Analysis of New Characteristics of Statistical Analysis in the Era of Big Data
DOI:
https://doi.org/10.53469/ijomsr.2025.08(09).03Keywords:
Big data, Statistical analysis, CharacteristicAbstract
With the overall advancement of China's scientific and technological prowess, the nation has fully entered the era of "Internet Plus." This paradigm shift has elevated big data to a pivotal role, profoundly influencing both daily life and professional landscapes. The meteoric rise of large-scale e-commerce platforms means individuals encounter and generate an ever-expanding volume of data daily. This data explosion has exerted a significant and dual-faceted impact on the field of statistics. On one hand, it presents unprecedented opportunities, catalyzing innovation within the discipline by providing massive, diverse datasets that enable more granular real-time analysis, predictive modeling, and insights into complex social and economic phenomena, thereby pushing the boundaries of traditional statistical methodologies. On the other hand, it introduces substantial challenges; the sheer volume, velocity, and variety of big data (the "3Vs") strain conventional data processing tools and statistical frameworks, demanding new techniques for data capture, storage, cleaning, and analysis. This evolution consequently places increasingly higher demands on talent within the statistics field, necessitating professionals who are not only well-versed in classical statistical theory but also proficient in data mining, machine learning, and computational programming. In response to these dynamics, statistics in the new era is demonstrating distinct new characteristics, such as a greater emphasis on data-driven decision-making, interdisciplinary integration with fields like computer science, and a focus on extracting meaningful patterns from unstructured data. It is precisely these emerging traits that form the basis for further research and analysis in this paper, aiming to deepen the understanding of statistics' evolving nature in the age of big data.
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