Extracting meaningful insights from massive datasets to help guide business decisions requires specialized skills in data analysis. Unfortunately, the supply of these skills does not meet the demand, due to the massive amount of data generated by society each day. This leaves businesses with a large amount of unanalyzed data that could have been used to support business decision making. Automating the process of analyzing this data would help address many companies' key challenge of a lack of appropriate analytical skills. This paper examines the process and challenges in automating this analysis of data. Central challenges include removing outliers without context, transforming data to a format that is compatible with the analysis method that will be used, and analyzing the results of the model.
| Identifer | oai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:cmc_theses-2393 |
| Date | 01 January 2016 |
| Creators | Holmgren, Rachelle |
| Publisher | Scholarship @ Claremont |
| Source Sets | Claremont Colleges |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CMC Senior Theses |
| Rights | © 2016 Rachelle L. Holmgren |
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