1. Introduction

Background

Within the present-day, particularly competitive and data centric environment, business analytics (BA) and machine learning (ML) have only increased gaining attention among investment analysts. This had arisen from several factors including rapid accumulation of data, fast developing technology and changes in market trends that required advanced predictive capabilities and risk analysis. Business analytics is the analytical practice of developing new insights from data of the past through the complex usage of statistical approaches, data analysis, and forecasting systems (Cox Jr., 2023). Machine learning on the contrary is a subset of artificial intelligence that mainly concerns itself with forming models given the availability of input data and the underlying characteristics of the process (Alghamdi & Agag, 2023).

Companies are integrating analytical and AI-based tools to improve decision-making processes in domains, such as risk and resource management, as well as building business relations (Alghamdi & Agag, 2023). This majorly contributes to the already mentioned competitiveness of these organisations within the market. With the expansion of business operations, the realization of collaborative strategies, as well as the diversification of their structures and activities, making sound and accurate investment decisions becomes paramount.

The ability to foster business collaborations, networks, and cooperation generates a competitive edge, boosts business relations, and contributes to growth and satisfaction (Farida & Setiawan, 2022). Long-term relationships take into account the investing in them, develop skills in the trends of the market, the attitudes and behaviors of consumers, and the abilities of the organization (Kurilovas, 2019). As business circumstances become more complicated, business analytics and machine learning could facilitate overseas investments by improving the accuracy, timeliness, and effectiveness of such investments, including the success of business relations with concerns.

For example, business firms like Hyundai and Samsung in South Korea have been on the lead in utilizing these technologies to enhance their strategic investment. South Korea’s focus on technology driven growth, combined with the country’s strong industrial base, has enabled firms to adopt BA and ML for improving strategic partnerships, especially in sectors like automotive, electronics, and telecommunications (Dayton, 2020; Hong, 2023). This enables these firms to not only remain relevant in the competition but also respond tremendously to the market.

To summarize, industries cannot ignore the business analytics and machine learning tools since the data will continue increasing and they need to appropriately use them for making judicious investment decisions. These tools have both enhanced decision-making processes and strengthened business relations by enabling companies to form more reliable, and efficient partnerships. This makes them important for organizations aiming to stay competitive in the fast-evolving global economy.

Problem Statement

Organizations globally, including those in the UK, South Korea, and other advanced economies, have significant difficulties in making informed strategic investment decisions within the context of business relations. With the nature of markets evolving and the nature of data becoming more complicated and bulky, it causes a difficulty in decision making. The normal approaches used to come up with conclusions on the viability of the given investment, for example, financial projection on paper or going by past trends, is not enough in the current world filled with demographics bombarded with countless data. In addition, there are difficulties with the quality of the conveyed data, implementing analytical systems, and employing people who could effectively use BA and ML.

The challenges are further exacerbated by the fast technological changes and the global interconnectedness of business relations. Factors such as geopolitical changes, instability in markets, and ever-increasing unstructured data encourage indecisiveness among the leaders in charge of making such significant decisions. In particular, industries in countries like the UK and Korea, which rely heavily on technological innovation and global trade networks, need to find ways to better leverage business analytics and machine learning to navigate these complexities. Otherwise, organizations would find it hard to make investment predictions accurately. These challenges leads to the need for robust business analytics and machine learning tools to improve decision accuracy and ensure alignment with strategic objectives.

Research Objectives

To explore how business analytics and machine learning tools are applied in strategic investment decisions.

To examine the impact of these tools on enhancing business relations and improving investment outcomes.

To identify the key factors that influence successful integration of analytics and machine learning in strategic decision-making.

Research Question

What are the roles and impacts of analytics and machine learning in improving strategic investment decisions in business relations?

Significance of the Study

This study is significant for corporate strategists, investors and decision-makers looking to leverage the advanced data-driven tools in making informed and effective strategic investment decisions. Availing how business analytics and machine learning can be harnessed for improving business relations and enhancing investment decision making is relevant as it adds to the already existing literature in business analytics and strategic management. In light of the trends of digitalization that are taking over industries, it is very important to appreciate the investment efficiency, risk control and facilitation of improved business relations that the new technologies can bring. Such knowledge will assist companies in penetrating the market with these innovations subsequently improving decision making, stakeholder relationships, and their competitive level in the market. Furthermore, the research provides important guidance for companies which aim to change from conventional investment techniques into more advanced investment strategies based on data.

2. Literature Review

Introduction

This chapter reviews relevant literature on the role of business analytics and machine learning (ML) in improving strategic investment decisions within business relations. It covers various theoretical frameworks, applications of business analytics and machine learning, and case studies from different industries. The review highlights the intersection of data-driven decision-making with investment strategies, emphasizing how predictive tools can enhance such collaborations.

Theoretical Framework

Decision Theory

Decision theory is basic in appreciating the roles of business analytics and analytics in making strategic investments by organizations making decisions under uncertainty (Cox Jr., 2023; Kaggwa et al, 2024). Therefore, the theoretical framework including the normative decision theory, which focuses on prescriptively how an optimal decision is made, in this case, about capital investment, and the description decision theory which aims analytically at prescriptive accounts of how decisions are made (Jankelová & Puhovichová, 2020). In light of this, with particular respect to strategic investments, the business analytics and machine learning can be viewed as improving the operational efficiency of the firm’s decision making by providing forecasts in a more accurate manner and lowering the level of ambiguity (Akram & Abbas, 2023; Kurilovas, 2019).

In a different end, scholars such as Kahneman and Tversky (1979) created prospect theory arguing that often decision-makers do not behave rationally. In relation to those biases, business analytics tackles some of them and would enable better placement of strategic resources in investment due to managed risks particularly in business engagements involving foreign direct investment (Renierbotha Ltd, 2024). As such why construction of the theoretical models through concepts of decision theories in the investment areas would improve the scenario planning and risk analysis.

Resource-Based View (RBV)

Focusing on the resource- based view (RBV) of an organization, a competitive advantage is internalised within the firm based on how it leverages valuable, scarce and difficult to substitute resources within its operations (Alghamdi & Agag, 2023; Mikalef et al. 2019; Utami & Alamanos, 2023). Emphasis on RBV of business has emerged as an important dimension in the management of vertical information whereby businesses determine what kind of IT resources they need to evaluate before going ahead with the investments and in what manner should these be structured in accordance with their activities (Mikalef et al., 2019). In that respect, business methodology is regarded as unique since only organizations with a particular set of strategic resources can be competitive (Mikalef et al., 2019). These include business analytics and machine learning, which form part of the resources needed for a firm to translate data sets into functional information necessary for decision making (Alghamdi & Agag, 2023).

Such firms possessing data analytics meaning that they understand their data broadly help the organizations to project better their investments and improve more the relationships with businesses facilitating the companies to have a strong edge over their counterparts. In modern economy, data and processing data or interpreting data forms the critical competencies. (Yang & Morgan 2011) The resource-based theory allows the managers to systematically consider the resources in terms of their advantages or disadvantages so that the organization can make informed strategic choices which will utilize the advantages to meet the goals of the organization against the limitations effectively (Gupta, 2023).

Change Management in Technology Implementation

The successful integration of business analytics and machine learning requires effective change management. According to Lewin’s Change Management Model, the three stages of change—unfreezing, changing, and refreezing provide a framework for implementing new technologies (Barrow et al., 2024; Hussain et al., 2018). Studies on technology acceptance models suggest that organizations need to prepare their workforce to adopt analytics tools by emphasizing the benefits of these tools for improving strategic investment decisions (Bach et al., 2016; Parvari et al., 2015).

The technology acceptance model posits that perceived usefulness and ease of use are key drivers of successful technology adoption (Bach et al., 2016). In the context of business relations, implementing business analytics and machine learning to enhance investment decisions often requires overcoming internal resistance, ensuring proper training, and fostering a data-driven culture within organizations (Bach et al., 2016; Barrow et al., 2024). This theory underscores the importance of managing the human aspects of integrating machine learning into strategic decision-making.

Business Analytics and Machine Learning

Business analytics and machine learning are increasingly being used to support decision-making in organizations by utilizing data-driven approaches to analyze historical trends, predict future outcomes, and manage risks (Akram & Abbas, 2023; Wakefield, n.d.). Predictive analytics, scenario analysis, and risk management are core components of how business analytics improves strategic investment decisions.

Predictive models based on machine learning algorithms help organizations anticipate market trends, customer behaviors, and financial outcomes (Gupta et al., 2024; Wakefield, n.d.). For instance, the use of regression models and time-series forecasting allows businesses to predict the performance of potential investments in their partnerships and joint ventures (Gupta et al., 2024).

Business analytics enables decision-makers to explore multiple investment scenarios and assess the risks and rewards associated with different strategies (Ibeh et al., 2024). This is particularly useful in business relations where long-term commitments and resource-sharing are common. Prescriptive analytics relies on predictive models to help guide strategic decisions, using optimization methods and simulations to suggest the best actions to take (Ibeh et al., 2024).

Machine learning algorithms can be used to analyze vast amounts of data to identify potential risks and trends that may not be immediately apparent through traditional methods. These tools can help organizations mitigate risks in business partnerships by identifying financial or operational vulnerabilities (Kurilovas, 2019).

Strategic Investment Decisions in Business Relations

In business relations, strategic investment decisions often involve partnerships, joint ventures, and long-term collaborations (Elmassri et al., 2020). Traditionally, these decisions were made based on historical performance or intuition, but the growing use of data-driven approaches is transforming this process. Business analytics and machine learning enhance the evaluation of potential partners by analyzing financial performance, market trends, and industry data (Ibeh et al., 2024). These tools also help optimize investment terms by forecasting the long-term benefits and risks of partnerships. Furthermore, real-time insights enable dynamic adjustments to investment strategies as market conditions shift (Wakefield, n.d.). For instance, in joint ventures, analytics tools can monitor key performance indicators (KPIs), ensuring both parties benefit from the collaboration through continuous performance tracking.

Applications of Business Analytics and Business Relations

Business analytics is applied across various industries to improve internal management and decision-making. Different companies reveal how analytics and machine learning are utilized in strategic investments. In the financial sector, firms like HSBC and BlackRock employ machine learning to enhance portfolio management, improve risk assessments, and automate decision-making processes (O’Connell, 2024). Machine learning and analytics enable the financial sector to perform tasks such as credit risk assessment, fraud detection, and market movement prediction using advanced models (Kalogiannidis et al., 2024).

Predictive analytics help firms make faster and more informed investment decisions, improving returns and reducing exposure to market risks. In the manufacturing sector, predictive analytics has been used to improve supply chain efficiency, optimize production processes, and enhance collaborations with key suppliers (Ollion, 2023).

Gaps in Existing Literature

Although much research covers the use of business analytics and machine learning in decision-making, few studies focus on their role in enhancing business relations through strategic investments. Most existing work centers on operational efficiencies or customer-facing applications rather than business relations investment decisions.

Moreover, little research has been done on the difficulties that organizations face while implementing these technologies, such as change aversion and problems with data quality. These gaps necessitate more research into how businesses might use analytics and machine learning to make wiser strategic investment choices in intricate business relationships.

This study will employ a case study approach to investigate how BA and ML techniques are used in diverse industries to influence strategic investment decisions, in light of the gaps found in the literature. In order to accomplish the research objectives, Chapter 3 will describe the research design, case selection criteria, and data collection techniques.

3. Methodology

Introduction

This chapter presents the methodology used to examine the role of business analytics (BA) and machine learning (ML) in enhancing strategic investment decisions within business relations. The research aims to explore these technologies' application and impact across various industries, focusing on real-world cases. The methodology is structured to ensure a systematic approach that fulfills the research objectives.

Research Design

The case study approach is chosen because it facilitates a comprehensive exploration of real-life examples where BA and ML have been integrated into strategic investment decisions. This method enables the researcher to focus on how and why certain companies succeed or face challenges when implementing these technologies (Rashid et al., 2019). By examining cases across different industries, the study will derive comparative insights, offering both depth and generalizability.

The selection of case studies is grounded in the need to address the research question: What are the roles and impacts of business analytics and machine learning in improving strategic investment decisions in business relations? Case studies allow for a detailed investigation of companies’ internal decision-making processes, enabling the identification of specific factors that influence successful integration of BA and ML. Case studies ensure that the findings are relevant and applicable to real-world scenarios, making the research practical for corporate strategists and investors (Rashid et al., 2019).

Case Selection Criteria

The purpose of this selection criterion is to ensure that the case studies are relevant and provide helpful insights: (1) firms must have implemented business analytics and machine learning (BA and ML) into strategic decision making to gain a competitive advantage; (2) a selection will be made across industries, such as finance, technology, manufacturing, and healthcare to prevent industry confines; (3) companies should be engaged in strategic alliances, or joint or long-term business ventures; (4) they tend to be global or market leaders in their field. For these purposes, examples of such types of companies could be Hyundai, HSBC or Samsung, which actively adopt BA and ML in their decision making process.

Data Collection Methods

There will be collection of data for this research from secondary sources as the key sources for case studies are concerned. This is the rationale for proposing this approach that, there are many perspectives on company, which use BA and ML in the strategic investment decision to be easily accessed. Reports and studies produced every year, finances, and strategic documents will provide how these companies apply BA and ML.

News articles, white papers, and industry reports from reliable sources will serve the purpose of providing more information and showing with examples BA and ML directions of making strategic decisions in various industries. Also, the research will involve using other databases including ResearchGate which has academic research articles as well as market research reports on the use of BA and ML in making decisions on investments.

Data Analysis

A thematic analysis of the research cases will be carried out to analyze how the themes of the cases are interrelated. Thematic analysis is flexible and is appropriate for analyzing how fast-growing companies that incorporate the use of BA and ML into their investment strategies. The recognition of patterns in data will enable the study capture dependencies between the active use of data analytics and further maturity of the investment strategy.

Apart from the qualitative methods, comparative analysis is prone to be employed to assess how these technologies are adopted by different companies in different industries (Cantrell, 2011). Where applicable, descriptive statistics will be used particularly in the evaluation of investment performance after the application of BA and ML in particular investment decisions.

Ethical Considerations

Ethical considerations are essential in research involving company data, especially when dealing with proprietary or sensitive business information (TRIPATHY, 2013). Since this is a secondary research where data will be obtained from published, available documentaries and academics, the risk of breaching any confidentiality clause is quite slim. However, measures will be taken to protect the confidentiality of sensitive information. The research will also be designed in such a way that none of the companies used in the case analyses will suffer any reputational damage, or be subjected to undue pressure from the analysis. Ethics will be observed in the entire research work.

4. Results’ Analysis and Findings

Introduction

In this chapter of the work, a brief overview of the application and potentialities of BA and ML in strategic investment decision-making processes has been undertaken, focusing on apportioning particular case studies on business organizations. Each selected business case, sheds light on a different industry and design for the assessment of the use of BA and ML for strategic investment in a number of industries.

The chapter has three parts each making a case study of Hyundai Company with reference to automobile industry, HSBC in respect of banking and finance and Samsung in the electronics domain. A cross-case analysis will then be performed with the aim of deriving similarities and differences between the application of BA and ML in the context of investment decision making by these companies.

Case Studies

Each case study has been structured to present the company’s background, the presenting problem it faced in strategic investments, and the solution offered through BA and ML tools. The case study results are then related to the theoretical frameworks discussed in the Literature Review chapter, with a focus on decision theory, resource-based view (RBV) and change implementation. Each case has been carefully formulated, focusing on specific research questions and grounded in existing literature and theoretical frameworks.

Case Study 1: Hyundai Motor Company

Hyundai Motor Company is a multinational automotive manufacturer with headquarters in South Korea. It is, moreover, a progressive corporate establishment that has adopted various advanced technologies such as BA and ML in order to improve all aspects of its operations, be it the running of factories, or forming business partnerships. Hyundai has shown that they are able to assess local markets and utilize this information to make vehicles that are well adapted to a specific market (Mixson, 2022).

Hyundai have been faced with increasingly complex global markets, where the company had to manage a variety of external risks such as fluctuating demand, evolving customer preferences, and supply chain disruptions (Akabane, 2012). The need for improved decision-making in long-term investments. Hyundai Card, the credit card division of the Korean automotive giant, has been steadily attracting top data science talent for years and is now equipped to analyze the spending habits of its over 9 million cardholders as of 2020 (Kang, 2020). This card gathers between 2,000 and 3,000 data points from each of its cardholders, using the information to predict when it’s time to replace a vehicle’s tires or brakes, as well as to analyze individual spending habits and lifestyle choices (Kang, 2020).

Hyundai have implemented demand forecasting, the searching for the potential markets expansion and the improvement of investments in the electric vehicle segment by making use of predictive analytics and machine learning models. Due to the implementation of these sophisticated data-based tools, Hyundai used various market strategies in order to optimize the investments of the company (Mixson, 2022). These technologies also improved the relationships with the suppliers through enhancing the supply chain and cutting down costs (Hong, 2023). One of such systems is the autonomous delivery robot unveiled by Hyundai Motor Company and Kia Corporation which employs machine learning and a blockchain based supply chain supplier monitoring system (Hong, 2023; O’Sullivan-Dale, 2024). Therefore, machine learning algorithms were central to this collaboration as this enabled prediction of how the electric vehicle market will develop in the years to come based on past market data (HMG Newsroom, 2024). Hyundais will not be automobiles but AI machines which will be constantly evolving with the aid of data from software defined vehicles to improve safety, driving and usability.

Hyundai Motor has revealed plans to invest a total of KRW 120.5 trillion over the next decade, spanning from 2024 to 2033 (HMG Newsroom, 2024). According to HMG Newsroom, 2024, 14.4 trillion allocated specifically for strategic initiatives, this example highlights how data-driven technologies can influence decision-making in industries like automotive, and offers a framework for comparing the effectiveness of BA and ML in optimizing investments across different sectors.

Case Study 2: HSBC Holdings

HSBC, one of the largest banking companies in the world has its headquarters in the U.K, has had to endure problems in managing strategic investments and establishing long lasting business relationships due to the rapidly changing financial landscape. However, with the increasing regulatory constraints and uncertainties in the global economy, HSBC have had to deploy strategies that would be able to rationalize its investment policies and anticipate how the market would develop further (HSBC Global Research, 2024).

HSBC’s view focuses on responsible use of the adequate data that collected from customers to provide enhanced customer services and experiences. This is in the light of the research question regarding the contributions of business analytics in the making of business strategies. In doing so, it provides HSBC with tools for understanding customers and making sounder service and investment opportunities (Boteju, 2020). HSBC's focus on thematic research (demographics, disruptive technologies, etc.) illustrates how business analytics can identify trends that are critical for making informed investment choices (HSBC Global Research, 2024).

In addition, HSBC’s integration of natural language processing (NLP) tools helped analyze vast amounts of unstructured data, such as news reports and market sentiment, to inform strategic investment decisions (HSBC, 2023). These innovations not only improved the bank’s ability to assess risk but also helped streamline partnerships and improve long-term business relations through more accurate data-driven decisions. The insights HSBC gains from big data not only enhance customer service but also inform strategic decisions about products, services, and financial offerings (Morning Studio Editors, 2022; Pahuja, 2023). By integration of machine learning technologies in various aspects of research, such as equity strategy and foreign exchange (FX), HSBC enhance the analytical process and contribute to better investment decisions. HSBC's integration of AI signifies a strategic move aimed at improving efficiency, scalability, and elevating client services within the banking industry (Zaytsev, 2023).

The adoption of ML and BA tools enabled HSBC to increase the accuracy of its investment assessments, resulting in more successful mergers and partnerships (Zaytsev, 2023). One of the key findings was that ML-driven insights into credit risks and asset valuations improved the bank’s ability to make long-term investment decisions in high-risk markets (Fawthrop, 2019; McKinsey, 2022).

Case Study 3: Samsung Electronics

Samsung Electronics, a manufacturer of technology and consumer electronics, has its headquarters are located in South Korea, while its subsidiaries are involved in sales and production through other countries (Prabhu & Bhat, 2020). It has also been making significant effort into R&D and other novel forms of technology to remain competitive. The company has also used strategic investments in diverse areas to enhance its presence in the global market including those concerned with 5G technology, semiconductors and smart devices (IDC, 2020).

An instance of the strategic management of Samsung’s business analytics capabilities was during product development processes of new products & practices. Augmented intelligence systems empower teams to study consumer behaviors and alternatives using big data and algorithms. This has been especially critical in the cellphone segment where Samsung leads with the Galaxy range of products. As an illustration, Samsung’s marketing and analytical divisions leverage augmented intelligence approaches to understand customer data such as devices, upgrade preferences, carrier loyalty, among others, enabling sound management of product launch and marketing decisions (TDWI, 2022). Samsung Electronics also leverages Amazon forecasting tool canvas to help business analysts comprehend memory chip demand forecasting trends without any coding or ML assistance (AWS, 2023). This approach helps Samsung maintain its competitive edge especially in fast-paced markets with intense pressure from lower-cost competitors.

Samsung Electronics has made significant investments in emerging technologies, such as artificial intelligence, quantum computing, and electric vehicle (EV) batteries, using machine learning and business analytics to guide these decisions (Law, 2024). By analyzing data from various sources, Samsung identifies trends and opportunities in industries with high growth potential, such as renewable energy and autonomous vehicles (Emilio, 2021; Law, 2024). One example is Samsung’s investment in AI-driven battery research, where machine learning algorithms are used to predict the behavior of new materials and improve battery efficiency (Rani, 2024).

Samsung uses business analytics and machine learning to optimize its global supply chain, ensuring that its vast network of suppliers, manufacturers, and distributors operates efficiently (AWS, 2023; Kharwal, 2023). Machine learning models are used to forecast demand, manage inventory, and in identifying potential risks in the supply chain, such as disruptions caused by geopolitical events or natural disasters. By accurate forecasting of demand and managing inventory in real time, Samsung ensures efficient product availability while reducing waste and lowering operational costs (Kharwal, 2023).

Comparative Analysis

Hyundai aims at improving the use of BA and ML in enhancing operations especially targeting the electric vehicle (EV) production and the global market area. In utilizing predictive analytics, Hyundai has been able to increase the accuracy of demand forecasting, improve the supply chain, and strengthen relationships with suppliers. Similar to HSBC, within the banking sector, risk advanced analytics, customer analysis, and regulatory compliance are integrated by means of BA and ML. HSBC Banking also uses predictive analysis techniques such as natural language processing (NLP) to analyze market trends and forecast economic performance, which helps the bank invest wisely and serve clients better (Kharwal, 2023). Samsung Company also combines BA and ML for demand forecasting, supply chain management and investment in 5G and AI battery technology. Samsung utilizes Amazon SageMaker to enable its analysts to make decisions that integrate product creation as well as inventory management (AWS, 2023).

To summarize, Hyundai and Samsung do a lot of improvements BA and ML enabled for operational effectiveness and product strategy, whereas HSBC does their main activities towards risk assessment and seeking customers’ insights. All of them show how such technologies are essential in undertaking the right investment decisions.

5. Discussion of the Findings

Introduction

This chapter provides an in-depth discussion of the research findings, examining the roles of business analytics (BA) and machine learning (ML) in improving strategic investment decisions. Key themes include the effectiveness of these technologies in business relations, critical success factors, challenges encountered, and the broader implications for strategic management. The insights gained from the case studies in Chapter 4 will be discussed to offer a comprehensive understanding of how BA and ML impact corporate decision-making processes.

Role of Business Analytics in Strategic Investment

Business analytics (BA) has proven to play a pivotal role in enhancing the quality and efficiency of investment decisions. Across industries, BA enables companies to analyze vast amounts of data, providing critical insights that inform decision-making (Decision Foundry, 2024). For instance, Hyundai’s use of predictive analytics in EV investments allowed the company to foresee market demands, optimize production, and reduce risks associated with fluctuating market conditions (HMG Newsroom, 2024). HSBC applied BA to customer data, helping the bank identify new opportunities, improve risk management, and streamline investment portfolios (Morning Studio Editors, 2022; Pahuja, 2023). Samsung utilized analytics to predict market trends in the fast-moving technology sector, leading to more informed product development and resource allocation decisions (AWS, 2023; Kharwal, 2023).

Overall, BA helps companies transform data into actionable insights, fostering data-driven investment strategies that enhance decision quality and precision. By providing real-time data analysis and trend identification, BA contributes to making strategic decisions that are not only responsive to market conditions but also proactive in anticipating future opportunities.

Machine Learning's Contribution to Predictive Decision-Making

Machine learning (ML) significantly enhances companies' abilities to assess risks, predict trends, and analyze potential investment scenarios. The integration of ML models in companies like HSBC and Samsung has improved the accuracy and reliability of decision-making processes. For example, HSBC’s use of ML for risk assessments has led to better investment decisions in high-risk markets. By analyzing large datasets, ML algorithms help businesses predict future market trends, forecast demand, and assess investment outcomes with greater precision.

At Hyundai, ML was used to predict future market growth for EVs, allowing the company to invest strategically in technology and infrastructure (HMG Newsroom, 2024). Samsung’s use of ML in demand forecasting for semiconductors illustrates how predictive decision-making can drive resource optimization and long-term growth in volatile markets (Law, 2024).

The value of ML lies in its ability to continuously learn from new data, improving the accuracy of predictions over time and offering businesses the tools to make decisions that are both data-informed and forward-looking.

Success Factors in the Use of Analytics and ML

Several critical success factors emerged in the successful implementation of BA and ML across the case studies. These include;

Data Accessibility and Quality

For BA and ML to be effective, companies must have access to high-quality, structured data. Hyundai’s ability to gather and process vast amounts of data across global markets was crucial in improving its EV investments. Similarly, HSBC’s success with ML-based risk assessments was contingent on its ability to access accurate financial and customer data.

Integration with Business Strategy

Companies that align their BA and ML initiatives with broader business goals achieve better outcomes. Samsung’s integration of ML into its R&D and supply chain management strategies helped optimize resource allocation and streamline operations, enabling more informed investment decisions.

Cross-functional Collaboration

Successful BA and ML adoption requires collaboration across departments. For instance, Hyundai’s integration of data science into its automotive and credit card divisions allowed the company to leverage insights across different business areas, enhancing overall decision-making capabilities.

Investment in Talent and Technology

Companies that invest in advanced technology infrastructure and skilled personnel are more successful in implementing BA and ML. Hyundai, HSBC, and Samsung all demonstrated the importance of acquiring talent with data science expertise to maximize the potential of these tool.

Challenges and Limitations of BA and ML Integrations

Despite successful integration of BA and ML as shown in the case studies, there are possible challenges and limitations that could pose as barriers in the process. First, is on data reliability where the accessibility and ensuring that the accessed data is quality. Studies have more emphasized the inconsistent and incomplete data can hinder the effectiveness of analytics and machine learning (Hariri et al., 2019). Data integrity and availability remain significant barriers. Incomplete or inconsistent data can limit the effectiveness of BA and ML models, as seen in cases where companies struggled to consolidate data from disparate sources (Danielkievych, 2022). Hyundai’s adoption of BA and ML for EV production likely faces challenges in market forecasting due to fluctuating demand and consumer preferences in the EV sector. Furthermore, data quality issues could arise as Hyundai integrates data from various sources, including market research, manufacturing, and customer feedback.

The integration of advanced technologies with existing legacy systems is often complex and resource-intensive. Integrating advanced technologies like BA and ML with legacy systems is often both complex and costly as shown in different cases (Zawadzki, 2024). Legacy systems generally lack the flexibility needed to support modern analytics, requiring significant investments in upgrades, data migration, and compatibility adjustments. For instance, Samsung’s adoption of ML for demand forecasting necessitated substantial updates to its existing infrastructure, including data storage and processing enhancements. These updates not only incur high initial costs but also require skilled personnel and ongoing maintenance, making smooth integration a resource-intensive endeavor that can disrupt regular operations.

Lastly, the resistance to change and a lack of data literacy among employees can also impede the successful adoption of BA and ML. Approximately 28% of AI/ML initiatives have failed, with key factors including a shortage of skilled personnel, insufficient production-ready data, and the absence of an integrated development environment (IDC, 2020). Companies need to ensure that employees are trained in data-driven decision-making processes and that there is a culture of trust in the outputs of these technologies.

Implications for Strategic Management

The results of this study are quite relevant and hence the strategic management and decision theory are inextricably linked to them. One of the first things to note is that business analytics (BA) and machine learning (ML) integration necessitates data driven approaches in decision making. Organizations that use data efficiently stand a better chance of meeting new needs in the marketplace as there are shifts in the directions of the markets that changes with time (AWS, 2023; Kharwal, 2023). It increases the flexibility and competitiveness of firms in industries characterized by constant changes. Further, the Resource Based View (RBV) is also strengthened as assets that do not have a physical form such as data and technology become imperative in the pursuit of competitive advantage (Utami & Alamanos, 2023). Lastly, companies like Hyundai and Samsung illustrate on how BA and ML can evolve the core processes of a company while fostering innovations at the same time salvaging the brand power of such companies.

Also, for the successful execution of BA and ML, there is a need for an effective change management strategy, since these technologies are often associated with a need for a change in organizational culture (Bach et al., 2016; Barrow et al., 2024). It is the role of the managers to create a culture of innovation and learning where employees are willing to work in a data-centric manner. Lastly, due to the predictive nature of ML, firms are encouraged to think long term and devise strategies on how to beat competition by placing investments in the most upcoming trends (Akram & Abbas, 2023; Ollion, 2023). Such a perspective enables businesses to lower potential losses, enhance plans of action, and maintain a competitive position within the confines of the dynamic market.

6. Conclusion and Recommendations

Introduction

This chapter concludes the research, reflecting on the initial objectives and summarizing the findings. The main purpose of this study was to investigate the use of business analytics (BA) and related machine learning (ML) technologies for improving decision-making within business relations, particularly, strategic investment activities. The objective of the research was also to discover whether and how these technologies have changed and continue changing decision-making through the investigation of case studies across various industries. This research, in general, has met the research objectives by asserting that BA and ML technologies are beneficial in increasing the efficiency, precision, and future value of strategic investments.

Conclusion

Summary of Key Findings

The study demonstrates that BA and ML play a crucial role in enhancing strategic investments across various industries. In the cases of Hyundai, HSBC, and Samsung, it can be seen how these technologies add value in risk mitigation, demand forecasting and effective management of complicated entities. BA enables companies to use data in colossal proportions in seeking out the qualifying patterns and resource management while coping up with the shift in the market. In the same way, ML also plays a role by providing importance to information through forecasting and predictive activities by providing information to the companies on selecting the right strategy in growth opportunities in the market to enhance growth. This technology allows firms to optimize investment decisions in order to maintain their competitiveness and minimize risks.

Firms use BA to manage large sets of data in order to make trend spotting and resource management more effective. For example, Hyundai utilizes predictive analytics for market risk evaluation connected to its electric vehicle ventures, therefore facilitating a more efficient allocation of resources (Mixson, 2022). HSBC employs machine learning procedures in its credit risk management to enhance forecasting processes and more accurate financial planning in volatile markets (HSBC Global Research, 2024). For the case of Samsung employs, it employs BA to analyze market trends and consumer behavior, which informs its strategic investments in emerging technologies such as AI and quantum computing (AWS, 2023; Law, 2024). By utilizing scenario analyses, companies can anticipate future market developments and make data-driven decisions, thereby optimizing investment strategies and reducing uncertainties.

Contributions to Theory and Practice

From a theoretical standpoint, this study adds to the increasing work on business analytics and machine learning for the case of strategic investments with a focus on decision theory and resource based view focalizations. It stresses on the concept of competition being built through resources such as information and technology. The study also shows how these technologies can be used by organizations to support decision making and build long-term business relationships as well as how resources can be efficiently managed. Focusing on real-life examples makes the study beneficial to business practitioners and investors who wish to incorporate BA and ML in their business and investment strategies.

Limitations

Nevertheless, an obvious drawback of this paper is the much use of secondary data sources which is understandable given the complex nature of the business analytics and machine learning deployment in the companies analyzed. In addition, cross-industrial generalizability of the findings may be affected by relatively small scope of work due to broad industry specific issues and peculiarities. These shortcomings state that future studies should use such data collection method which requires seeking answers from people and are more task specific to provide further explanations on how such technologies work in practice. This could include the lack of incorporating of primary data, such as interviews with decision-makers, to provide firsthand insights and a deeper understanding of the challenges and advantages of BA and ML in strategic investment contexts.

Recommendations for Future Research

Future research should explore the industry-specific applications of BA and ML, focusing on how these technologies function in sectors beyond those studied here. For example, industries like healthcare, retail, or energy could offer new insights into how BA and ML impact strategic investment.

Also, further exploration of the integration of artificial intelligence (AI) with BA and ML could provide a deeper understanding of emerging technologies’ role in strategic decision-making. Future research could also examine the ethical implications of using these advanced tools, particularly regarding data privacy and the potential biases in AI-driven decisions.

Finally, the future researches could be able to benefit more from incorporating primary data, such as interviews or surveys with decision-makers and data analysts directly involved in BA and ML implementation. By collecting firsthand insights researchers could gain a deeper understanding of the real-world challenges, practical benefits and evolving strategies associated with these technologies. Primary data will be able to reveal nuanced perspectives on issues like employee adaptation to analytics tools, the exact challenges in data quality management and, practical approaches to aligning BA and ML with business strategy.