Название: Applications of Data Analysis across Business Functions and Domains
Автор: Sayantani Roy Choudhury
Издательство: Emerald Publishing Limited
Год: 2026
Страниц: 214
Язык: английский
Формат: pdf
Размер: 20.7 MB

Applications of Data Analysis across Business Functions and Domains offers a comprehensive exploration of how data analytics is transforming decision-making and operational efficiency across industries. In an era defined by data-driven competition, this timely work equips readers with foundational knowledge of key analytical techniques―descriptive, diagnostic, predictive, and prescriptive―and examines their practical applications in core business functions such as marketing, finance, human resources, supply chain, and operations. It also addresses critical topics like data quality, governance, and ethics, providing a well-rounded understanding of the challenges and responsibilities that come with leveraging data effectively.

Drawing on real-world case studies and theoretical insights, author Sayantani Roy Choudhury extends its scope to diverse domains including healthcare, retail, manufacturing, and education. Readers will discover how data analytics is used to personalise marketing, forecast financial trends, optimise workforce strategies, and enhance supply chain performance. Emerging areas such as learning analytics in education and predictive maintenance in manufacturing are also explored, highlighting the growing influence of Industry 4.0 technologies. Designed for professionals, academics, and students alike, this is an essential resource for anyone seeking to harness the power of data to drive innovation and success across business landscapes.

In an era of information abundance, data extraction that makes sense is an art that marks success. Whether it is a company trying to forecast customer attrition, a public health researcher trying to track the outbreaks, or a government trying to allocate resources efficiently, interpreting and acting on data is the core. This interpretation is driven by two pillars: statistical methods and Machine Learning (ML) techniques.

Statistics, on the one hand, has been the backbone of interpreting data for more than a century. It is concerned with gathering, analyzing, and making inferences from data, particularly in environments with uncertainty. ML, on the other hand, which emerged from Artificial Intelligence and Computer Science, is concerned with creating algorithms that learn patterns from data and predict them. Together, these tools comprise a complete analytical toolkit. The Chapter 1 will present the concepts, uses, and differences of statistical and ML tools to give readers the essential knowledge necessary to further explore data analytics.

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