This study envisions the future trajectory of intelligent optimization and machine learning (ML) in the realm of business analytics, introducing novel perspectives. It investigates the synergy between big data analytics and ML, underscoring the effectiveness of deep learning architectures in unravelling complex patterns. Emphasizing interpretability, the study explores the development of ML models tailored for business contexts and delves into decentralized model training and data privacy through edge computing and federated learning. In the optimization domain, it addresses the ascendancy of customized meta-heuristic algorithms and explores the convergence of optimization and ML for heightened operational efficiency. This research contributes to a nuanced understanding, fostering innovative applications in the dynamic landscape of business analytics. It has been observed that machine learning and intelligent optimization techniques are very useful for business analytics.
Top1. Introduction
The applicability of emerging techniques like artificial intelligence (AI), machine learning (ML), deep learning (DL), etc. in different fields like healthcare (Tripathi, et. al 2024; Kumar, et al 2024), image processing (Rai, et. al 2023, Tyagi, et. al 2022), agriculture (Tripathi, et. al 2023, Tripathi, et al 2022) and many more has motivated the researchers to apply these techniques in business. According to Bharadia (2023), business intelligence is the process of data collection from external as well as internal sources, turning it into insightful knowledge, and presenting it to decision-makers in a way that is clear and helpful using a variety of tools, technologies, and approaches. Business analytics now includes ML and adaptive optimization techniques as essential elements, providing previously unheard-of capabilities for data analysis, modelling predictions, and decision optimization. This study examines the state of intelligent optimization and machine learning within business analytics, emphasizing important techniques and applications. Also, explores new developments and potential paths, such as improvements in deep learning, reinforcement learning, and different types of meta-heuristic optimization, and how these may affect corporate decision-making procedures. This paper offers insights into the future terrain of ML and intelligent optimization in business analytics through a thorough study of opportunities and difficulties. One of machine learning's main advantages for businesses is its capacity to extract insightful knowledge from massive data sets, enabling them to make more accurate and effective decisions based on data. Machine learning gives organizations the ability to recognize patterns, trends, as well as anomalies that might not be seen using more conventional analysis techniques. It does this by utilizing advanced algorithms and predictive models. This enables businesses to acquire a competitive edge in quickly changing markets, manage processes, and enhance consumer experiences. Additionally, as new data becomes available, algorithms that use machine learning can adjust and learn from it, fostering innovation and constant improvement in a variety of business operations areas. Few of benefits of using machine learning in business sector as given below:
Figure 1.
Incorporation of ML techniques in business sector
Minimizing the need for human data entry: Incorrect and duplicate data are currently the most typical issues that firms deal with. Predictive modelling and machine learning algorithms both employ newly discovered data to prevent errors of this kind. Thus, by implementing machine learning, staff members can optimize their time more effectively.
Identifying spam: By identifying spam, machine learning gives organizations additional value. Previously, email service providers used rule-based spam filtering techniques that were already in place. In order to identify spam and fraudulent messages, spam filters nowadays have developed new rules with the use of neural networks.
Financial analysis: Financial analysis makes extensive use of large amounts of precise, quantitative historical data. In the financial industry, it's commonly utilized for loan underwriting, portfolio management, and algorithmic trading fraud detection. Additionally, chatbots, multiple sentiment analysis interfaces, and customer service will be among the upcoming uses of machine learning in finance.
CLV prediction: The two most frequent issues that marketing experts deal with these days are client segmentation and CLV prediction. Since businesses can readily access large amounts of data, they may use it to gain useful business insights. Users' browsing and purchase history can be understood via ML and data mining, which can then be employed to present them with the greatest deals.