Big Data Analytics in Information Systems Research: Current Landscape and Future Prospects Focus: Data science, cloud platforms, real-time analytics in IS

Аннотация

The emergence of information systems (IS) research and big data analytics (BDA) represents a paradigm shift in the world of organization choice that is shifting the field to one that is dominated by big data and technologies that enable it. This paper offers an in- depth discussion of the present situation and future of the BDA in IS, especially the revolutionary opportunities of data science, cloud-based computing environments, and real-time analytics. The study will undergo a mixed- method research design as it combines the approaches of bibliometric analysis and qualitative synthesis of 1,136 peer-reviewed articles published between 2013 The American Journal of Engineering and Technology 177 https://www.theamericanjournals.com/index.php/tajet and 2024 on the key academic databases. Quantitative currently one of the most important sources of patterns suggest a steep increase in the research of IS innovation in both IS research and practice. with machine learning, predictive modelling and cloud- The history of the development of IS in relation to big based analytics architecture. Sectoral analysis shows data can hardly be discussed without mentioning the that there are extensive and intensive application of the emergence of cloud platforms offering elastic, scalable, sector across diversified domains such as healthcare, inexpensive environments to operate with huge finance, manufacturing and public administration where volumes of data. There are no longer on-premise the real-time consideration of analytics has become very limitations facing the enterprises since they are crucial in providing responsiveness and agility. The nowadays using services like AWS, Microsoft Azure, and thematic forecasting plays up on areas of future Google Cloud to ingest, store, process and analyze data expansion such as explainable AI, federated learning and in a manner that enables flexibility and speed. Such quantum-enhanced analytics all of which are closely cloud architectures are helping in facilitating real-time associated with the continuing development of the analytics-the systems that could take advantage of cloud infrastructure and advanced data science streaming data to carry out decision making in real time. procedures and methods. Even with methodological Whether developed to screen live data in the financial development, there remains a problem of algorithmic services to predict diagnosing in the medical field or transparency, data cross-sector interoperability and adaptive logistics in the supply chain, the ability to governance. This paper presents a prospective research operate on live data is transforming the potential of IS agenda to the IS field and future practitioners, a point to strategically. This is not simply a technical change; this is consider in the future is an interdisciplinary cooperation, an organizationally profound change which touches an ethically responsible development, and a strategic governance models, decision hierarchies and user incorporation of scalable analytics platforms. The expectations. originality of the studies in the paper is that it is In this regard, data science has found its place as an conducted on an empirical basis and has a specific analytical drive towards contemporary IS. As opposed to narrow focus of the technology enabling the next traditional business intelligence strategies which were generation of data-dependent information systems. based on descriptive analytics and trend analyses on Keywords: Big Data Analytics, Information Systems, past experiences, data science highly incorporates Predictive Modeling, Research Trends, Data-Driven probabilistic models, prediction algorithms and Decision Making optimisation methods that enable systems to predict 1. Introduction the future and prescribe the best possible course of actions. These are no longer the preserve of expert Information systems (IS) research, combined with big analytics teams, but are increasingly built into enterprise data analytics (BDA) is one of the most important information systems, and are increasingly both paradigmatic shifts in the digital scholarship and autonomous and running alongside human decision- enterprise transformation of the modern day. IS being a makers. Such an integration is observable as science that is traditionally based on structured convergence among the disciplines with computer databases, enterprise applications, decision support science, statistics, behavioral science, information systems, is getting its new definition these days with its theory coming together to afford the infrastructure of integration with the advanced data science intelligent, adaptsove, and self-evolved systems. IS methodologies, cloud-based infrastructures and real study will, therefore, need to broaden the theoretical time analytics functionalities. The deluge of information, and methodological boundaries in order to reflect these spurred by 24-hour digital interfaces, the so-called changes. Internet