This chapter focuses on the idea of business analytics through AI and aims to address how AI has emerged as a powerful force in augmenting and replacing traditional human-computer interactions in the realm of business analytics. AI-powered analytics can uncover hidden patterns, detect anomalies, and automate decision-making processes, significantly augmenting the efficiency and accuracy of data analysis. Thus, the purpose of this chapter is two-fold. First, the chapter sheds light on business analytics, big data, and big data analytics through AI. It delves into the theories of machine and deep learning and their synergy with big data analytics. Secondly, the authors analyze a case study to substantiate our theory. ML-based prediction models using stock market data are developed to underline the significance of adopting AI-driven approaches for business analytics.
TopIntroduction
Human-computer interaction (HCI) is “the research area that studies the interaction between people and computers, which involves designing, implementing, and evaluating interactive systems in the context of the user’s task and work” (Dix et al., 2004). This can be thought of as an attempt to communicate between two powerful information processing units (a computer and a human) over a narrowband and very limited interface (Tate, 2002). Human cognition, including analytical reasoning and decision-making, is embedded in a computational framework with automated analysis in the human-computer model. It has varied applications for health care (electronic health records, medical imaging systems), education (digital classrooms), gaming platforms (PS4, Xbox One), speech recognition (Alexa, Siri) and in industry and business. HCI has played a crucial role in enhancing business analytics by providing a user-friendly interface that enables professionals to interact with complex data and extract valuable insights. Through intuitive design and interactive visualization tools, HCI has facilitated the interpretation of vast datasets, allowing businesses to make informed decisions and identify patterns that may have otherwise gone unnoticed.
However, the landscape of business analytics has undergone a transformative shift with the advent of artificial intelligence (AI) which enables computers to perform human-like tasks by mimicking cognitive functions such as learning, reasoning, and problem-solving. AI has not only automated many tasks involved in data analysis but has also introduced machine and deep learning algorithms capable of discerning intricate patterns and predicting future trends. This evolution has significantly reduced the reliance on manual interaction in the analytics process, enabling businesses to process and interpret data at an unprecedented speed and scale. While HCI remains crucial for facilitating human understanding and decision-making, AI has emerged as a powerful force in augmenting and, in some cases, replacing traditional human-computer interactions in business analytics. On the other hand, in the information era, huge volumes of data from various sources are accessible to decision-makers. The term “Big data” has been coined to encapsulate this data's unprecedented volume, variety, and velocity, highlighting the need for advanced tools and analytics to derive meaningful insights and make informed decisions in this data-rich landscape.
Fan et al. (2015) introduced a marketing-mix framework aimed at effectively utilising big data for marketing intelligence, offering a structured approach for decision-making in marketing based on insights from big data analytics. A. Sharma et al. (2022) delved into the realm of big data in the healthcare industry, highlighting its wide-ranging impact across various stakeholders such as healthcare systems, insurers, researchers, and governmental bodies. The study emphasized the pivotal role of big data analytics in shaping the future landscape of healthcare delivery, drawing from diverse and abundant data sources, both structured and unstructured. Moreover, Wells et al. (2016) delved into the realm of big data in the healthcare industry, highlighting its wide-ranging impact across various stakeholders such as healthcare systems, insurers, researchers, and governmental bodies. The study emphasized the pivotal role of big data analytics in shaping the future landscape of healthcare delivery, drawing from diverse and abundant data sources, both structured and unstructured. Iqbal et al. (2020) explored the synergistic potential of big data and Computational Intelligence (CI) in addressing real-world challenges in smart cities, identifying numerous areas where innovative applications can be developed by leveraging these powerful tools. Nevertheless, despite these advancements, existing literature lacks research that integrates theories of big data analytics with AI specifically tailored for business analytics.
Hence, this chapter elucidates the imperative of a symbiotic relationship between AI and big data analytics for business decisions. The primary objective of this chapter is to address this current gap in the understanding and application of business analytics, big data, and big data analytics through the integration of AI.