BIG DATA ANALYTICS TOOLS: A BIBLIOMETRIC LITERATURE REVIEW
dc.access.option | Open Access | |
dc.contributor.advisor | Drake, John R. | |
dc.contributor.author | Jones, Holden P. | |
dc.contributor.department | Management Information Systems (MIS) | |
dc.date.accessioned | 2016-06-14T14:43:20Z | |
dc.date.available | 2016-06-14T14:43:20Z | |
dc.date.created | 2016-05 | |
dc.date.issued | 2016-04-29 | |
dc.date.submitted | May 2016 | |
dc.date.updated | 2016-06-14T13:44:58Z | |
dc.degree.department | Management Information Systems (MIS) | |
dc.degree.discipline | Management Information Systems | |
dc.degree.grantor | East Carolina University | |
dc.degree.level | Undergraduate | |
dc.degree.name | BSBA | |
dc.description.abstract | Analytics as a field is rapidly growing because of businesses need to tackle increasingly large and complex datasets to gain a competitive edge in the business environment. Currently there are two distinct types of analytics technologies: proprietary and open-source. Each has their distinct strengths and weaknesses, but which is relevant in today business environment? Questions like this are important for businesses wanting utilize these technologies in order to become larger and more efficient. This research answered this question and it was found that both proprietary and open-source technologies are equally relevant in current analytics research. Because of this, businesses should be more aware of the analytics field and how both types of technologies could benefit their current operations and should strategically utilize both types to meet their specific data needs. | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/10342/5615 | |
dc.publisher | East Carolina University | |
dc.subject | Analytics, Big Data, Information Systems | |
dc.title | BIG DATA ANALYTICS TOOLS: A BIBLIOMETRIC LITERATURE REVIEW | |
dc.type | Honors Thesis | |
dc.type.material | text |
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