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IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics
BACKGROUND: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080525/ https://www.ncbi.nlm.nih.gov/pubmed/27729304 http://dx.doi.org/10.2196/publichealth.5810 |
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author | Hoyt, Robert Eugene Snider, Dallas Thompson, Carla Mantravadi, Sarita |
author_facet | Hoyt, Robert Eugene Snider, Dallas Thompson, Carla Mantravadi, Sarita |
author_sort | Hoyt, Robert Eugene |
collection | PubMed |
description | BACKGROUND: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. OBJECTIVE: To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. METHODS: The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. RESULTS: Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. CONCLUSIONS: IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. |
format | Online Article Text |
id | pubmed-5080525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-50805252016-11-07 IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics Hoyt, Robert Eugene Snider, Dallas Thompson, Carla Mantravadi, Sarita JMIR Public Health Surveill Original Paper BACKGROUND: We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. OBJECTIVE: To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. METHODS: The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. RESULTS: Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. CONCLUSIONS: IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. JMIR Publications 2016-10-11 /pmc/articles/PMC5080525/ /pubmed/27729304 http://dx.doi.org/10.2196/publichealth.5810 Text en ©Robert Eugene Hoyt, Dallas Snider, Carla Thompson, Sarita Mantravadi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 11.10.2016. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Hoyt, Robert Eugene Snider, Dallas Thompson, Carla Mantravadi, Sarita IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title | IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title_full | IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title_fullStr | IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title_full_unstemmed | IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title_short | IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics |
title_sort | ibm watson analytics: automating visualization, descriptive, and predictive statistics |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080525/ https://www.ncbi.nlm.nih.gov/pubmed/27729304 http://dx.doi.org/10.2196/publichealth.5810 |
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