Difference between revisions of "Portal:Business Intelligence/Introduction"

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[[Image:KPI.png|left|75px]] Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information<ref>http://en.wikipedia.org/wiki/Business_intelligence</ref>. While operational systems - such as {{IMSMANG}} and many others - are designed and optimised for data entry and updates, a BI system is read-only and designed for '''analysing''' and '''visualising''' data. The overall objective is to inform the decision making process, thus allow for evidence-based decision making. An important criterion of the underlying data is its '''quality''' - meaningful analysis can only be achieved if the input data is accurate, complete, and of known quality.
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Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information [http://en.wikipedia.org/wiki/Business_intelligence]. While operational systems - such as {{IMSMANG}} and many others - are designed and optimised for data entry and updates, a BI system is read-only and designed for '''analysing''' and '''visualising''' data. The overall objective is to inform the decision making process, thus allow for evidence-based decision making. An important criterion of the underlying data is its '''quality''' - meaningful analysis can only be achieved if the input data is accurate, complete, and of known quality.

Latest revision as of 20:56, 20 February 2020

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Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information [1]. While operational systems - such as IMSMANG and many others - are designed and optimised for data entry and updates, a BI system is read-only and designed for analysing and visualising data. The overall objective is to inform the decision making process, thus allow for evidence-based decision making. An important criterion of the underlying data is its quality - meaningful analysis can only be achieved if the input data is accurate, complete, and of known quality.