{"id":3813,"date":"2013-06-10T10:59:17","date_gmt":"2013-06-10T09:59:17","guid":{"rendered":"http:\/\/www.solucominsight.fr\/?p=3813"},"modified":"2015-01-21T09:54:20","modified_gmt":"2015-01-21T08:54:20","slug":"big-data-tour-dhorizon-2013","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2013\/06\/big-data-tour-dhorizon-2013\/","title":{"rendered":"Big data : tour d\u2019horizon 2013 !"},"content":{"rendered":"

En 2012, nous avions publi\u00e9 toute une s\u00e9rie d\u2019articles autour du\u00a0 Big data. Un an apr\u00e8s, le sujet est suscite toujours autant d\u2019int\u00e9r\u00eat aupr\u00e8s des DSI. Comment l\u2019expliquer ?<\/em><\/p>\n

Big data : un int\u00e9r\u00eat qui ne faiblit pas<\/h2>\n

Depuis 2012, l\u2019int\u00e9r\u00eat pour le Big data ne cesse de cro\u00eetre (cf. courbe google trends)<\/a>. Pourtant, faute de disposer d\u2019une d\u00e9finition tangible, la pertinence des usages de cette expression reste, aujourd\u2019hui encore, sujette \u00e0 interpr\u00e9tation.<\/p>\n

Prenons un \u00e9chantillon de d\u00e9finitions produites par les voix les plus influentes du domaine IT (Gartner, Forrester, IBM etc.). Derri\u00e8re le Big data, une multitude de sujets : il peut s\u2019agir, selon les analystes,\u00a0 1) des donn\u00e9es\u00a0 (d\u2019un genre caract\u00e9ristique), 2) d\u2019un ph\u00e9nom\u00e8ne ou encore 3) d\u2019un ensemble de techniques ou technologies. Il en ressort cependant un champ lexical qui fait assez largement consensus et s\u2019est impos\u00e9 sur la plupart des slidewares (avec aussi des contradicteurs).<\/p>\n

\u00ab Petite \u00bb analyse lexicale du \u00ab Big \u00bb data<\/h2>\n

3 V. Volume, Vari\u00e9t\u00e9, V\u00e9locit\u00e9.<\/strong> Vision du Gartner de 2001<\/a> : chacune de ces caract\u00e9ristiques constituent un d\u00e9fi pour les entreprises qui souhaitent les exploiter ; leur combinaison accentue d\u2019autant la difficult\u00e9 que repr\u00e9sente le traitement des donn\u00e9es. Le Big data n\u2019est pas que l\u2019explosion des volumes. C\u2019est aussi la richesse des formats et le temps r\u00e9el. Ce qui pose question et qui est rarement explicit\u00e9, c\u2019est la mesure de chacune de ces trois dimensions. Le volume se mesure-t-il en t\u00e9ra- ou en p\u00e9tabytes ? O\u00f9 se situent les niveaux d\u2019acceptabilit\u00e9 qui permettent de d\u00e9terminer qu\u2019une situation ou un cas d\u2019usage rel\u00e8ve du Big data ? Le Big data d\u2019aujourd\u2019hui sera-t-il celui de demain ? Car avec la croissance des donn\u00e9es et les avanc\u00e9es technologiques, le curseur ne cessera sans doute pas de se d\u00e9placer\u2026<\/p>\n

4V = 3V+Valeur.<\/strong> Certains ont not\u00e9 un glissement<\/a> de l\u2019acception Big data de 3 \u00e0 4 V : des caract\u00e9ristiques de nature technique \u00e0 celle de \u00ab valeur \u00bb. Que peut-on tirer des donn\u00e9es ? C\u2019est en fait la question essentielle : celle de \u00ab l\u2019opportunit\u00e9 \u00e0 saisir \u00bb ou du \u00ab besoin \u00e0 combler \u00bb. Une autre question appara\u00eet avec les fournisseurs de donn\u00e9es : quel est le prix des donn\u00e9es ?<\/p>\n

Technologie accessible.<\/strong> Il n\u2019y a pas de d\u00e9finition technologique du Big data au sens o\u00f9 aucune technologie n\u2019est li\u00e9e de mani\u00e8re exclusive et cat\u00e9gorique au concept. Tout comme les web services n\u2019\u00e9taient pas la SOA, le Big data n\u2019est pas Hadoop*, m\u00eame si Hadoop est la valeur sure d\u2019un march\u00e9 en plein essor et encore peu lisible. Certes, l\u2019envol\u00e9e du Big data doit beaucoup \u00e0 l\u2019\u00ab accessibilit\u00e9 \u00bb\u00a0 d\u2019Hadoop et du noSQL. Mais le co\u00fbt et le ROI de ces solutions sont-ils \u00e0 la hauteur des promesses ?<\/p>\n

Acquisition, visualisation etc.<\/strong> Le Big data n\u2019est pas que stockage et analyse de donn\u00e9es. Il faut\u00a0 d\u00e9velopper son gisement pour trouver la bonne information. L\u2019information produite doit \u00eatre comprise, retenue, travaill\u00e9e et des techniques d\u2019analyse visuelle sont aujourd\u2019hui mises en avant.\u00a0 Enfin, pour les questions de fiabilit\u00e9 et de s\u00e9curit\u00e9, des \u00e9vidences en gestion des donn\u00e9es, des solutions se positionnent.<\/p>\n

D\u00e9cision.<\/strong> Le Big data bouscule le d\u00e9cisionnel. Les cas d\u2019usage Big data sont quasiment exclusivement analytiques. Les technologies dont nous parlions ne sont d\u2019ailleurs pas construites pour supporter des processus transactionnels qui restent en p\u00e9riph\u00e9rie de la \u00ab r\u00e9volution \u00bb \u00e0 la source des donn\u00e9es.<\/p>\n

Un Big data \u00e0 ma sauce<\/h2>\n

Le Big data est une v\u00e9ritable probl\u00e9matique, soit litt\u00e9ralement un faisceau de questions. Si l\u2019exercice de d\u00e9finition va se poursuivre, il serait salvateur de l\u2019\u00e9vacuer rapidement. Mieux vaut se concentrer sur les d\u00e9fis techniques et organisationnels du traitement des donn\u00e9es et la recherche de nouveaux leviers de performance.<\/p>\n

Forrester propose pour ce faire une approche pragmatique \u00ab Calculer son \u00ab Big Data score \u00bb<\/a>, qui vise \u00e0 s\u2019auto-\u00e9valuer sur sa capacit\u00e9 \u00e0 stocker, traiter, requ\u00eater ses donn\u00e9es selon chacune des 3 dimensions. Big data ne veut pas dire la m\u00eame chose pour Google et pour moi. Il faut revenir \u00e0 des enjeux r\u00e9alistes : ai-je explor\u00e9 les opportunit\u00e9s d\u2019utilisation des donn\u00e9es \u00ab \u00e0 ma disposition \u00bb ? Suis-je capable de \u00ab passer \u00e0 l\u2019\u00e9chelle \u00bb efficacement (d\u00e9lai, co\u00fbt) si une opportunit\u00e9 se pr\u00e9sente ?
\nEt, \u00e0 quelle \u00e9chelle : *10, *100, *1000 ? Que font mes concurrents ?<\/p>\n

Alors : in or out<\/em> ?<\/p>\n

*Hadoop comme nombre de bases de donn\u00e9es noSQL est distribu\u00e9 en open source. Le d\u00e9ploiement d\u2019Hadoop n\u2019exige pas d\u2019infrastructure r\u00e9seau et\/ou serveurs haut de gamme.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

En 2012, nous avions publi\u00e9 toute une s\u00e9rie d\u2019articles autour du\u00a0 Big data. Un an apr\u00e8s, le sujet est suscite toujours autant d\u2019int\u00e9r\u00eat aupr\u00e8s des DSI. Comment l\u2019expliquer ? Big data : un int\u00e9r\u00eat qui ne faiblit pas Depuis 2012,…<\/p>\n","protected":false},"author":300,"featured_media":3701,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"page-templates\/tmpl-one.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[35],"tags":[189,1168,59,1128,321,1169,157],"coauthors":[2545],"class_list":["post-3813","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-strategie-projets-it","tag-big-data","tag-cas-dusage","tag-donnees","tag-evaluation","tag-hadoop","tag-pragmatique","tag-pratique"],"acf":[],"yoast_head":"\nBig data : tour d\u2019horizon 2013 !<\/title>\n<meta name=\"description\" content=\"En 2012, nous avions publi\u00e9 toute une s\u00e9rie d\u2019articles autour du Big data. Un an apr\u00e8s, le sujet est suscite toujours autant d\u2019int\u00e9r\u00eat aupr\u00e8s des DSI. 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