{"178181":{"#nid":"178181","#data":{"type":"news","title":"Big Findings in Big Data","body":[{"value":"\u003Cp\u003EImagine a back-office banking employee hard at work at data analysis. Using a spreadsheet, she pores over the latest extraction from the \u201cbig data\u201d of total transactions. Her focus: a tiny subset of seemingly routine banking transactions that may be the latest entries in an elaborate, multi-continent money-laundering scheme. Happily for the bank employee, a complex software program using automated machine learning has already culled through the vast universe of potentially suspicious transactions. What once took dozens of investigators many months to do by hand, the artificial intelligence technology does\u2014with fewer errors\u2014in days.\u003C\/p\u003E\u003Cp\u003ETo Coca-Cola Chair in Engineering Statistics Jeff Wu, this kind of big data mining is only one example of the potential in exploring the vast store of information accumulated by millions of business and consumer transactions in modern life. The banking example is a real one, developed by Wu and colleagues, including a senior vice president with Bank of America, which later commercialized the product and used it to save millions of dollars through better identification of money-laundering fraud.\u003C\/p\u003E\u003Cp\u003E\u201cWe have had big data since the days of the NCR cash register and the automotive assembly line,\u201d says Wu. \u201cBut in the early years, retailers and manufacturers were not thinking about how to use it.\u201d Today, with huge quantities of data collected and stored via the Internet, the challenge is no longer on collecting data, but on figuring out ways to use it for better decision-making in a wide range of fields. \u201cWe need the data to make sense,\u201d he says. \u201cWe have data collected by Google, Amazon, Yahoo, Facebook\u2014what can we do with it? It\u2019s not just a computer science challenge; it\u2019s a statistical and industrial engineering challenge as well.\u201d\u003C\/p\u003E\u003Cp\u003EThis article first appeared in the 2012 edition of the \u003Ca href=\u0022http:\/\/issuu.com\/isyealumnimagazine\/docs\/2012\u0022\u003EISyE Alumni Magazine\u003C\/a\u003E.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJeff Wu discusses big data and the potential in exploring the vast store of information accumulated by millions of business and consumer transactions in modern life.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":"","uid":"27511","created_gmt":"2012-12-18 10:57:40","changed_gmt":"2016-10-08 03:13:26","author":"Ashley Daniel","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2012-12-18T00:00:00-05:00","iso_date":"2012-12-18T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"178211":{"id":"178211","type":"image","title":"(L to R) Jan Shi, Carolyn J. Stewart Chair; Jeff Wu, Coca-Cola Chair in Engineering Statistics; and Ming Yuan, Coca-Cola Junior Professor, research how to successfully use massive data sets to help transform the way we do business.","body":null,"created":"1449179039","gmt_created":"2015-12-03 21:43:59","changed":"1475894825","gmt_changed":"2016-10-08 02:47:05","alt":"(L to R) Jan Shi, Carolyn J. Stewart Chair; Jeff Wu, Coca-Cola Chair in Engineering Statistics; and Ming Yuan, Coca-Cola Junior Professor, research how to successfully use massive data sets to help transform the way we do business.","file":{"fid":"195952","name":"jeffwugroup_final.jpg","image_path":"\/sites\/default\/files\/images\/jeffwugroup_final_0.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/jeffwugroup_final_0.jpg","mime":"image\/jpeg","size":496424,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/jeffwugroup_final_0.jpg?itok=R1zymnEz"}}},"media_ids":["178211"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[{"id":"145","name":"Engineering"}],"keywords":[{"id":"53461","name":"Bank of America"},{"id":"53451","name":"big data mining"},{"id":"559","name":"Coca Cola"},{"id":"9106","name":"Engineering Statistics"},{"id":"109","name":"Georgia Tech"},{"id":"426","name":"isye"},{"id":"7879","name":"Jeff Wu"},{"id":"53441","name":"money-laundering"},{"id":"169545","name":"Stewart School of Industrial \u0026 Systems Engineering"}],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:barbara.christopher@isye.gatech.edu\u0022\u003E\u003Cstrong\u003EBarbara Christopher\u003C\/strong\u003E\u003C\/a\u003E\u003Cbr \/\u003EIndustrial and Systems Engineering\u003Cbr \/\u003E\u003Cstrong\u003E404.385.3102\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}