Challenges of Big Data Analysis

TL;DR

Big Data analysis offers new opportunities for uncovering complex patterns but also presents significant computational and statistical challenges that require innovative paradigms and careful methodological considerations.

Contribution

This paper provides an overview of Big Data features, discusses their impact on computational and statistical paradigms, and highlights the limitations of current methods due to incidental endogeneity.

Findings

High-dimensional data pose scalability challenges.

Incidental endogeneity can lead to invalid inferences.

Sparsest solutions in high-confidence sets are viable.

Abstract

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.