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==Uses==
In-database processing makes data analysis more accessible and relevant for high-throughput, real-time applications including fraud detection, credit scoring, risk management, transaction processing, pricing and margin analysis, usage-based micro-segmenting, behavioral ad targeting and recommendation engines, such as those used by customer service organizations to determine next-best actions.<ref name=Kobelius>{{citation|last=Kobelius|first=James|title=The Power of Predictions: Case Studies in CRM Next Best Action|url=http://www.forrester.com/The+Power+Of+Predictions/fulltext/-/E-RES60094|publisher=Forrester|date=June 22, 2011|access-date=May 15, 2012|archive-date=April 13, 2012|archive-url=https://web.archive.org/web/20120413193606/http://www.forrester.com/The+Power+Of+Predictions/fulltext/-/E-RES60094|url-status=dead}}</ref>
==Vendors==
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==Related Technologies==
In-database processing is one of several technologies focused on improving data warehousing performance. Others include [[parallel computing]], shared everything architectures, [[shared nothing architecture]]s and [[massive parallel processing]]. It is an important step towards improving [[predictive analytics]] capabilities.<ref name="TimManns">
==External links==
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