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Traditional approaches to data analysis require data to be moved out of the database into a separate analytics environment for processing, and then back to the database. ([[SPSS]] from [[IBM]] are examples of tools that still do this today). Doing the analysis in the database, where the data resides, eliminates the costs, time and security issues associated with the old approach by doing the processing in the data warehouse itself.<ref name="DBTA">{{citation|last=Das|first=Joydeep|title=Adding Competitive Muscle with In-Database Analytics|url=http://www.dbta.com/Articles/Editorial/Trends-and-Applications/Adding-Competitive-Muscle-with-In-Database-Analytics-67126.aspx|publisher=Database Trends & Applications|date=May 10, 2010}}</ref>
Though in-database capabilities were first commercially offered in the mid-1990s, as object-related database systems from vendors including IBM, [[Illustra]]/[[Informix]] (now IBM) and [[Oracle Corporation|Oracle]], the technology did not begin to catch on until the mid-2000s.<ref name="IE">{{citation|last=Grimes|first=Seth|title=In-Database Analytics: A Passing Lane for Complex Analysis|url=http://intelligent-enterprise.informationweek.com/info_centers/data_int/showArticle.jhtml;jsessionid=YH5ZICM4SKOMRQE1GHPSKH4ATMY32JVN?articleID=212500351&cid=RSSfeed_IE_News|publisher=Intelligent Enterprise|date=December 15, 2008}}</ref> The concept of migrating analytics from the analytical workstation and into the Enterprise Data Warehouse was first introduced by Thomas Tileston in his presentation entitled, “Have Your Cake & Eat It Too! Accelerate Data Mining Combining SAS & Teradata” at the [[Teradata]] Partners 2005 "Experience the Possibilities" conference in Orlando, FL, September 18–22, 2005. Mr. Tileston later presented this technique globally in 2006,<ref>{{Cite web|url=http://www.itworldcanada.com/article/business-intelligence-taking-the-sting-out-of-forecasting/7193|title=Business Intelligence – Taking the sting out of forecasting | IT World Canada News|date=31 October 2006}}</ref> 2007<ref>http://www2.sas.com/proceedings/forum2007/371-2007.pdf {{Bare URL PDF|date=March 2022}}</ref><ref>http://de.saswiki.org/wiki/SAS_Global_Forum_2007 {{Dead link|date=March 2022}}</ref><ref>{{Cite web |url=http://lexjansen.com/cgi-bin/sug_proceedings_pdf.php?c=SUGI&x=SGF2007 |title=Archived copy |access-date=2014-08-21 |archive-url=https://web.archive.org/web/20140822051218/http://lexjansen.com/cgi-bin/sug_proceedings_pdf.php?c=SUGI&x=SGF2007 |archive-date=2014-08-22 |url-status=dead }}</ref> and 2008.<ref>http://www.teradata.kr/teradatauniverse/PDF/Track_2/2_2_Warner_Home_Thomas_Tileston.pdf {{Bare URL PDF|date=March 2022}}</ref>
At that point, the need for in-database processing had become more pressing as the amount of data available to collect and analyze continues to grow exponentially (due largely to the rise of the Internet), from megabytes to gigabytes, terabytes and petabytes. This “[[big data]]” is one of the primary reasons it has become important to collect, process and analyze data efficiently and accurately.
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