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Challenges of Big Data analysis | National Science Review | Oxford Academic
Source: academic.oup.com
Article summary:
1. Big Data brings new opportunities and challenges to data scientists.
2. Challenges include scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors.
3. New computational and statistical paradigms are needed to address these challenges.
Article analysis:
The article is generally reliable and trustworthy in its discussion of the challenges of Big Data analysis. It provides a comprehensive overview of the salient features of Big Data and how they impact on paradigm change on statistical and computational methods as well as computing architectures. The authors provide various perspectives on the Big Data analysis and computation, emphasizing the viability of the sparsest solution in high-confidence set. They also point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity, which can lead to wrong statistical inferences and consequently wrong scientific conclusions.
The article does not appear to have any biases or one-sided reporting; it presents both sides equally by discussing both the opportunities presented by Big Data as well as the challenges associated with it. It also does not contain any promotional content or partiality towards any particular approach or method for analyzing Big Data. Furthermore, possible risks associated with using Big Data are noted throughout the article, such as noise accumulation, spurious correlation, etc., which could lead to incorrect conclusions if not taken into account when analyzing data sets.
The only potential issue with this article is that it does not explore counterarguments or present evidence for some of its claims regarding exogenous assumptions in most statistical methods for Big Data being unable to be validated due to incidental endogeneity. However, this is a minor issue since the article still provides an overall comprehensive overview of the challenges associated with analyzing large data sets without making unsupported claims or missing points of consideration.