big data
Sign in to saveinformation 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.
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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.