics of algorithms: mapping the debate. Big Data & Society, 3(2), pp.1–21.

[68] Mišić, V.V. and Perakis, G., 2019. Data analytics in operations management: a review. arXiv preprint arXiv:1905.00556.

[69] Nwanga, M.E., Onwuka, E.N., Aibinu, A.M. and Ubadike, O.C., 2015, March. Impact of big data analytics to Nigerian mobile phone industry. In 2015 International Conference on Industrial Engineering and Operations Management (IEOM) (pp. 1-6). IEEE.

[70] Ogundipe, F., Sampson, E., Bakare, O.I., Oketola, O. and Folorunso, A., 2019. Digital Transformation and its Role in Advancing the Sustainable Development Goals (SDGs). transformation, 19, p.48.

[71] Oluigbo Ikenna, V., Nwokonkwo Obi, C., Ezeh Gloria, N. and Ndukwe Ngoziobasi, G., 2017. Revolutionizing the healthcare industry in nigeria: The role of internet of things and Big Data analytics. International Journal of Scientific Research in Computer Science and Engineering, 5(6), pp.1-12.

[72] Oni, O., Adeshina, Y.T., Iloeje, K.F. and Olatunji, O.O., ARTIFICIAL INTELLIGENCE MODEL FAIRNESS AUDITOR FOR LOAN SYSTEMS. Journal ID, 8993, p.1162.

[73] Oyedokun, O.O., 2019, Green Human Resource Management Practices (GHRM) and Its Effect on Sustainable Competitive Edge in the Nigerian Manufacturing Industry: A Study of Dangote Nigeria Plc. MBA Dissertation, Dublin Business School

[74] Priyono, A., Hidayatno, A., Mahlia, T.M.I., Takashima, R. and Masukujjaman, M., 2018. Application of dynamic knowledge creation model in big data. Technological Forecasting and Social Change, 136, pp.327–337.

[75] Qi, Q. and Tao, F., 2019. Digital twin and big data for smart manufacturing. Journal of Manufacturing Systems, 48, pp.157–169.

[76] Rajan, R. and Perumal, P., 2018. Credit risk evaluation through big data analytics: an Indian perspective. International Journal of Data Science and Analytics, 5(1), pp.45–63.

[77] Rohrbeck, R., 2010. Corporate foresight: towards a maturity model for future orientation. Springer.

[78] Russom, P., 2011. Big data analytics. TDWI best practices report, fourth quarter, 19(4), pp.1-34.

[79] Sareen, S. and Ahuja, S., 2018. Sentiment analysis of tweets collected during crisis. International Journal of Data Mining & Knowledge Management Process, 8(6), pp.29–42.

[80] Sharma, A., Adekunle, B.I., Ogeawuchi, J.C., Abayomi, A.A. And Onifade, O., 2019. IoT-enabled Predictive Maintenance for Mechanical Systems: Innovations in Real-time Monitoring and Operational Excellence.

[81] Shu, H., 2016. Big data analytics: six techniques. Geo-spatial Information Science, 19(2), pp.119-128.

[82] Singh, S.K. and El‑Kassar, A.N., 2019. Big data analytics and sustainable development: empirical evidence from environmental domain. Journal of Cleaner Production, 213, pp.1264–1273.

[83] Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, pp.263-286.

[84] Sætra, H.S., 2017. Ethics and big data: the role of data in society. Technological Forecasting and Social Change, 115, pp.1–6.

[85] Ugodulunwa, C.A., Anikweze, C.M., Francisca, N.O., Eucharia, E.L., Ntasiobi, C.I., Uwaleke, C.C., Duru, C.K. and Eze, A., 2019. Big data and assessment for learning in Nigerian Universities: Prospects and challenges. American-Eurasian Journal Agriculture & Environmental Science, 19(3), pp.224-232.

[86] Uribe, R. and Gómez, J., 2018. Big Data and supply chain optimization: a case of predictive maintenance. International Journal of Operations & Production Management, 38(2), pp.384–401.

[87] Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), pp.626–639.

[88] Wagner, A., Anupa, J., Sarukan, A., Muralidharan, C. and Szekely, P., 2019. Big data analytics framework for urban systems: from smart cities to sustainable development. Future Generation Computer Systems, 100, pp.486–498.

[89] Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2017. How “big data” can make big impact: findings from a systematic review. International Journal of Production Economics, 165, pp.234–246.

[90] Wang, H., Li, X. and Zou, Y., 2019. Federated learning for privacy‑preserved big data analytics: a survey. IEEE Network, 34(1), pp.194–204.

[91] Wang, Y. and Hajli, N., 2017. Exploring the path to big data analytics success: antecedents and impact on decision outcomes. Journal of Business Research, 70, pp.306–313.

[92] Xiang, Z., Schwartz, Z., Gerdes Jr, J.H. and Uysal, M., 2015. What can big data and text analytics tell us about hotel guest experience and satisfaction?. International journal of hospitality management, 44, pp.120-130.

[93] Xing, B., Su, H., Zheng, X., Wang, H. and Yang, Y., 2018. Security and privacy in IoT‑based big data analytics: a survey. Journal of Platforms for Data and Artificial Intelligence, 2(4), pp.27–38.

[94] Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S. and Stoica, I., 2016. Apache Spark: cluster computing with working sets. Communications of the ACM, 59(11), pp.56–65.

[95] Zhang, L. and Zhu, P., 2019. Explainable AI for predictive analytics: an interpretability perspective. In Proceedings of the IEEE International Conference on Data Science, pp.112–121.

[96] Zhang, X. and Wei, Z., 2018. Ensemble learning methods for predictive modeling in finance. Journal of Financial Data Science, 1(2), pp.71–85.

[97] Zheng, Y.Z., Ying, S., Hu, J.L. and Zeng, D.N., 2019. Real‑time recommendation systems in e‑commerce driven by hybrid analytics. Expert Systems with Applications, 127, pp.54–65.

[98] Zhou, K., Fu, C. and Yang, S., 2016. Big data driven smart energy management: From big data to big insights. Renewable and sustainable energy reviews, 56, pp.215-225.

[99] Zikopoulos, P., 2016. The evolution of Hadoop‑based analytics engine: from MapReduce to Spark. Journal of Big Data Technologies, 1(1), pp.1–13.

[100] Zikopoulos, P., 2017. Streaming analytics: transforming raw data into real‑time intelligence. IEEE Computer, 50(11), pp.9–11.

[101] Zikopoulos, P., Eaton, C., DeRoos, D., Deutsch, T. and Lapis, G., 2012. Understanding Big Data: analytics for enterprise class Hadoop and streaming data. McGraw‑Hill Education.

[102] Zott, C. and Amit, R., 2010. Business model design: an activity system perspective. Long Range Planning, 43(2–3), pp.216–226.

@article{1709354,
      author = {Chioma Susan Nwaimo, Oluchukwu Modesta Oluoha, Oyewale Oyedokun},
      title = {Big Data Analytics: Technologies, Applications, and Future Prospects},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {11},
      pages = {411-438},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709354.pdf},
      abstract = {Big Data Analytics (BDA) has emerged as a transformative force across industries, reshaping how organizations generate value from the voluminous, fast-moving, and diverse data produced daily. The evolution of data-centric technologies since the early 2010s has catalyzed a paradigm shift in how insights are derived, with 2019 marking a critical point of convergence between maturing analytics frameworks and accelerated adoption across sectors. This journal explores the core technologies underpinning BDA, such as distributed computing, cloud infrastructures, and advanced machine learning algorithms, detailing how they collectively facilitate the extraction of meaningful patterns and predictions from large datasets. It evaluates the interplay between structured and unstructured data environments, storage mechanisms such as Hadoop Distributed File System (HDFS), and processing engines like Apache Spark, in driving scalable and real-time analytics capabilities. Furthermore, the paper assesses real-world applications of BDA across domains such as healthcare, finance, smart cities, e-commerce, and cybersecurity. It outlines how data analytics has enhanced precision in clinical decision-making, improved fraud detection in financial services, and enabled intelligent systems in urban governance. By integrating case examples and empirical studies, the research highlights both the accomplishments and limitations of current BDA implementations. The role of data governance, ethical considerations, and regulatory frameworks are also considered, recognizing the importance of responsible data practices in a globally connected world. Looking toward the future, the journal identifies emerging frontiers such as quantum analytics, edge computing, and federated learning, which promise to redefine the limits of big data processing and insight generation. The work aims to provide a comprehensive scholarly foundation for understanding the technological, practical, and theoretical dimensions of big data analytics as of 2019, while laying the groundwork for future research in the evolving data ecosystem.},
      keywords = {Big Data Analytics, Machine Learning, Distributed Computing, Apache Spark, Cloud Infrastructure, Data Governance, Real-Time Analytics, Emerging Technologies, 2019 Trends.},
      month = {May},
  }