Data-driven Decision Research Articles

The desire path: unleashing expectations, discussing apprehensions, and proposing a way forward for GAI use in higher education

Purpose The purpose of this study is to demonstrate the desire path of using GAI in higher education, including expectations, apprehensions, and the way forward. Design/methodology/approach This qualitative study employs thematic analysis, scrutinizing 11 interviews with innovative higher education faculty. The methodology section details the utilization of GAI (Chat GPT) for conducting thematic analysis on interviews, showcasing academics' practical application of this technology for research purposes. Findings Stakeholders expect continuous improvement in technology, overdependence, advocate for gradual adjustment, and emphasize context-dependent technology utilization. Concerns encompass issues such as data reliability, ethical considerations, risks of undermining fundamental aspects, limitations in fully replacing human involvement, and worries about personal responsibility. Practical implications Recommendations include flexible regulations, data-driven decision-making, professional development, diverse stakeholder engagement, and promoting distributed responsibility. Originality/value This study offers valuable insights into the use of GAI in higher education, formulating policies that encourage innovation without hampering effectiveness.

Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers.

AI-Based Environmental Monitoring System with Farm Automation

This project presents an innovative approach to environmental monitoring in agricultural settings using AI-based solutions integrated with IoT technology. The system leverages data collected from various sensors such as soil moisture, gas sensors, and other environmental indicators to monitor the conditions in real-time. By utilizing an Arduino microcontroller, ESP module, and ThingSpeak server, the project is able to capture, store, and transmit sensor data effectively. The highlight of the project is the AI-based weather prediction module implemented using a Convolutional Neural Network (CNN) in Python, providing critical insights into weather conditions and allowing for data-driven decision-making. The system also includes a user-friendly web interface to display real-time monitoring and predictions, enabling farmers to better manage their resources and plan ahead for changes in environmental conditions

Data-Driven Decision-Making in Transportation Management Using AI

The rapid growth of data generation and the advancements in artificial intelligence (AI) have opened up new opportunities for data-driven decision-making in transportation management. This article presents a comprehensive review of the applications of AI in transportation, focusing on machine learning techniques, optimization algorithms, and predictive analytics. The article proposes a novel AI-based decision-making framework that integrates data preprocessing, AI modeling, and performance evaluation to address complex transportation challenges. A case study on traffic congestion management is conducted to demonstrate the effectiveness of the proposed framework in reducing travel times and improving system efficiency compared to traditional methods. The results highlight the potential of AI in optimizing transportation operations and supporting informed decision-making. However, the article also discusses the limitations and challenges of implementing AI-based decision-making in transportation, such as data quality, privacy concerns, and computational requirements. Future research directions, including transfer learning, integration with emerging technologies, and explainable AI, are identified to facilitate the widespread adoption of AI-based decision-making in transportation management. The findings of this article contribute to the growing body of knowledge on data-driven intelligent transportation systems and provide valuable insights for researchers, practitioners, and policymakers in the field.

Real-Time Industrial Data Analysis and Visualization

In the era of Industry 4.0, real-time data monitoring and analysis have become essential for enhancing operational efficiency and decision-making in industrial environments. This project focuses on the development of a comprehensive system for visualizing and analyzing real-time industrial data using a DHT11 sensor, which measures temperature and humidity. The collected data is transmitted and visualized through a dynamic web interface built on MyPHPAdmin, enabling users to access and interpret data seamlessly. The visualization aspect of the project aims to present the data in an intuitive format, allowing stakeholders to monitor environmental conditions effectively. Following the visualization phase, the data will undergo thorough analysis using advanced tools like Tableau and Excel, providing insights into patterns, trends, and anomalies in the industrial setting. This project not only demonstrates the integration of hardware and software technologies for real-time data management but also emphasizes the importance of data visualization and analysis in optimizing industrial processes. Ultimately, it serves as a foundation for further research and development in the field of industrial automation and data-driven decision-making.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference

Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions.

Dengue Outbreak Control and Prevention: A Case Study of Gampaha District, Sri Lanka

Dengue fever remains a persistent public health challenge in Gampaha District, Sri Lanka. Despite ongoing efforts by the Ministry of Healthcare, Nutrition & Indigenous Medicine, the district reported 10,212 cases in 2022, with significant hospital admissions and deaths in high-risk areas like MC Negombo, Gampaha, and Minuwangoda. The study examines Dengue control and prevention strategies implemented from November 2022 to March 2023. Data was collected from existing records, interviews with key informants, and field observations. Major challenges were identified and prioritised using evidence-based methodologies, including the "Five Whys" root cause analysis. The analysis revealed significant challenges categorised into program-related and non-program-related issues. Key issues include inadequate use of existing data, insufficient evidence-based control measures, and operational inefficiencies. The root cause analysis identified core problems such as lack of funding, poor planning, and limited community engagement. The study highlights the critical need for data-driven decision-making and enhanced collaborative efforts. Establishing a technical body, develop context-specific programs, and improve community involvement. Resource mobilisation for research and forecasting epidemics was also emphasised. This case study provides valuable insights into the challenges and strategic interventions necessary for effective Dengue control in the Gampaha District. Emphasising evidence-based decision-making and a comprehensive action plan underscores the importance of collaborative efforts to combat Dengue in the region.

Digital Social Media Influencers’ Impact on Beauty and Personal Care Purchases in South Africa

The South African beauty and personal care industry has been significantly impacted by digital technologies, with social media influencers (SMIs) playing a pivotal role in shaping consumer behaviour. Despite the significant role of SMIs, there is a notable research gap in optimising their influence to boost customer engagement and drive sales. This study addresses this gap by introducing a comprehensive digital framework integrating artificial intelligence (AI) and digitalisation to enhance influencer marketing (IM) strategies in South Africa’s beauty and personal care industry. The primary objective is to present a novel approach that digitally overlays traditional SMI marketing processes, enhancing engagement and effectiveness. A mixed-method approach was employed, integrating quantitative data from web scraping and sentiment analysis (SA) of Instagram posts with qualitative insights from the existing literature. This approach facilitated data-driven decision-making and optimised IM efficacy through technology-driven feedback analysis. The empirical findings confirmed that incorporating AI into influencer marketing (IM) campaigns significantly improves data collection and sentiment analysis, enhancing consumer engagement and purchase intent. This study underscores the transformative impact of digitalisation on marketing practices and the potential of digital tools to optimise SMI strategies in the beauty and personal care sector. This research also provides a valuable framework for marketers aiming to enhance IM campaigns, emphasising the strategic advantage of an automated response workflow based on feedback analysis. This study contributes to developing more impactful digital marketing strategies, maximising the effect of SMIs, and ensuring market competitiveness.

Machine learning­based approaches for enhancing human resource management using automated employee performance prediction systems

Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

Leveraging Spatial Analysis Techniques for Developing a Climate Resilient and Localized Urban Greening Plan for Pasig City

Abstract. A recent study has been conducted to evaluate the current state of green spaces in Pasig City, Philippines, and to propose a Climate Resilient and Localized Urban Greening Plan (CRLUGP) to address urban greening that is essential for creating sustainable cities, especially in the face of climate change and urban population growth. The study utilized Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) to map green spaces, analyze climate risks, and identify necessary corrective measures. The findings revealed that Pasig City currently only has 70.18 hectares of green space, which is significantly below the World Health Organization's recommended minimum of nine (9) square meters of green space per capita. Projections indicate that by 2032, the city will need 925.72 hectares of green space to meet the needs of its growing population. Additionally, the study identified a connection between low canopy cover and increased urban heat island effects, with the top 10 barangays showing a canopy cover of only 27.64%, well below the recommended 40%. Moreover, areas with limited green spaces were found to be at a higher risk of flooding, highlighting the importance of more vegetation for managing stormwater and mitigating climate-related risks. The study recommends a strategic approach to urban greening, leveraging spatial analysis for continuous monitoring and evaluation. By integrating spatial technologies and data-driven decision-making, Pasig City aims to establish a more sustainable and resilient urban environment for its residents.

Multitask scheduling on cloud additive manufacturing using NSGA-II

Purpose: Cloud manufacturing (CM) represents a new manufacturing paradigm that integrates distributed resources to provide on-demand services. The high consumer demand from various locations, coupled with the customizability and complexity of manufacturing, complicates task scheduling. In this context, 3D printers are crucial as innovative manufacturing technologies with significant potential in producing complex and custom products. Scheduling in CM falls under the non-deterministic polynomial time-hard category, where tasks must be scheduled and distributed rapidly. Considerations of distance, minimization of delays, and makespan become critical variables that must be considered. This research aims to schedule and distribute tasks in CM using the non-dominated sorting genetic algorithm II (NSGA-II) to minimize delays, reduce makespan, and decrease costs.Methodology: NSGA-II is employed to tackle the complexities of scheduling in CM. The strength of NSGA-II lies in its ability to determine optimal and efficient solutions for multiobjective problems. Tasks originating from requests at various locations are adjusted based on material parameters and dimensions and then distributed to providers while considering aspects such as makespan, delay minimization, and cost.Findings: The optimization results using NSGA-II demonstrate effective and efficient task distribution to providers. Across the four tested task distribution scenarios, the average computational time required was 5.59 seconds. Pareto analysis indicates a trade-off between various objective functions. Solutions with short distances tend to have increased maximum time and delays.Originality/value: NSGA-II is effective for task distribution with multiobjective considerations. Not all three objective functions can be optimized simultaneously, given the trade-offs between distance, maximum time, and lateness. The priority of the objective functions should be determined to achieve optimal results. If minimizing lateness is most important, the focus should be on points with low lateness values. Further development can be done by modifying the Pareto front to make data-driven decisions that consider these trade-offs.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

A Brief Study on Indian Startups- Analytic Research on the Success and Failure Factors of Swiggy, Boat, Doodhwala, And Shopx

India’s startup ecosystem has witnessed unprecedented growth in recent years, emerging as one of the most dynamic and fast-paced environments globally. This research paper examines the primary factors leading to the high failure rate of Indian startups, focusing on familiar challenges such as inadequate business models, poor product-market fit, cash flow problems, regulatory hurdles, and inefficient management. Through case studies of successful ventures like Swiggy, and BoAT, this paper highlights critical success factors that have enabled these companies to thrive, including data-driven decision-making, customer-centric strategies, adaptability, and strong strategic partnerships. The study also explores the pivotal role of venture capitalists (VCs) in shaping startup trajectories, from early seed funding to advanced growth stages. Additionally, the paper delves into the influence of Indian government initiatives, such as Startup India and the Atal Innovation Mission, in fostering a supportive ecosystem that encourages entrepreneurship and innovation. Keywords: Entrepreneur, Start-up, Economy, Future market, Challenges

Tailoring Learning Analytics for Success

Learning Analytics systems are emerging as a powerful tool for student success, optimising curricula, and informing data-driven decisions. However, these will not be achieved without effective implementation strategies tailored to institutional contexts. This study compares the learning analytics practices of five diverse Australian universities, offering a nuanced exploration of the similarities, differences, and patterns that characterise their adoption journeys. We will work to identify the factors that contribute to successful implementation and enhanced impact. Our findings emphasise the importance of aligning learning analytics initiatives with institutional contexts, student demographics, and unique needs, emphasising the necessity of tailored approaches that resonate with stakeholders and address specific challenges. Our intention is to provide a practical roadmap for Higher Education Institutions seeking to benefit from learning analytics, giving them the means to harness the full potential of these tools.

Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture

The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, and data-driven decision making. In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and thermal sensors mounted on drones with AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, and automated decision making, thus improving productivity with considerably reduced resource consumption. However, limitations include high costs of operation, limited UAV battery life, and the need for highly trained operators. The novelty of this study lies in the thorough analysis and comparison of all UAV-AI integration research, along with an overview of existing related works and an analysis of the gaps. Furthermore, practical solutions to technological challenges are summarized to provide insights into precision agriculture. This paper also discusses the barriers to UAV adoption and suggests practical solutions to overcome existing limitations. Finally, this paper outlines future research directions, which will discuss advances in sensor technology, energy-efficient AI models, and how these aspects influence ethical considerations regarding the use of UAVs in agricultural research.

The Impact of Information Technology On Data-Driven Decision Making In Human Resource Management

In the contemporary landscape of human resource management (HRM), the integration of information technology (IT) has revolutionized data-driven decision-making processes. This shift is characterized by the increased reliance on data analytics, cloud computing, and advanced software applications that enable HR professionals to harness vast amounts of data effectively. As organizations strive for agility and competitiveness, the role of IT in HRM has become pivotal, influencing various aspects such as recruitment, performance management, employee engagement, and talent development. The first major impact of IT on data-driven decision-making in HRM is the enhancement of recruitment processes. Traditional hiring methods often relied on subjective assessments and limited candidate data. However, IT solutions, such as Applicant Tracking Systems (ATS) and AI-driven recruitment tools, have enabled HR managers to analyze candidates’ qualifications, experiences, and fit with organizational culture based on data-driven metrics. This not only improves the quality of hires but also reduces time-to-fill positions and enhances overall recruitment efficiency. Performance management has evolved significantly due to the integration of IT. Performance appraisal systems now utilize real-time data analytics to track employee performance against key performance indicators (KPIs). This allows HR managers to make informed decisions about promotions, training needs, and workforce planning. The implementation of continuous feedback mechanisms, facilitated by IT platforms, fosters a culture of transparency and engagement, enabling employees to take ownership of their development. IT has transformed employee engagement strategies through the use of data analytics. Organizations now leverage employee surveys and feedback tools that provide insights into employee satisfaction and engagement levels. By analyzing this data, HR professionals can identify trends, address concerns proactively, and implement targeted initiatives that enhance workplace culture and retention rates. Such data-driven approaches ensure that decisions are aligned with employee needs and organizational objectives. The role of IT in talent development cannot be overstated. Learning Management Systems (LMS) and data analytics tools provide HR managers with the capability to assess skill gaps and monitor training effectiveness. By analyzing learning outcomes and employee progress, organizations can tailor development programs that align with both individual career paths and organizational goals. This strategic alignment is crucial for fostering a skilled workforce capable of meeting future challenges. Despite these benefits, the reliance on IT in HRM also poses challenges, particularly regarding data privacy and security. As organizations collect and analyze large volumes of employee data, they must navigate complex regulations and ethical considerations to protect sensitive information. This underscores the need for robust data governance frameworks that ensure compliance while fostering a culture of trust within the organization. In conclusion, the impact of information technology on data-driven decision-making in human resource management is profound. By leveraging data analytics and advanced IT tools, organizations can enhance their recruitment processes, improve performance management, boost employee engagement, and foster talent development.It is essential for HR professionals to address the challenges of data privacy and security to maximize the benefits of IT in HRM. As technology continues to evolve, the synergy between IT and HRM will play a critical role in shaping the future of work.

Utilizing Technology and Artificial Intelligence in Educational Administration to Enhance School Performance at Junior High School

This study aims to explore the use of technology and artificial intelligence (AI) in education administration to improve school performance. In the growing digital era, demands for efficiency and effectiveness in education administration are increasing, especially with the development of school information management systems (SIMS) and AI-based technologies. This research examines how these technologies can automate routine tasks such as class scheduling, student data management, as well as improve data-driven decision-making that is more accurate and efficient. The research method used in this study is descriptive qualitative, with the process of collecting data through in-depth interviews, observation, and document analysis directly from several schools that have adopted this technology. In analyzing the data, I used Miles and Hubberman's data triangulation technique in the form of data reduction, data analysis, and conclusion drawing. The results show that the implementation of technology and AI has improved operational efficiency, accelerated administrative processes, and facilitated the monitoring of student and staff performance. However, the study also found challenges in technology adoption, including limited infrastructure, resistance to change, and concerns regarding data privacy. To overcome these obstacles, a holistic strategy is needed, including the development of supportive policies, digital skills training for staff, and improvement of technology infrastructure in schools. This research concludes that although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

Primary healthcare as a strategy for eliminating hepatitis C: the METRIC toolkit.

This paper presents the development of the METRIC toolkit, aimed at enhancing primary healthcare interventions in the context of hepatitis C control, thus contributing to the World Health Organization's global strategy to achieve the elimination of the disease by 2030. At the global level, most people living with hepatitis C are unaware of their condition. As such, the eradication of hepatitis C necessitates comprehensive strategies within primary healthcare settings, as it provides an opportunity to reach the general population, facilitates the identification of potential patients who may be unfamiliar with hepatitis C, and guides them toward adequate care. Herein, we propose the METRIC toolkit as a means to optimize the efficiency and efficacy of healthcare services dedicated to hepatitis C control. The development of the METRIC toolkit was guided by a thorough review of pertinent literature, focusing on primary healthcare interventions in hepatitis C control. Key components were identified, encompassing systematic problem identification, solution formulation, outcome evaluation, and feedback integration. The METRIC toolkit represents a valuable resource for strengthening primary healthcare interventions in hepatitis C control. By fostering a culture of continuous improvement and data-driven decision-making, this framework holds promise in advancing the global agenda towards hepatitis C elimination. However, its successful application requires careful consideration of contextual factors and ongoing adaptation to local needs and circumstances.