Data to Decision-Making: An Analysis of Business Analytics Applications
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| Izdano u: | International Journal of Communication Networks and Information Security vol. 16, no. 3 (Sep 2024), p. 374 |
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| Glavni autor: | |
| Izdano: |
Kohat University of Science and Technology (KUST)
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 100 | 1 | |a Garg, Raj Kumar |u Assistant Professor, Department of IT & DA, New Delhi Institute of Management, Delhi, India Email ID - rajkumar.garg@ndimdelhi.org | |
| 245 | 1 | |a Data to Decision-Making: An Analysis of Business Analytics Applications | |
| 260 | |b Kohat University of Science and Technology (KUST) |c Sep 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the ever-evolving business landscape, leveraging data for decision-making is crucial for staying competitive and improving operational efficiency. This paper, titled "Data to Decision-Making: An Analysis of Business Analytics Applications" explores how business analytics acts as a vital bridge between raw data and actionable insights, transforming strategies and operations across various sectors. Business analytics utilizes advanced tools and methods to examine data, uncover patterns, and provide insights that aid in making well-informed decisions. The paper provides an in-depth look at the different types of business analytics-descriptive, predictive, and prescriptive-and their specific applications. Descriptive analytics focuses on summarizing and interpreting past data to understand previous performance. Predictive analytics uses statistical techniques and machine learning to forecast future trends and outcomes. Prescriptive analytics takes it further by offering actionable recommendations based on data insights to guide strategic decisions and planning. The research underscores the application of business analytics across multiple sectors, such as marketing, finance, operations, human resources, healthcare, retail, sports, and e-commerce. In the marketing domain, business analytics is instrumental in customer segmentation, assessing campaign effectiveness, and conducting market basket analysis, which results in more focused and efficient marketing strategies. In finance, it is crucial for managing risks, detecting fraud, and forecasting financial trends, contributing to the stability and development of financial institutions. In operational contexts, business analytics enhances supply chain optimization, inventory management, and quality control, resulting in improved efficiency and reduced costs. Human resources departments benefit from analytics through better talent acquisition, employee performance analysis, and strategic workforce planning. The healthcare sector uses analytics for optimizing patient care, predicting disease outbreaks, and improving hospital operations.Retailers leverage analytics for sales forecasting, customer behavior analysis, and store layout optimization, driving enhanced customer experiences and increased sales. In sports, analytics supports performance evaluation, injury prevention, and fan engagement, contributing to better team performance and fan satisfaction. E-commerce businesses use analytics for personalizing user experiences, dynamic pricing, and analyzing customer lifetime value, which helps in maximizing revenue and customer loyalty. The paper also discusses the challenges of implementing business analytics, including concerns about data privacy, the complexity of integrating analytics with existing systems, and the demand for specialized skills and training. Additionally, it explores future trends in business analytics, including advancements in artificial intelligence, real-time analytics, and the development of more sophisticated predictive models. In conclusion, this research underscores the transformative power of business analytics in enabling data-driven decision-making across various industries. By leveraging analytics effectively, organizations can derive valuable insights, streamline operations, and meet strategic goals. This paper seeks to clarify how business analytics can be utilized to make impactful decisions, thereby fostering organizational success and growth. | |
| 653 | |a Performance evaluation | ||
| 653 | |a Injury prevention | ||
| 653 | |a Data mining | ||
| 653 | |a Human performance | ||
| 653 | |a Data analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Employee benefits | ||
| 653 | |a Customers | ||
| 653 | |a Sales | ||
| 653 | |a Big Data | ||
| 653 | |a Marketing | ||
| 653 | |a Prediction models | ||
| 653 | |a Power | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Electronic commerce | ||
| 653 | |a Algorithms | ||
| 653 | |a Inventory management | ||
| 653 | |a Inventory | ||
| 653 | |a Workforce planning | ||
| 653 | |a Finance | ||
| 653 | |a Quality control | ||
| 653 | |a Human resources | ||
| 653 | |a Success | ||
| 653 | |a Trends | ||
| 653 | |a Analytics | ||
| 653 | |a Professional development | ||
| 653 | |a Cost benefit analysis | ||
| 653 | |a Data processing | ||
| 653 | |a Privacy | ||
| 653 | |a Health care industry | ||
| 653 | |a Brand loyalty | ||
| 653 | |a Customer satisfaction | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Health care | ||
| 653 | |a Epidemics | ||
| 653 | |a Supply chains | ||
| 653 | |a Business analytics | ||
| 653 | |a Forecasting | ||
| 773 | 0 | |t International Journal of Communication Networks and Information Security |g vol. 16, no. 3 (Sep 2024), p. 374 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3108399809/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3108399809/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3108399809/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |