Cited by 1,482Big Data Technologies and ApplicationsStatistical Methods and InferenceStatistical Methods and Applications

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Challenges of Big Data analysis

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Fan, Jianqing, Han, Fang, Liu, Han·2014·National Science Review

DOI 10.1093/nsr/nwt032PMC PMC4236847

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Abstract

Big Data bring new opportunities to modern society and challenges to data scientists. On the 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 paper gives 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 emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

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Self-citation

Authors: Fan, J., Han, F., & Liu, H.
Affiliations: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA;
Johns Hopkins University (US)
Princeton University (US)
ORCID: Han, Fang
Liu, Han
Venue: National Science Review
Publisher: Oxford University Press (OUP)
Published: 2014-02-05
Volume: 1
Issue: 2
Pages: 293-314
ISSN: 2053-714X (ISSN-L)
2095-5138
DOI: 10.1093/nsr/nwt032
PMC: PMC4236847
OpenAlex: W2114060717
Cited by: 1,482
References: 144
Citations by year: 2026: 86 · 2025: 132 · 2024: 142 · 2023: 172 · 2022: 118 · 2021: 122 · 2020: 125 · 2019: 157 · 2018: 162 · 2017: 120 · 2016: 90 · 2015: 40 · 2014: 15
FWCI: 165.47
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 973 d)
Locations: National Science Review (publishedVersion, cc-by)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
HighWire Press Open Archive (submittedVersion)
arXiv (Cornell University)
Funders: National Science Foundation (R01-GM072611, 1206464, 1332109, 1116730)
National Institutes of Health
National Institute of Mental Health (R01MH102339)
National Institute of General Medical Sciences (R01GM072611, R01GM100474)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: big data analytics, high-dimensional data, scalability, noise accumulation, spurious correlations, measurement error, statistical inference, computer architecture

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