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''' Dark data''' | ''' Dark data''' | ||
'''dusty data''' | |||
Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. | |||
In an industrial context, dark data can include information gathered by sensors and telematics. | |||
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also known as “dusty data,” is any data that typically is not used – and in many cases not even stored – and therefore does not interact with other regular forms of data. Typically it is the digital data amassed throughout the enterprise from intermediate layers like network devices, system logs, and even emails and other unstructured sources. Many times, companies don’t even realize the powerful insights inherent in that data. Dark analytics identifies opportunities in this hidden data – unstructured, untapped, and/or untagged information including that found in server log files and mobile geolocation data – to provide valuable new customer and/or business insights. | also known as “dusty data,” is any data that typically is not used – and in many cases not even stored – and therefore does not interact with other regular forms of data. Typically it is the digital data amassed throughout the enterprise from intermediate layers like network devices, system logs, and even emails and other unstructured sources. Many times, companies don’t even realize the powerful insights inherent in that data. Dark analytics identifies opportunities in this hidden data – unstructured, untapped, and/or untagged information including that found in server log files and mobile geolocation data – to provide valuable new customer and/or business insights. | ||
<small> | <small> | ||
[https://en.wikipedia.org/wiki/Dark_data Source : Wikipedia ] | |||
[https://www.accenture.com/us-en/applied-intelligence-glossary Source : Accenture - applied intelligence glossary ] | [https://www.accenture.com/us-en/applied-intelligence-glossary Source : Accenture - applied intelligence glossary ] | ||
[[Catégorie:Vocabulary]] | |||
[[Catégorie:Intelligence artificielle]] | |||
[[Catégorie:Accenture]] | |||
Version du 21 mai 2020 à 08:59
en construction
Définition
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Dark data
dusty data
Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics.
also known as “dusty data,” is any data that typically is not used – and in many cases not even stored – and therefore does not interact with other regular forms of data. Typically it is the digital data amassed throughout the enterprise from intermediate layers like network devices, system logs, and even emails and other unstructured sources. Many times, companies don’t even realize the powerful insights inherent in that data. Dark analytics identifies opportunities in this hidden data – unstructured, untapped, and/or untagged information including that found in server log files and mobile geolocation data – to provide valuable new customer and/or business insights.
Contributeurs: Imane Meziani, wiki
