Big data: Difference between revisions
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For example, anti-fraud technology in banks can analyse how often bank customers log into their account, where they usually log in from, and how quickly they type in their user name and password. | For example, anti-fraud technology in banks can analyse how often bank customers log into their account, where they usually log in from, and how quickly they type in their user name and password. | ||
This process is also known as ''data mining'', or ''knowledge discovery in data''. | |||
== See also == | == See also == | ||
*[[ | * [[Data]] | ||
*[[Data lake]] | |||
*[[Data mining]] | |||
*[[Data science]] | |||
* [[Database]] | |||
* [[Datasphere]] | |||
*[[General Data Protection Regulation]] | |||
*[[Granular]] | |||
*[[Operational risk]] | *[[Operational risk]] | ||
| Line 14: | Line 24: | ||
[[Category:Manage_risks]] | [[Category:Manage_risks]] | ||
[[Category:Risk_frameworks]] | [[Category:Risk_frameworks]] | ||
Latest revision as of 21:10, 4 October 2023
Big data refers to the large volumes of historically unstructured information held by organisations.
Big data technology interrogates this previously unstructured information to produce more useful summarised and selected data and analysis.
For example, anti-fraud technology in banks can analyse how often bank customers log into their account, where they usually log in from, and how quickly they type in their user name and password.
This process is also known as data mining, or knowledge discovery in data.
