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Data analysts — whether in industry, government, or academia – are faced with increasingly large datasets, or big data. Some examples of discourse on big data include its impact on marketing analytics [1], official statistics [2], and biomedical research [3]. However, when data actually becomes big is subjective. This can refer to a large number of records, a large number of measured variables, or both. Nonetheless, the growing number of data collection strategies, as well as increases in computational efficiency and storage, have resulted in big data being relatively cheap to obtain and manage.
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