Authors: Chen Zhang, Kaibo Wang, Fugee Tsung, and Miodrag Lovric
This comprehensive entry provides a deep dive into the concept of Big Data, contrasting it with Small Data and exploring its defining attributes, evolution, techniques, applications, and emerging challenges. Big Data is characterized by the 6Vs: volume, velocity, variety, veracity, value, and variation. It is not merely about massive data volume but also the complexity, real-time nature, and dynamism of data generation from sources such as IoT, social media, sensors, and industrial systems.
The article outlines two broad categories of techniques: data storage/management and data analysis. Technologies such as Hadoop, Spark, NoSQL, and in-memory computing are discussed as foundational to Big Data architecture. Analytics approaches—descriptive, diagnostic, predictive, and prescriptive—are presented within the DIKW framework, supported by AI, machine learning, deep learning, and statistical modeling.
The application spectrum is vast. In healthcare, Big Data enables disease prediction, drug discovery, and public health monitoring. In finance and economics, it supports risk modeling, algorithmic trading, and real-time market analysis. Energy systems benefit through smart grids and consumption analytics; transportation utilizes predictive maintenance and traffic optimization. Other domains include manufacturing (Industry 4.0), education, social media, scientific research, e-commerce, government operations, and personalized marketing.
The article also details challenges—data privacy, infrastructure limitations, budget constraints, talent shortages, and regulatory uncertainty. It critiques the overuse of hypothesis testing under Big Data regimes, warning against misinterpretation due to massive sample sizes and the Jeffreys–Lindley paradox. Recommended solutions include shifting focus to effect sizes, practical significance, and robust methods like bootstrapping, parallel computing, and Bayesian inference.
Looking forward, the future of Big Data is shaped by technologies like edge computing, 5G, hyperscale data centers, green computing, and trustworthy AI. These advancements promise to enhance the scalability, responsiveness, and sustainability of Big Data ecosystems worldwide.
For full exposition, applications, figures, and technical insights, refer to the complete article in the International Encyclopedia of Statistical Science.