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big data

information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value

AI overview

Big data refers to extremely large collections of information that come in so many different types and arrive so quickly that traditional tools can't easily handle them. It matters because specialized technology and methods can transform this overwhelming amount of data into useful insights that wouldn't be possible to extract otherwise.

AI-generated from the Wikipedia summary — may contain errors.

Research

57,225 papers

Big data and artificial intelligence in cancer research.ReviewTrends in cancer · 2024Wu X, Li W, Tu HDOI: 10.1016/j.trecan.2023.10.006
Big Data in Surgery.ReviewThe Surgical clinics of North America · 2023Prien C, Lincango EP, Holubar SDDOI: 10.1016/j.suc.2022.12.002
Big Data in Ophthalmology.ReviewAsia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2020Cheng CY, Soh ZD, Majithia S et al.DOI: 10.1097/APO.0000000000000304
Privacy in the age of medical big data.ReviewNature medicine · 2019Price WN 2nd, Cohen IGDOI: 10.1038/s41591-018-0272-7
Big data and reference intervals.ReviewClinica chimica acta; international journal of clinical chemistry · 2022Yang D, Su Z, Zhao MDOI: 10.1016/j.cca.2022.01.001
Understanding Big Data in Neurosurgery.ReviewAdvances in experimental medicine and biology · 2024Beheshti A, Alinejad-Rokny H, Suero Molina E et al.DOI: 10.1007/978-3-031-64892-2_10
Big Data in Cardiology.ReviewAnatolian journal of cardiology · 2019Okuyan EDOI: 10.14744/AnatolJCardiol.2019.83738
Big Data Approaches in Heart Failure Research.ReviewCurrent heart failure reports · 2020Lanzer JD, Leuschner F, Kramann R et al.DOI: 10.1007/s11897-020-00469-9

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Wikidata facts

Image: Big Data ITMI model with topics.jpg

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Commons category: Big data
WordLift URL: data.thenextweb.com/tnw/entity/big_data
Wikimedia import URL: en.wikipedia.org/wiki/Big_data

Sources (7)

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Article

A diagram of the generation and common application of big data Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.

Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracity, which refers to the level of reliability of data, was thus added. Without sufficient investment in expertise to ensure big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data.