{"id":11388,"date":"2020-10-05T13:26:50","date_gmt":"2020-10-05T13:26:50","guid":{"rendered":"https:\/\/datascientest.com\/?p=11388"},"modified":"2024-03-25T13:35:34","modified_gmt":"2024-03-25T13:35:34","slug":"pourquoi-echec-data-science","status":"publish","type":"post","link":"https:\/\/datascientest.com\/pourquoi-echec-data-science","title":{"rendered":"Pourquoi la plupart des projets de Data Science \u00e9chouent (et comment r\u00e9ussir le v\u00f4tre) ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"11388\" class=\"elementor elementor-11388\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9be2bde e-flex e-con-boxed e-con\" data-id=\"9be2bde\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f4ec9e1 elementor-widget elementor-widget-heading\" data-id=\"f4ec9e1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">La Data Science est de plus en plus utilis\u00e9e en entreprise, mais 85% des projets \u00e9chouent. D\u00e9couvrez quelles sont les causes de ce ph\u00e9nom\u00e8ne alarmant, et quelles sont les solutions pour mener \u00e0 bien vos projets...<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bc7f3b elementor-widget elementor-widget-text-editor\" data-id=\"1bc7f3b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Dans tous les secteurs, les entreprises se tournent aujourd&rsquo;hui vers la <a href=\"https:\/\/datascientest.com\/data-science-definition\">Data Science<\/a> et le <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\">Machine Learning<\/a>. En th\u00e9orie, ces technologies nouvelles peuvent <strong>apporter un avantage comp\u00e9titif et stimuler la croissance<\/strong> des organisations.<\/p><p>Cependant, dans la pratique, l&rsquo;impl\u00e9mentation de la science des donn\u00e9es s&rsquo;av\u00e8re souvent plus difficile que pr\u00e9vu. Soyons clairs : selon une \u00e9tude men\u00e9e par Gartner, <strong>85% des projets de Data Science \u00e9chouent<\/strong>.<\/p><p>De m\u00eame, d&rsquo;apr\u00e8s un rapport de Dimensional Research, <strong>seuls 4% des entreprises ont r\u00e9ussi \u00e0 d\u00e9ployer des mod\u00e8les<\/strong> de Machine Learning dans un environnement de production. Dans un contexte \u00e9conomique rendu encore plus difficile par la crise du COVID-19, il y a donc de quoi h\u00e9siter \u00e0 lancer une initiative de science des donn\u00e9es en 2020.<\/p><p>Toutefois, il serait dommage de passer \u00e0 c\u00f4t\u00e9 des <strong>nombreux bienfaits de la Data Science<\/strong> et du Machine Learning par peur de l&rsquo;\u00e9chec. En r\u00e9alit\u00e9, il existe plusieurs principaux facteurs expliquant ce ph\u00e9nom\u00e8ne.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1b50046 e-flex e-con-boxed e-con\" data-id=\"1b50046\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8c78a41 elementor-widget elementor-widget-heading\" data-id=\"8c78a41\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Pourquoi les projets de Data Science \u00e9chouent ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba2e0c2 elementor-widget elementor-widget-text-editor\" data-id=\"ba2e0c2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Tout d&rsquo;abord, beaucoup d&rsquo;entreprises sont confront\u00e9es \u00e0 ce que l&rsquo;on appelle <strong>des silos de donn\u00e9es<\/strong>. Alors que les donn\u00e9es devraient \u00eatre regroup\u00e9es et nettoy\u00e9es, elles sont souvent \u00e9parpill\u00e9es entre plusieurs bases de donn\u00e9es dans diff\u00e9rents formats inadapt\u00e9s \u00e0 l&rsquo;analyse.<\/p><p style=\"text-align: justify;\">Or, <strong>la qualit\u00e9 et la bonne gestion des donn\u00e9es<\/strong> sont essentielles au bon d\u00e9roulement d&rsquo;un projet de Data Science ou d&rsquo;IA. Malheureusement, la plupart des organisations ne disposent pas d&rsquo;infrastructure de donn\u00e9es requise et leurs donn\u00e9es sont de pi\u00e8tre qualit\u00e9.<\/p><p>La seconde cause de l&rsquo;\u00e9chec des projets de Data Science est la p\u00e9nurie d&rsquo;experts. En 2018, un rapport LinkedIn r\u00e9v\u00e9lait un manque de plus de 150 000 Data Scientists et autres experts pour r\u00e9pondre \u00e0 la demande des entreprises.<\/p><p style=\"text-align: justify;\">Un troisi\u00e8me probl\u00e8me est celui du <strong>manque de transparence<\/strong>. Les objectifs et ambitions des \u00e9quipes techniques et business divergent, et la communication est insuffisante.<\/p><p style=\"text-align: justify;\">L&rsquo;\u00e9quipe de Data Science se focalisera par exemple sur la pr\u00e9cision d&rsquo;un mod\u00e8le, tandis que les gestionnaires s&rsquo;int\u00e9resseront davantage aux avantages financiers qu&rsquo;il apporte. De plus, bien souvent, <strong>les mod\u00e8les de Machine Learning fonctionnent en \u00ab\u00a0bo\u00eete noire\u00a0\u00bb<\/strong>\u00a0et il est impossible de les interpr\u00e9ter.<\/p><p style=\"text-align: justify;\">Enfin, le dernier obstacle au bon d\u00e9roulement des projets de Data Science est <strong>l&rsquo;incompr\u00e9hension partielle ou totale<\/strong> de la mani\u00e8re dont ces projets seront utilis\u00e9s en production. Cette incompr\u00e9hension emp\u00eache la science des donn\u00e9es de cr\u00e9er une valeur r\u00e9elle pour les entreprises.<\/p><p style=\"text-align: justify;\">Le pipeline de production est g\u00e9n\u00e9ralement g\u00e9r\u00e9 par les \u00e9quipes informatiques, et les \u00e9quipes de Data Science n&rsquo;ont donc pas de vision architecturale de la fa\u00e7on dont leurs projets y seront int\u00e9gr\u00e9s. De fait,<strong> les projets n&rsquo;apportent pas les b\u00e9n\u00e9fices attendus<\/strong>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e863757 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"e863757\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/Lhc4ObgzhP8&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3222138 e-flex e-con-boxed e-con\" data-id=\"3222138\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c8ac553 elementor-widget elementor-widget-heading\" data-id=\"c8ac553\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">AutoML : l'automatisation est la cl\u00e9 du succ\u00e8s des projets de Data Science<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c5751b elementor-widget elementor-widget-text-editor\" data-id=\"6c5751b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>L&rsquo;une des solutions \u00e0 ces diff\u00e9rents probl\u00e8mes est <strong>l&rsquo;automatisation des processus de Data Science<\/strong>. De plus en plus d&rsquo;entreprises se tournent vers cette option.<\/p><p>L&rsquo;automatisation permet d&rsquo;accro\u00eetre la transparence, et d&rsquo;am\u00e9liorer en continu gr\u00e2ce \u00e0 l&rsquo;it\u00e9ration. Les <strong>plateformes AutoML<\/strong> permettent en effet d&rsquo;automatiser les \u00e9tapes it\u00e9ratives pour permettre de tester rapidement de nouvelles fonctionnalit\u00e9s ou de valider des mod\u00e8les.<\/p><p>M\u00eame en cas d&rsquo;\u00e9chec, <strong>celui-ci surviendra plus vite<\/strong> et ses cons\u00e9quences seront donc moins d\u00e9sastreuses. Les hypoth\u00e8ses d&rsquo;\u00e9chec peuvent \u00eatre test\u00e9es rapidement, et donc \u00e9limin\u00e9es d&#8217;embl\u00e9e. Ceci permet de r\u00e9aliser d&rsquo;importantes \u00e9conomies et d&rsquo;accro\u00eetre la productivit\u00e9.<\/p><p>Par ailleurs, <strong>les mod\u00e8les en \u00ab\u00a0white box\u00a0\u00bb<\/strong>\u00a0sont importants puisqu&rsquo;ils offrent des explications claires sur la fa\u00e7on dont ils se comportent, dont ils produisent des pr\u00e9dictions, et sur les variables qui les influencent. Il est donc pr\u00e9f\u00e9rable d&rsquo;utiliser des mod\u00e8les interpr\u00e9tables, tels que les arbres de d\u00e9cision ou les mod\u00e8les lin\u00e9aires.<\/p><p>En r\u00e9sum\u00e9, <strong>l&rsquo;automatisation de la Data Science permet de gagner du temps<\/strong>, de d\u00e9velopper et de d\u00e9ployer rapidement et d&rsquo;effectuer facilement des ajustements. Les mod\u00e8les de Machine Learning peuvent \u00eatre modifi\u00e9s avec les derni\u00e8res donn\u00e9es en date, et les entreprises peuvent explorer davantage de sources de donn\u00e9es.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c9684a0 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"c9684a0\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/jn-22XyKsgo&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f33a91f elementor-widget elementor-widget-text-editor\" data-id=\"f33a91f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>En conclusion, <strong>les outils AutoML permettent de mener \u00e0 bien les projets de Data Science<\/strong> sans avoir \u00e0 recruter d&rsquo;experts suppl\u00e9mentaires. D\u00e9couvrez tout ce que vous devez savoir sur <a href=\"https:\/\/datascientest.com\/automl-automatisation-menace-data-scientists\" target=\"_blank\" rel=\"noopener\">l&rsquo;automatisation de la science des donn\u00e9es<\/a>, et <a href=\"https:\/\/datascientest.com\/data-quality-erreurs\" target=\"_blank\" rel=\"noopener\">les 10 erreurs \u00e0 ne pas commettre pour la qualit\u00e9 des donn\u00e9es<\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35ecbe1 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"35ecbe1\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Formez-vous \u00e0 la Data Science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>La Data Science est de plus en plus utilis\u00e9e en entreprise, mais 85% des projets \u00e9chouent. D\u00e9couvrez quelles sont les causes de ce ph\u00e9nom\u00e8ne alarmant, et quelles sont les solutions pour mener \u00e0 bien vos projets&#8230; Dans tous les secteurs, les entreprises se tournent aujourd&rsquo;hui vers la Data Science et le Machine Learning. En th\u00e9orie, [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":11395,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2431],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Science : Les erreurs \u00e0 ne pas commettre pour r\u00e9ussir vos projets !<\/title>\n<meta name=\"description\" content=\"Selon Gartner, 85% des projets de Data Science \u00e9chouent. 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