Introduction to Big Data
Data is an important resource. However, if you do not have the right means to process it, then there is not much that you can benefit from regarding its value. Nowadays every organisation today including Banking is data driven, because in order to make effective decisions data analytics and insights are essential. Big data primarily refers to the data sets that are too large or complex to be dealt with by complex data processing complex software. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, search and updating. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate manage and process data within a tolerable elapsed time.
Characteristics of Big Data Big data can be described by the following characteristics:
Volume: This is the amount of data that is produced or received by the company in a day. It refers to the quantity of generated and stored data. The size of the data determines the volume and potential insight, and whether it can be considered big data or not. The size of the data is usually in terabytes and petabytes. This can also present a big challenge because it would take an unreasonable amount of time to analyse the data if it is done manually.
Variety: Earlier the existing technologies were capable of handling structured data efficiently and effectively. However, with the change in the type and nature from structured to semi structure or unstructured challenged the existing tools and technologies. Data can come from various formats and sources.
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Diese Geschichte stammt aus der November 2024-Ausgabe von BANKING FINANCE.
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