Difference between revisions of "Analytics"
Jump to navigation
Jump to search
Karla.diel (talk | contribs) |
Karla.diel (talk | contribs) |
||
| Line 1: | Line 1: | ||
# Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.<ref><nowiki>https://www.gartner.com/en/information-technology/glossary/analytics</nowiki></ref> | # Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.<ref><nowiki>https://www.gartner.com/en/information-technology/glossary/analytics</nowiki></ref> | ||
| − | # Systematic computational transformation of enterprise data into insights, for the purpose of making better decisions<ref>''Accountabilities, Responsibilities, Authorities (ARA),'' ADM (Data, Innovation and Analytics)</ref> | + | <br /><br /># Systematic computational transformation of enterprise data into insights, for the purpose of making better decisions<ref>''Accountabilities, Responsibilities, Authorities (ARA),'' ADM (Data, Innovation and Analytics)</ref> |
<references /> | <references /> | ||
Revision as of 08:31, 30 March 2020
- Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.[1]
# Systematic computational transformation of enterprise data into insights, for the purpose of making better decisions[2]
