Big Data Foundations and Critiques

Definition of big data

Big data primarily refers to datasets that are too large or too complex for traditional data-processing software to capture, curate, manage, and process within a reasonable time. The term is not tied to a single fixed size; what counts as “big” depends on the tools and capabilities available, and the threshold keeps shifting as technology advances. Big data also emphasizes that the main focus is often on unstructured data (along with structured and semi-structured data), which traditional systems handle less effectively. Historically, big data has been associated with the “3Vs” (volume, variety, velocity), and later expanded to include “veracity” (reliability/quality of the data). In practice, modern usage of the term often centers on using advanced analytics—such as predictive analytics and user behavior analytics—to extract value, rather than merely on the dataset’s size. Big data analysis involves challenges across the data lifecycle, including capturing, storage, analysis, search, sharing, transfer, visualization, querying, updating, privacy, and ensuring trustworthy data sources.

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Big data vs business intelligence

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