Importance of machine learning and data science in modern business

In the rapidly evolving digital landscape, machine learning (ML) and data science have emerged as pivotal tools for businesses seeking to enhance their operational efficiency and competitive edge. The disparity in the adoption of these technologies marks a significant divide in the corporate world. Companies that integrate machine learning (ML) and data science into their operational frameworks indeed tend to outperform those that do not . That means that companies which will not be able to start using those tools may loose competition on the market. That is why it is crucial for each organization to explore the power of data science and ML tools. This research will help organization to better understand how these technologies may help their business.

Analysis of publications

The application of data science in hiring processes has proven to enhance employee selection, aligning more closely with a company's strategic goals and improving efficiency and decision-making [1]. Moreover, the integration of digital transformation strategies, including data mining and ML, has shown significant improvements in enterprise performance [2]. Additionally, advanced predictive analytics and forecasting methods in data science led to optimized business operations and increased organizational productivity [3].

The research into the investment by public companies into machine learning and data science reveals significant engagement. For example, major firms like Robeco, Goldman Sachs Global Investment Research, and Neuberger Berman utilize AI and big data tools to enhance their investment processes, indicating a strong investment into these technologies [27]. Moreover, public companies in Europe that invest in ESG (Environmental, Social, and Governance) practices, leveraging machine learning techniques, have shown better financial performances, reflecting a growing trend in responsible investment decisions [28]. Ahmed Abbood Ali in his paper predicts that the global market size of machine learning implementations will exceed $20 billion by 2024. It emphasizes the widespread adoption across various sectors, including government and social services [29]. According to Market Research News US, paper estimates that machine learning-based implementations will have a global market value of nearly $50 billion by 2025. This reflects significant investment in improving accuracy and reducing errors through advanced machine learning and deep learning technologies [30].

For Ukrainian businesses, the stakes are even higher. Operating under the pressures of an ongoing military conflict and economic instability, leveraging advanced technologies is not merely an option but a necessity for survival and growth. The agility and resilience provided by ML and data science are critical in navigating the complexities of the current economic landscape. Cloud services significantly contribute to the democratization of technology, allowing companies to remain competitive and innovative in challenging market environments. The use of cloud computing has been found to enhance the competitive advantages of commercial organizations, affecting key dimensions such as quality, cost, and responsiveness [4]. Additionally, cloud services have been integral in enabling companies to undergo digital transformations rapidly, especially during the COVID-19 pandemic, by improving collaboration, productivity, and driving innovation [5]. Furthermore, cloud adoption plays a pivotal role in reshaping firm strategies, encouraging adaptability in a volatile digital landscape [6]. These platforms offer scalable solutions that empower even small businesses to implement cutting-edge technologies that refine forecasts, enhance operational efficiencies, and improve service offerings.

Through the strategic use of cloud-based ML and data science services, businesses can tap into powerful analytics and predictive models that drive better business and financial performance. This approach not only mitigates the need for expensive personnel but also accelerates the adoption of innovations, enabling companies to swiftly adapt to market changes and consumer needs while maintaining a focus on core business activities.

Feng Wang & Joey S. Aviles explored how integrating machine learning with business intelligence can enhance operational efficiency. They demonstrated how regression and neural network algorithms, when applied to historical sales data, enable precise sales forecasting. This supports strategic decision -making across marketing, inventory management, and production planning, thus optimizing business operations [7]. Aaron D. Tucker, Markus Anderljung, and Allan Dafoe examined the socio-economic impacts of improved data efficiency through machine learning. Their research focused on how increased data efficiency could potentially lower barriers to entry in AI, fostering greater competition. They discussed the complex effects on privacy, data markets, robustness, and misuse of AI systems, noting that larger, data-rich firms might disproportionately benefit from these advances [8]. Pasquale D'Angelo tackled the challenges of efficiency and effectiveness in machine learning across large datasets. His thesis detailed th e development of scalable learning models that are effective in complex situations such as survival analysis with large Medicare data and multi -label classification involving extensive data sets like Wikipedia and Amazon products [9]. Avln Sujith and his team investigated how machine learning could impact effective financial decision-making within businesses. Their study highlighted how ML techniques help in analyzing vast amounts of data to extract patterns and forecast outcomes, thereby facilitating better decision-making across various business functions including finance and marketing [10]. Leonidas G. Barbopoulos, Rui Dai, Talis J. Putnins, and Anthony Saunders researched the impact of machine learning on market efficiency, particularly in financial markets. They analyzed how machine learning improves the processing of financial information, leading to more efficient market responses and reduced biases, particularly in handling complex and voluminous data sets [11]. M. Aruna, Dr.R. Jayakarthik, Er.S. John Pimo, Bhola Khan, Makarand Upadhyaya, and Neenu Kuriakose investigated the challenges and opportunities of adopting machine learning (ML) in small and medium-sized enterprises. They found that while SMEs struggle with the adoption of ML due to resource limitations and a lack of ML expertise, partnerships and strategic use of available tools can mitigate these barriers. Their study offers strategies to enable SMEs to effectively utilize ML to enhance their competitiveness [12]. Inayatulloh developed a model for adopting machine learning to improve e-commerce effectiveness for SMEs. His research addresses the underperformance of SMEs in meeting sales targets through e-commerce platforms. By proposing a machine learning adoption model, Inayatulloh aims to enhance feature recommendations on SME e-commerce platforms, thereby boosting online sales effectiveness [13]. Shahen Hacyan also focused on enhancing e-commerce effectiveness for SMEs through machine learning. Similar to Inayatulloh, Hacyan's research emphasizes building a machine learning model to assist SMEs in improving their sales performance on e-commerce platforms, highlighting the practical application of ML in navigating the competitive online retail environment [14]. Ling Peng explored the role of machine learning in enhancing the innovation performance of startup enterprises, particularly in the context of organizational resilience. This study uses machine learning algorithms to analyze various business indicators, providing insights into how startups can leverage ML to navigate economic challenges and enhance their innovation capabilities [15]. Nianqiang Li explored the application of data science in marketing and its critical role in achieving a sustained competitive advantage. His study examines how advanced analytics can enhance data-driven decision-making in marketing, highlighting benefits such as improved customization, predictive forecasting, and effective return on investment (ROI) measurements. Li's research underscores the transformational potential of integrating big data and analytics into marketing strategies to boost marketing effectiveness and overall business performance [16]. M. Swetha, N. E., and Santosh D. Parakh discussed the broad impact of empirical data analysis across various business functions such as production, operations, finance, marketing, and human resources. Their work highlights the essential role of data science in enhancing market share by employing diverse data analysis techniques to make more effective business decisions. The study showcases how data science can identify and analyze relationships and trends among business factors, significantly influencing strategic decision-making [17]. Shevchenko N., Turlakova S., and Latisheva O. describe in their work how Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) have proven instrumental in enhancing operational efficiency and transforming business processes for industrial enterprises. These systems collect a significant amount of data related to various facets of business operations. Analyzing this data using Machine Learning (ML) and Data Science techniques can unlock valuable insights to drive strategic development and operational efficiency [18]. Key areas where data from ERP and MES systems can be utilized include predictive maintenance, where historical and real-time machinery data is analyzed to predict maintenance needs, reducing downtime and repair costs. Supply chain optimization involves forecasting demand, optimizing inventory levels, and improving supplier selection processes. Production scheduling and process optimization streamline production schedules, reduce bottlenecks, and maximize equipment utilization. Anomaly detection and quality control help identify deviations in production quality, ensuring consistent output. Lastly, resource management and budgeting optimize resource allocation, control expenditures, and improve budget planning. Antonio Duarte Santos provided insights into how data science strengthens businesses by leveraging large volumes of data to gain market insights. Santos elaborates on the utilization of data science tools and techniques, such as Rstudio and SPSS, to perform economic and business analysis. His essay emphasizes data science's role in transforming raw data into actionable insights that can lead to regenerative benefits for organizations, thereby enhancing their competitive stance [19]. Teresa Guarda, Washington Torres, and Maria Fernanda Augusto analyzed the transformative impact of quantum computing combined with data science on business competitiveness. Their research discusses how quantum computing can address processing challenges associated with large data sets, enhancing the capabilities of data science to solve complex problems and achieve competitive advantages. This interdisciplinary approach suggests significant potential for businesses to innovate and gain market advantage [20]. Burl Henry focused on the role of Big Data in achieving competitive advantage. Henry argues that Big Data is crucial for enhancing productivity, fostering innovation, and transforming business processes. The paper outlines how Big Data can revamp decision-making processes and shift business ecosystems, thereby underlining the strategic importance of data science in maintaining and enhancing competitive advantage in various industries [21].

The analyzed works and a number of other studies remain a number of unresolved questions. None of the referenced studies specifically address the use of ML and data science tools to navigate the challenges posed by external crises like military conflicts or economic instability. While some papers mention general strategies for implementing ML and data science, there are no detailed, step -bystep approach to deploying these technologies in a real life context. This includes data collection, predictive modeling, feature engineering, segmentation, and intervention strategies specifically tailored to manage customer churn during unstable times. The studies mentioned tend to focus on theoretical or broad applications of these technologies rather than detailed, practical integrations. Although some papers discuss the potential economic benefits of ML and data science, none of them provides a concrete analysis of economic impact, including ROI, revenue increase, cost savings, and margin improvement from specific ML - driven strategies.

The goals of the article