<?xml version='1.0' encoding='UTF-8'?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:ccmbenchmark="https://www.ccmbenchmark.com/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Antoine Buat : Dernier contenus</title><link>/account/antoine-buat-26619</link><atom:link href="/account/antoine-buat-26619" rel="self" type="application/rss+xml" /><language>fr</language><pubDate>Wed, 29 Jul 2026 23:54:35 +0200</pubDate><lastBuildDate>Wed, 29 Jul 2026 23:54:35 +0200</lastBuildDate><item><title>5 pièges à éviter pour analyser d’importants volumes de données</title><link>https://www.journaldunet.com/big-data/1207137-5-pieges-a-eviter-pour-analyser-d-importants-volumes-de-donnees/</link><description><![CDATA[<a href="https://www.journaldunet.com/big-data/1207137-5-pieges-a-eviter-pour-analyser-d-importants-volumes-de-donnees/"><img src="https://img-0.journaldunet.com/Mx9nF0jW6lTDyqvJEfk8FfoR9z4=/100x/smart/41c22082e6b44496b351da0ad355ac5b/user-jdn/10022997-anonyme-anonyme.jpg" align="left" hspace="5" vspace="0"></a>On dit du big data qu’il est le nouvel or noir de l’économie numérique. Selon Pricewaterhouse Coopers et Iron Mountain, 43% des entreprises tirent peu d'avantages de leurs informations, tandis que 23% n'en tirent pas profit.]]></description><pubDate>Mon, 12 Feb 2018 10:48:41 +0100</pubDate><guid>https://www.journaldunet.com/big-data/1207137-5-pieges-a-eviter-pour-analyser-d-importants-volumes-de-donnees/</guid><dc:creator><![CDATA[Antoine Buat]]></dc:creator><ccmbenchmark:content_author><![CDATA[Antoine Buat]]></ccmbenchmark:content_author><ccmbenchmark:content_author_company><![CDATA[DigDash]]></ccmbenchmark:content_author_company><ccmbenchmark:content_author_illustration>1</ccmbenchmark:content_author_illustration></item></channel></rss>