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Challenges of Big Data analysis
Open accessAbstract
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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“Overfitting is a key risk in this context, which means that a machine may learn a complete model that also explains the errors, consequently failing to generalize appropriately beyond its training data (Fan, Han, & Liu, 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Fan, Han, & Liu, 2014
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“In addition, the combination of datasets in the big data age needs to cope with a number of challenges listed in [20,61], for example efficient data representation, redundancy reduction and data compression, spurious correlations, and noise accumulation.”
From Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience (2016) · cites this paper as [61]
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Liu, P., Yuan, H., Ning, Y., Chakraborty, B., Liu, N., & Peres, M. A. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02427-8 · Cited by 59
Conceptions of Good Science in Our Data-Rich World
Elliott, K. C., Cheruvelil, K. S., Montgomery, G. M., & Soranno, P. A. (2016) · BioScience · doi:10.1093/biosci/biw115 · Cited by 43
Metadata
- 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)
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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