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
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.
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem."
Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on". Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large datasets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics.
Begin with the source’s own compact description: “Big data” is information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value. The dossier treats that line as a proposition to test through data, information and assets, not as a finished interpretation.
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The phrase “information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value” supplies a clear boundary for inquiry. It also exposes the unanswered questions: who defined that boundary, when it became stable and which sources sit outside it.
Stable identifiers, scientific names and standards terminology offer the best bridge between this overview and specialist evidence. The source revision retrieved here is dated Sep 16, 2026. The linked authority identifier is Q858810. The Library of Congress control number is sh2012003227. None of the 1 selected statements returned an explicit reference.
Scientific names, classifications and consensus can change while older terminology persists in catalogues and historical literature. The source lead contains qualifying language; that uncertainty should survive quotation, summary and reuse. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
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This entry incorporates text from “Big data” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.