Analytics Landscape Research Articles

Future of Artificial Intelligence & Business Analytics

Artificial intelligence (AI) and business analytics are two rapidly developing technologies transforming how businesses operate. AI is the branch of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, while business analytics refers to the process of using data to make informed business decisions. The purpose of this paper is to explore the future of AI and business analytics and how these technologies will shape the business landscape in the coming years. The increasing availability and use of artificial intelligence (AI) technologies in business analytics have the potential to revolutionize the way organizations operate. This research paper explores the future of AI and business analytics, examining how advances in AI technology are shaping the field and what changes we can expect to see in the coming years. The paper examines the benefits and challenges of using AI in business analytics, as well as the ethical considerations that arise with the increased use of this technology. Additionally, the paper explores how organizations can best leverage AI in their business analytics strategies to maximize the potential benefits while minimizing the risks. Through a review of existing literature and analysis of emerging trends, this paper provides valuable insights for businesses and policymakers seeking to understand the evolving landscape of AI and business analytics. Looking ahead, the future of AI and business analytics is likely to be shaped by a number of key trends. These include the increasing use of machine learning and deep learning algorithms, the integration of AI with other emerging technologies such as blockchain and the Internet of Things, and the growing importance of explainable AI that can provide transparency and accountability in decision-making.

Predictive analytics in credit risk management for banks: A comprehensive review

This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical and regulatory compliance. It underscores the effectiveness of predictive analytics, showcasing its ability to enhance risk assessment precision, decision-making agility, and overall banking performance. Comparative analyses reveal the varying performance of predictive models across contexts, emphasizing the importance of tailored model selection. However, challenges such as data quality, model interpretability, talent scarcity, ethical considerations, and implementation costs pose significant hurdles. Looking forward, predictive analytics promises to be an indispensable tool for mitigating credit risk in the banking sector, offering refined risk assessments, smarter decisions, and enhanced resilience. The insights from this review provide valuable guidance for banking professionals, regulators, and researchers navigating the evolving landscape of predictive analytics in banking.

Reviewing business analytics in healthcare management: USA and African perspectives

This comprehensive review explores the application and impact of business analytics in healthcare management, focusing on the distinctive perspectives of the United States and Africa. In an era where data-driven decision-making is paramount, healthcare organizations globally are increasingly turning to business analytics to enhance efficiency, improve patient outcomes, and optimize resource allocation. The review begins by examining the state of business analytics in healthcare management within the United States, a pioneer in adopting advanced analytics solutions. The analysis encompasses the utilization of predictive modeling, data mining, and artificial intelligence to streamline operations, enhance clinical decision-making, and improve patient care. Case studies and success stories from leading healthcare institutions in the USA illustrate the transformative power of business analytics in areas such as patient risk stratification, resource optimization, and population health management. Contrastingly, the review delves into the unique challenges and opportunities that characterize the adoption of business analytics in healthcare across the African continent. Despite facing resource constraints and varying levels of technological infrastructure, African nations are increasingly recognizing the potential of analytics to address pressing healthcare challenges. The discussion encompasses initiatives and collaborations aimed at leveraging data analytics to improve healthcare delivery, disease surveillance, and resource management in the African context. The comparative analysis between the USA and Africa highlights the diversity of approaches and contexts in implementing business analytics in healthcare management. Factors such as data accessibility, infrastructure development, and cultural considerations emerge as critical determinants shaping the successful integration of analytics tools in healthcare decision-making. The review underscores the pivotal role of business analytics in reshaping healthcare management practices, while acknowledging the need for tailored strategies that align with the specific challenges and opportunities inherent in the USA and African healthcare landscapes. This study provides valuable insights for policymakers, healthcare administrators, and researchers seeking to navigate the evolving landscape of healthcare analytics on both a global and regional scale.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.

AI INTEGRATION IN BUSINESS ANALYTICS: A REVIEW OF USA AND AFRICAN TRENDS

The relentless evolution of Artificial Intelligence (AI) has significantly transformed the landscape of business analytics, offering unparalleled opportunities for organizations to enhance decision-making processes and gain a competitive edge. This study provides a comprehensive review of AI integration in business analytics, focusing on the distinctive trends observed in both the United States (USA) and African business ecosystems. In the United States, a technologically advanced market, the adoption of AI in business analytics has witnessed remarkable strides. Corporations across various sectors leverage AI-driven tools and algorithms to analyze vast datasets, extract meaningful insights, and optimize strategic decision-making. The USA's emphasis on innovation and robust technological infrastructure has propelled AI integration as a cornerstone of modern business strategies. Contrastingly, the African continent is experiencing a unique trajectory in AI adoption within the realm of business analytics. Despite facing challenges related to infrastructure and resource limitations, African businesses are increasingly recognizing the transformative potential of AI. Initiatives promoting AI education and collaboration with global tech partners have contributed to a growing awareness and implementation of AI in business analytics across various African industries. This review explores commonalities and divergences in the trends observed between the USA and Africa, highlighting the factors influencing AI integration in each region. Factors such as regulatory frameworks, cultural nuances, and economic landscapes play a pivotal role in shaping the AI landscape in both contexts. By understanding these trends, businesses can tailor their AI strategies to align with regional dynamics, fostering sustainable growth and innovation. This study provides valuable insights into the evolving landscape of AI integration in business analytics, offering a comparative analysis of trends in the USA and Africa. As organizations navigate the complexities of adopting AI, acknowledging regional variations becomes crucial for developing effective and context-specific strategies.
 Keywords: AI, Business Analytics, USA, Africa, Business, Innovation.

Telecom data analytics: Informed decision-making: A review across Africa and the USA

Telecom data analytics has emerged as a pivotal tool for transforming raw data into actionable insights, empowering telecom operators to make informed decisions and enhance the overall efficiency of their networks. This abstract provides an overview of the comprehensive review that explores the landscape of telecom data analytics in both Africa and the USA. The review delves into the diverse strategies, challenges, and opportunities associated with telecom data analytics in these regions. It examines how advanced analytics techniques, including machine learning and artificial intelligence, are being leveraged to extract valuable insights from vast datasets. The comparative analysis highlights contextual differences in regulatory environments, infrastructure development, and technological landscapes that influence the adoption and implementation of telecom data analytics. In Africa, where the telecom landscape is dynamic and diverse, the review explores how data analytics is playing a crucial role in addressing connectivity challenges, optimizing network performance, and expanding telecommunications services. It also considers the impact of regulatory frameworks and investment climates on the deployment of data analytics solutions. In the USA, a mature telecom market with high technological adoption, the review investigates how data analytics is shaping decision-making processes, improving customer experiences, and contributing to the development of innovative services. It delves into the regulatory landscape, market dynamics, and the role of data analytics in maintaining a competitive edge. Throughout the review, the focus is on identifying best practices, lessons learned, and cross-regional insights that can inform the future trajectory of telecom data analytics. The abstract encapsulates the broader themes of the review, offering a glimpse into the critical role played by data analytics in shaping the telecom industry across Africa and the USA.

DATA SCIENCE: Data Visualization and Data Analytics in the Process of Data Mining

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).

DATA SECURITY IN BIG DATA: CHALLENGES, STRATEGIES, AND FUTURE TRENDS

In the dynamic landscape of big data analytics, this paper explores the critical dimension of data security, addressing challenges, strategies, and emerging trends. Recognizing the exponential growth and complexity of big data, the study delves into the multifaceted challenges posed by its volume, velocity, and variety. Through an extensive literature review, this paper provides an overview of existing studies on data security in big data environments, highlighting the current gaps in understanding. The paper then evaluates strategies employed to fortify data security, emphasizing encryption techniques, access controls, and authentication mechanisms. Drawing insights from real-world case studies, it showcases successful implementations of these strategies, providing practical examples for organizations navigating the intricacies of big data security. As technology continues to evolve, the paper investigates emerging trends in data security, such as the integration of artificial intelligence and machine learning for advanced threat detection, the potential of blockchain for enhancing data integrity, and the adoption of zero-trust architectures. The analysis includes a discussion on the implications of these technologies on the future of data security in big data environments. The methodology section outlines the research design and data collection methods, setting the foundation for the subsequent results and discussion. The results section summarizes key findings, addressing the effectiveness of current strategies and technologies in mitigating data security risks. The discussion section critically analyses the implications of these findings, identifying potential areas for future research and improvement. In conclusion, this paper provides a comprehensive examination of data security in the era of big data, offering valuable insights for practitioners, researchers, and policymakers. By addressing challenges, evaluating strategies, and anticipating future trends, the study contributes to the ongoing discourse surrounding the intersection of data security and big data analytics.

Predictive Analysis of Patient Risk of Death in ICU: A Bibliometric Analysis

This bibliometric analysis explores the synergy of artificial intelligence (AI), particularly machine learning, and biomedical signal processing in predicting patient mortality risk within the intensive care unit (ICU). Utilizing a comprehensive literature review, the study assesses the research landscape by applying these techniques to ICU data. Examining diverse data sources like vital signs and electronic health records, the analysis identifies trends and gaps in existing work, emphasizing AI’s potential for resource allocation and preventative care to enhance ICU outcomes. Structured within a bibliometric framework, the review encompasses methodological approaches, results, and discussions, while addressing clinical and ethical perspectives on mortality prediction. Challenges related to data, model performance, and fairness are evaluated through a bibliometric lens. The research questions underscore the importance of understanding past literature trends in predictive analysis for ICU patients. The review methodologically explores recent studies employing word representation models, impact assessments, and risk prediction of vital signs. Global research trends in AI for critical care are identified based on bibliographic data between 2013 and 2022. Noteworthy contributions, such as a sepsis dataset, are highlighted within the bibliometric analysis. In conclusion, this bibliometric analysis positions itself at the intersection of AI and critical care, emphasizing the importance of bibliographic data in understanding past trends, methodologies, and impactful contributions. It sets the stage for future directions in the evolving landscape of ICU predictive analytics within a bibliometric framework.

Enhancing Business Performance through Effective Data Analytics: A Comprehensive Study on Strategies, Tools, and Best Practices

This study explores the critical role of data analytics in enhancing business performance. The research delves into the evolving landscape of data analytics, examining how businesses can strategically integrate and apply analytics to gain a competitive edge. By investigating various tools and methodologies used in data analytics, the study aims to provide an understanding of the many approaches available to organizations. Focus on best practices that offers practical insights for optimizing the use of data analytics in different business contexts was also discussed. This research serves as a valuable resource for decision-makers, analysts, and practitioners seeking to navigate the complex terrain of data analytics and unlock its full potential for driving business success.

Predictive Analytics in Data Engineering: An AI Approach

Predictive analytics, as a cornerstone of data engineering, has witnessed a paradigm shift with the integration of Artificial Intelligence (AI) methodologies. This abstract provides an overview of the key themes explored in the paper titled “Predictive Analytics in Data Engineering: An AI Approach.” The paper delves into the transformative impact of AI on predictive analytics within the domain of data engineering. Traditional predictive analytics often relied on statistical models and historical data patterns to forecast future trends. The advent of AI technologies, particularly machine learning and deep learning, has revolutionized the predictive analytics landscape by enabling systems to autonomously learn and adapt from data. A central focus of the paper is the exploration of advanced AI algorithms in predictive analytics. Machine learning models, such as regression, decision trees, and ensemble methods, are examined for their efficacy in predictive modeling tasks. Additionally, the integration of deep learning architectures, known for their ability to capture intricate patterns in large datasets, is explored for enhancing predictive accuracy. The convergence of predictive analytics and AI introduces a dynamic dimension to data engineering workflows. The paper outlines how AI-driven predictive analytics not only enhances the accuracy of predictions but also automates feature extraction, identifies complex patterns, and adapts to evolving data structures. The synergy between AI and predictive analytics empowers data engineers to navigate the challenges posed by big data and unstructured datasets. Ethical considerations and interpretability in AI-driven predictive analytics are also scrutinized in the paper. As AI models become increasingly complex, ensuring transparency in decision-making processes and addressing biases are crucial for responsible deployment in real-world scenarios. The findings presented in this paper contribute to the evolving discourse on the integration of AI in predictive analytics within the realm of data engineering. By examining the practical implications, challenges, and ethical dimensions, the paper provides valuable insights for practitioners, researchers, and organizations aiming to harness the full potential of AI in predictive analytics to drive informed decision-making and innovation in data engineering workflows.

Revenue management: changes in the analytical landscape of the enterprise through improving the organization of accounting and analysis

Income management is an important concept in the system of managing the financial and economic activities of an enterprise. A solid revenue management strategy requires the use of analytics and available data to forecast product demand, make strategic pricing decisions, and maximize revenue.
 The article examines the dynamics of the income structure of enterprises and presents a sectoral analysis of companies according to the sectoral distribution of the market environment. The main trends are established and the main problems of the company's income management arising in conditions of uncertainty and risks are outlined. The paper presents the role of income in the management system of the enterprise, which directly affects the efficiency of the process of managing the economic activity of the enterprise.
 In the course of the study, ways of improving the formation and use of accounting and analytical information about the company's income for management purposes were proposed. The scheme of the organization of income analysis for the formation of analytical information is presented. Reasonable importance of management reporting, which is intended to provide understanding, clarity and direction of management activities. When considering the content of the management report, we recommend taking into account visualization forms and design layouts. The KPI dashboard is a visual tool that provides a centralized view of various data sources for effective tracking and optimization of goals. With the help of indicators and KPIs, companies can get an overview of their performance at an operational or strategic level.

YOLO and LSH-based video stream analytics landscape for short-term traffic density surveillance at road networks

YOLO and LSH-based video stream analytics landscape for short-term traffic density surveillance at road networks

Improving Credit Risk Management with Predictive Analytics

In the context of credit risk management within the banking industry, this in-depth investigation explores the landscape of predictive analytics, which is constantly evolving. For the purpose of providing a holistic understanding of the role that predictive analytics plays in modern banking, the study is based on a qualitative research approach and combines current literature with case studies from the actual world. Among the key developments highlighted by the evaluation are the following: the increasing importance of ethical and regulatory compliance; the democratisation of predictive analytics tools; the integration of predictive analytics across diverse banking activities; and the transition to advanced machine learning algorithms. It proves the usefulness of predictive analytics by showing that it can facilitate more accurate risk assessments, quicker decision-making, and better overall banking performance. Analyses that compare different contexts demonstrate that predictive models perform differently in each of those contexts, highlighting the significance of selecting models that are customised to the specific context. Nevertheless, there are considerable obstacles to overcome, including the quality of the data, the interpretability of the model, the shortage of personnel, ethical problems, and the fees associated with implementation. Predictive analytics has the potential to become a vital instrument for managing credit risk in the banking industry. It will provide more accurate risk assessments, more intelligent judgements, and more resilience.

ADVANCEMENT OF GAS CHROMATOGrAPHY IN QUANTITATIVE ANALYSIS

Gas chromatography (GC) is a versatile analytical technique with a rich history of advancements in quantitative analysis. This review explores the enduring relevance of GC, focusing on principles and instrumentation. GC’s ability to separate and quantify volatile compounds has solidified its place in environmental analysis, pharmaceuticals, food chemistry, and forensic science. With advanced instrumentation, sample preparation techniques, and quality control, GC continues to meet the demands of modern research. Recent innovations, including miniaturization, hyphenated techniques, and automation, enhance its precision and accessibility. This review highlights the challenges and prospects for GC in a changing analytical landscape, emphasizing sustainability and efficiency. Gas chromatography remains an indispensable tool for researchers, offering continued innovation and steadfast performance in quantitative analysis. Keywords: Sensitivity, Selectivity, Stationary Phase, Carrier Gas, Injector, Detectors, Column, Sample Introduction.

Harnessing the Power of Big Data: Challenges and Opportunities in Analytics.

Big Data analytics refers to the process of examining, processing, and extracting meaningful insights from large and complex datasets that are too vast and dynamic to be effectively managed and analyzed using traditional data processing tools and methods. It involves the application of various techniques, technologies, and algorithms to uncover patterns, trends, correlations, and valuable information within massive volumes of data. The era of Big Data has ushered in a transformative wave across various industries, offering unprecedented opportunities for organizations to glean valuable insights and drive informed decision-making. However, with this vast potential comes a myriad of challenges that must be addressed to fully harness the power of Big Data analytics. This paper delves into the multifaceted landscape of Big Data analytics, exploring both the challenges that impede its realization and the abundant opportunities it presents.[1] The challenges in harnessing Big Data analytics include issues related to data volume, velocity, variety, and veracity, as well as the complexities of data storage, processing, and privacy. Scalability, data quality, and the need for skilled personnel also pose significant obstacles. Conversely, the paper highlights the vast opportunities that Big Data analytics offers. It discusses the potential for improving business operations, enhancing customer experiences, and enabling data-driven innovation. Additionally, the paper explores the impact of Big Data analytics in diverse fields such as healthcare, finance, marketing, and cybersecurity. The human element in Big Data analytics is also scrutinized, emphasizing the importance of fostering a data-centric culture within organizations. The role of data scientists, analysts, and data stewards is pivotal in extracting meaningful insights from the data deluge. The benefits as well as challenges of Big Data Analytics will be discussed in this paper.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry–Based Secretomics

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.

Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets

The growing complexity and volume of financial data, driven by globalization and advancements in digital technologies, have significantly transformed decision-making processes in financial markets. This paper explores the integration of Artificial Intelligence (AI)-driven techniques in Big Data Analytics to enhance decision-making capabilities in the financial sector. AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), are reshaping the landscape of data analytics by providing more accurate predictions, uncovering market trends, and automating complex trading decisions. The study focuses on three core areas where AI-driven techniques have been effectively applied: predictive analytics, sentiment analysis, and algorithmic trading. Predictive models, such as support vector machines and neural networks, are employed to forecast market trends by analyzing vast amounts of historical and real-time financial data. Sentiment analysis, powered by NLP, is used to assess market sentiment from textual data, such as news articles and social media posts, providing additional context to market movements. Lastly, algorithmic trading utilizes AI algorithms to automate and optimize trading decisions based on predefined criteria, enhancing speed and precision in trade execution. Through a quantitative methodology, historical financial data from major stock exchanges were analyzed using AI models. The results demonstrate that AI-driven models, particularly neural networks and sentiment analysis tools, significantly improve prediction accuracy and market timing compared to traditional methods. The findings suggest that integrating AI into Big Data Analytics can lead to more effective decision-making, allowing financial institutions to better manage risks, seize opportunities, and maintain a competitive edge in increasingly volatile markets. Despite the benefits, the paper also addresses the challenges associated with the adoption of AI in financial markets, including issues related to data privacy, model interpretability, and regulatory compliance. As AI technologies continue to evolve, their role in financial markets will likely expand, but careful consideration of these challenges will be critical for their sustainable implementation.

Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines

Recent advances in Graph Neural Networks (GNNs) have changed the landscape of modern graph analytics. The complexity of GNN training and the scalability challenges have also sparked interest from the systems community, with efforts to build systems that provide higher efficiency and schemes to reduce costs. However, we observe that many such systems basically "reinvent the wheel" of much work done in the database world on scalable graph analytics engines. Further, they often tightly couple the scalability treatments of graph data processing with that of GNN training, resulting in entangled complex problems and systems that often do not scale well on one of those axes. In this paper, we ask a fundamental question: How far can we push existing systems for scalable graph analytics and deep learning (DL) instead of building custom GNN systems? Are compromises inevitable on scalability and/or runtimes? We propose Lotan, the first scalable and optimized data system for full-batch GNN training with decoupled scaling that bridges the hitherto siloed worlds of graph analytics systems and DL systems. Lotan offers a series of technical innovations, including re-imagining GNN training as query plan-like dataflows, execution plan rewriting, optimized data movement between systems, a GNN-centric graph partitioning scheme, and the first known GNN model batching scheme. We prototyped Lotan on top of GraphX and PyTorch. An empirical evaluation using several real-world benchmark GNN workloads reveals a promising nuanced picture: Lotan significantly surpasses the scalability of state-of-the-art custom GNN systems, while often matching or being only slightly behind on time-to-accuracy metrics in some cases. We also show the impact of our system optimizations. Overall, our work shows that the GNN world can indeed benefit from building on top of scalable graph analytics engines. Lotan's new level of scalability can also empower new ML-oriented research on ever-larger graphs and GNNs.

Transformation of Аnalytics of Large Databases in Procurement Management with the Development of Artificial Intelligence

The article offers an in-depth examination of the current state, as well as the growth trajectories, of the global market for big data analytics, with a specific focus on the subfield of procurement analytics. It goes beyond mere surface-level statistics to provide a nuanced understanding of market trends and potential future directions. This is explored not just in the context of the European Union but also extends to a detailed case study involving Ukraine, thereby offering a more global perspective. Furthermore, the article scrutinizes the various data sources that can be leveraged for making well-informed management decisions in the realm of procurement. It doesn’t stop at merely listing these sources but goes on to analyze their respective merits and limitations. In addition, the article provides real-world examples from Ukraine, showcasing the practical applications of data analytics in procurement processes, thereby grounding the theoretical discussions in empirical reality. The article also ventures into the burgeoning field of artificial intelligence (AI), outlining its transformative potential in procurement data analytics. It characterizes the myriad benefits that AI can bring to procurement management, from increased efficiency to more nuanced decision-making capabilities. To guide practitioners, the article proposes a detailed algorithmic workflow for employing AI in the analysis of data crucial for procurement decisions. This serves as a practical roadmap for organizations looking to integrate AI into their procurement strategies. However, the article is not blindly optimistic about the role of AI; it also brings to the fore the potential risks associated with employing artificial intelligence for the analysis of large and complex databases. This balanced approach adds a layer of caution to the otherwise optimistic narrative, making the article a comprehensive and nuanced contribution to the literature on procurement analytics and artificial intelligence. In sum, the article serves as a robust academic resource that traverses the landscape of procurement analytics, from market trends and macroeconomic impacts to the practicalities and potential pitfalls of AI integration.