Business Data Analytics
Written by Rajesh Singh,Anita Gehlot,Arpan Kumar Kar,Valentina Emilia Balas,Shahab Shamshirband
89 pages, about 2 hours of reading
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Themes, characters and key ideas in Business Data Analytics, written by Chaptra AI.
- about 8 hours
- advanced
- analytical
- informative
- scholarly
This book, "Business Data Analytics," serves as the proceedings for the First International Conference on Business Data Analytics (ICBDA 2022), held in Dehradun, India. It compiles six rigorously peer-reviewed full papers, selected from 107 submissions, showcasing original research from data scientists, machine learning experts, and data specialists worldwide. The conference and subsequent publication aim to foster knowledge exchange and collaboration across three core sub-categories: Predictive Modelling and Data Analytics, Decision Analytics and Support System, and Business Data Analytics, providing a snapshot of current academic and practical advancements in the field. Despite its brevity, the collection offers valuable insights into the contemporary challenges and solutions in data-driven business strategy.
“Specific quotes are not applicable as this is a collection of academic papers, not a narrative work. However, the overarching sentiment conveyed is the transformative potential of data: 'Unlocking the power of data for informed business decisions and strategic advantage remains the central challenge and opportunity.' This reflects the core mission of the research presented.”
Key themes
- Predictive Modelling and Data Analytics
- This theme focuses on the development and application of statistical and machine learning models to forecast future outcomes, identify patterns, and extract actionable insights from data. It encompasses various techniques such as regression, classification, clustering, and time series analysis, often with an emphasis on improving accuracy, efficiency, and interpretability in business contexts. The research explores how these models can be optimized for specific business problems, from sales forecasting to risk assessment.
- Business Data Analytics
- This overarching theme encompasses the broader application of data analytics principles and techniques to solve real-world business problems and drive strategic value. It integrates aspects of predictive modeling and decision support with a focus on specific industry challenges, organizational change management, and the economic impact of data-driven strategies. It often considers the entire data lifecycle, from data collection and governance to interpretation and communication of results to non-technical stakeholders, emphasizing the business context and outcomes.
- Decision Analytics and Support System
- This theme explores how data analytics can be integrated into decision-making processes and how systems can be designed to support intelligent choices. It covers the creation of tools, dashboards, and methodologies that enable managers and executives to make informed, data-driven decisions. Research in this area often considers the human-computer interaction aspects, the integration of analytics with business intelligence platforms, and the impact of these systems on organizational performance and strategy.
Worth discussing
Given the rapid evolution of data analytics, what are the most pressing ethical considerations that research in this field should address?
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