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) offer 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 source. 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, that refers to the level of relaibility of data was thus added. Without sufficient investment in expertise for 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.