Nanotechnology in the life sciences, Год журнала: 2024, Номер unknown, С. 395 - 427
Опубликована: Янв. 1, 2024
Язык: Английский
World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 22(3), С. 1927 - 1934
Опубликована: Июнь 30, 2024
This paper explores the transformative role of predictive analytics in enhancing strategic decision-making and business performance. It delves into components analytics, including data mining, machine learning, statistical techniques. highlights its historical evolution technological enablers like big platforms, cloud computing, AI. The examines how improves profitability, efficiency, market share by providing actionable insights from raw data. also discusses emerging trends such as advancements AI, Internet Things (IoT), real-time while addressing associated risks privacy ethical considerations. conclusion underscores necessity adopting for sustainable growth competitive advantage today's data-driven environment.
Язык: Английский
Процитировано
23
International Journal of Management & Entrepreneurship Research, Год журнала: 2024, Номер 6(6), С. 1982 - 1996
Опубликована: Июнь 24, 2024
In the context of escalating environmental concerns and transition towards a greener economy, sustainable energy investments have emerged as pivotal area for financial growth innovation. This paper outlines strategic framework decision-making in investments, emphasizing transformative role big data. By integrating data analytics into investment process, stakeholders can enhance market analysis, risk assessment, performance monitoring, predictive modeling, leading to more informed effective strategies. The delves various sources, analytical tools, technologies that facilitate collection, processing, interpretation vast amounts information. Additionally, it presents case studies illustrating successful applications solar wind projects, highlighting best practices common challenges. discussion extends future trends, including advancements artificial intelligence machine learning, which are poised further revolutionize sector. concludes with recommendations developing data-driven approach, building robust infrastructures, fostering culture continuous learning adaptation. leveraging data, investors maximize impact their drive growth, contribute global transition. Keywords: Sustainable Energy Investments, Big Data Analytics, Strategic Financial Decision-Making, Market Analysis, Risk Assessment, Predictive Modeling.
Язык: Английский
Процитировано
17
Finance & Accounting Research Journal, Год журнала: 2024, Номер 6(7), С. 1178 - 1190
Опубликована: Июль 7, 2024
This paper explores the intricate dynamics of product strategy development and financial modeling within burgeoning fields AI agritech start-ups. It begins by delineating stages development—from idea generation market research to launch scaling—emphasizing customer-centricity, innovation, collaborative partnerships as pivotal drivers success. Financial techniques, ranging from basic revenue cost structures advanced scenario analysis risk mitigation, are examined for their role in guiding strategic decision-making ensuring sustainability. In sector, rapid advancements machine learning data analytics reshaping industries through intelligent automation predictive insights. Agritech, meanwhile, leverages technology optimize agricultural processes, enhance productivity, promote sustainable practices amid global challenges. Both sectors share synergies integrating technologies innovate offerings performance, albeit facing distinct challenges such regulatory compliance adoption. Practical examples illustrate how start-ups apply these insights refine strategies models, enhancing competitiveness scalability. The implications practice underscore importance adapting dynamics, leveraging technological innovations, fostering collaborations drive growth innovation. Keywords: AI, Product Strategy Development, Modeling, Start-Ups
Язык: Английский
Процитировано
16
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1434 - 1452
Опубликована: Июнь 14, 2024
Driving the transition to sustainable energy is a critical global imperative, and financial innovation plays pivotal role in accelerating this process. This paper examines intersection of innovation, big data, Environmental, Social, Governance (ESG) metrics advancing transition. By harnessing power data integrating ESG considerations into investment decisions, institutions can drive meaningful change towards more future. The begins by exploring concept transition, highlighting its importance, drivers, challenges. It then delves discussing examples opportunities it presents for driving Subsequently, significance understanding consumption patterns optimizing efficiency, along with influencing decisions corporate behavior. emphasized, focus on their synergistic potential investments informing decision-making processes. Case studies are presented illustrate successful applications sector. Finally, discusses challenges future directions, including regulatory considerations, technological advancements, collaboration. concludes underscoring importance continued calls collective action Keywords: Energy Transition, Financial Innovation, Big Data, Metrics, Sustainability, Investment Decisions, Sustainable Energy, Renewable Climate Change
Язык: Английский
Процитировано
14
International Journal of Applied Research in Social Sciences, Год журнала: 2024, Номер 6(6), С. 1215 - 1226
Опубликована: Июнь 13, 2024
The alignment of oil and gas industry practices with Sustainable Development Goals (SDGs) is imperative for fostering a sustainable future. This abstract provides an overview the strategies challenges associated this alignment. plays significant role in global energy supply, economic development, geopolitical dynamics. However, its operations often have adverse environmental, social, impacts, making SDGs essential. Challenges aligning include environmental degradation caused by extraction activities, social disparities oil-producing regions, regulatory complexities. To address these challenges, such as reducing carbon footprints, transitioning to renewable sources, engaging local communities, protecting human indigenous rights, diversification are crucial. Case studies companies successfully highlight best lessons learned. Impact assessments demonstrate positive outcomes aligned on conservation, well-being, development. Recommendations policy reforms, guidelines, stakeholder collaboration facilitate broader adoption practices. In conclusion, essential achieving development goals globally. calls concerted efforts from stakeholders, policymakers, civil society create more Keywords: Oil, Gas, Industry Practices, (SDGs).
Язык: Английский
Процитировано
13
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(7), С. 1666 - 1679
Опубликована: Июль 25, 2024
This paper explores frameworks for effective data governance, emphasizing the importance of robust policies, processes, roles, and metrics. It outlines best practices ensuring high quality, privacy, security while highlighting stakeholder engagement role technology. The also discusses implementation challenges, including organizational, technical, regulatory, cultural obstacles. presents tailored strategies various industries such as financial services, healthcare, retail, manufacturing, public sector. Future directions research include integration AI machine learning, evolving privacy regulations, challenges posed by big IoT. Effective governance is crucial managing risks, compliance, unlocking full potential assets across industries. Keywords: Data Governance, Quality Management, Privacy, Regulatory Compliance.
Язык: Английский
Процитировано
13
International Journal of Management & Entrepreneurship Research, Год журнала: 2024, Номер 6(6), С. 1936 - 1953
Опубликована: Июнь 13, 2024
This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, virtualization, API integration, streaming businesses achieve a comprehensive, unified view of their data. The enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, improved support. Despite challenges silos, quality issues, scalability, security, technical complexity, solutions like governance, cloud computing, analytics tools address these obstacles. Future trends, including artificial intelligence, machine learning, edge block chains, fabric, promise to further transform e-commerce. Ultimately, are essential for companies stay competitive, optimize operations, satisfaction. In fast-paced competitive world e-commerce, ability make quick, informed decisions is crucial success. Business Intelligence (BI) plays vital role by transforming vast amounts into actionable that drive strategic operational efficiency. chain, Case studies from leading illustrate practical benefits implementing techniques, showcasing significant improvements efficiency Keywords: Intelligence, E-commerce Analytics, Data Integration, Real-Time Insights.
Язык: Английский
Процитировано
12
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1469 - 1487
Опубликована: Июнь 25, 2024
In the rapidly evolving landscape of digital transformation, cloud computing has emerged as a pivotal technology enabling businesses to achieve unparalleled scalability and operational flexibility. This review explores transformative impact advanced technologies on business operations, highlighting key innovations their implications for organizational growth efficiency. Cloud offers dynamic scalable environment where resources can be provisioned managed on-demand, allowing respond swiftly changing market conditions customer demands. By leveraging infrastructure, companies scale operations seamlessly without need significant upfront investments in physical hardware. flexibility not only reduces capital expenditure but also enhances ability innovate adapt competitive marketplace. Advanced technologies, such multi-cloud hybrid solutions, further augment by organizations optimize IT environments. Multi-cloud strategies allow distribute workloads across multiple providers, mitigating risks associated with vendor lock-in ensuring high availability redundancy. Hybrid which integrate on-premises infrastructure public private clouds, provide balanced approach managing sensitive data while benefiting from cloud. Moreover, cloud-native like containerization serverless have revolutionized application development deployment. Containers encapsulate applications dependencies, consistency different environments facilitating rapid Serverless reviews underlying developers focus solely code, thus accelerating cycle reducing overhead. The integration analytics artificial intelligence (AI) capabilities. platforms offer robust tools AI services that process vast amounts real-time, providing actionable insights predictive decision-making. empowers improve experiences, drive strategic initiatives data-driven precision. conclusion, are instrumental transforming harnessing power cloud, greater agility, cost efficiency, innovation, positioning themselves sustained advantage age. As continue evolve, potential redefine economic value will expand, making adoption critical imperative modern enterprises. Keywords: Operational Flexibility, Transforming, Business Scalability, Advanced, Computing Technologies.
Язык: Английский
Процитировано
11
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(7), С. 1680 - 1694
Опубликована: Июль 25, 2024
In the era of data-driven decision-making, ensuring data quality is paramount for organizations seeking to leverage their assets effectively. This paper explores comprehensive strategies enhancing through robust governance, methodologies, tools, and continuous improvement techniques. It highlights critical dimensions quality, including accuracy, completeness, consistency, timeliness, validity, uniqueness. discusses various assessment techniques, such as profiling, auditing, metrics. The also examines role cleansing, enrichment, integration, interoperability in maintaining high quality. Additionally, it provides an overview leading management evaluation criteria, best practices implementation. Finally, underscores importance monitoring, feedback loops, root cause analysis, fostering organization's culture. By adopting these strategies, can ensure reliability integrity data, improved business outcomes. Keywords: Data Quality, Governance, Profiling, Cleansing, Continuous Improvement.
Язык: Английский
Процитировано
11
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1358 - 1373
Опубликована: Июнь 7, 2024
This paper explores the application of predictive analytics and machine learning techniques to enhance credit assessment lending practices. By leveraging alternative data sources, such as mobile phone usage, social media activity, transactional records, models can provide more accurate risk evaluations for individuals with limited traditional financial histories. The study demonstrates efficacy these through empirical analysis, showcasing their potential reduce default rates while increasing approval applicants. Furthermore, discusses ethical considerations biases associated use non-traditional in scoring. findings underscore transformative impact fostering inclusion, offering practical insights policymakers, institutions, technology developers aiming bridge gap under banked communities. delves into enhancing inclusion by improving access populations. Traditional scoring methods often fail accurately assess creditworthiness lacking conventional histories, thereby excluding a significant portion population from services. incorporating sources interactions, utility payments, offer comprehensive precise evaluations. research methodology involves developing testing various algorithms, including decision trees, random forests, neural networks, predict creditworthiness. are trained validated on datasets that include both sources. performance is measured against standard metrics accuracy, precision, recall, area receiver operating characteristic (ROC) curve. Empirical results indicate utilizing significantly outperform methods, leading higher applicants maintaining or management standards. Keywords: Financial, Inclusion, Predictive, Analytics, Machine Learning, Alternative Data.
Язык: Английский
Процитировано
8