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dc.contributor.authorDodson, Sean
dc.date.accessioned2020-12-08T16:26:22Z
dc.date.available2020-12-08T16:26:22Z
dc.identifier.urihttp://hdl.handle.net/20.500.12419/606
dc.description.abstractThis presentation covers several topics regarding Big Data such as what the definition of data is and how it is stored electronically. Starting with what data is and how data in large scale and complexity has been managed and analyzed historically using punch cards moving on to electronic tape to current digital storage methods. Big Data can be broken down into three defining categories: volume, velocity, and variety. Data gets generated in large quantities very quickly with large disparity of type. Text, audio, video, sensor data and other types all need information regarding their meaning to be made useful to those that seek to use it. Without the contextual information explaining that the numerical pattern 8125551234 is a phone number, the numerical data is useless. Once this data is made meaningful with the correct contextual information the next task is to take this data and sort and analyze it to find trends, patterns, irregularities or whatever may be useful to the interested parties and stake holders. Companies today are receiving larger quantities of more complex data than any other time in history. This trending increase in data production, the rate of production and variety of data type is only going to get more complex as time moves forward. This data is crucial to modern organizations if they want to remain competitive and profitable.
dc.relation.youtubehttps://youtu.be/13bvkjtXeI0
dc.titleBig Data, A Big Problem
html.description.abstractThis presentation covers several topics regarding Big Data such as what the definition of data is and how it is stored electronically. Starting with what data is and how data in large scale and complexity has been managed and analyzed historically using punch cards moving on to electronic tape to current digital storage methods. Big Data can be broken down into three defining categories: volume, velocity, and variety. Data gets generated in large quantities very quickly with large disparity of type. Text, audio, video, sensor data and other types all need information regarding their meaning to be made useful to those that seek to use it. Without the contextual information explaining that the numerical pattern 8125551234 is a phone number, the numerical data is useless. Once this data is made meaningful with the correct contextual information the next task is to take this data and sort and analyze it to find trends, patterns, irregularities or whatever may be useful to the interested parties and stake holders. Companies today are receiving larger quantities of more complex data than any other time in history. This trending increase in data production, the rate of production and variety of data type is only going to get more complex as time moves forward. This data is crucial to modern organizations if they want to remain competitive and profitable.
dc.contributor.affiliationUniversity of Southern Indiana
dc.eventHonors Student Symposium Fall 2020


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