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données",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_1522565878":{"id":"flexible-column-container-222aeee56b","propertiesId":"hub-hero","type":"2-column-40-60","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f1a217d4df",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"text_1842995591":{"id":"text-d3f0f4fda8","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuide pratique\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-b076451d51","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Gouvernance des données : définition et enjeux stratégiques"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-9ca528ed26","additionalClasses":"hub-hero__subheadline","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003ECe guide présente les cadres, les principes et les étapes concrètes nécessaires pour garantir la fiabilité, l’auditabilité et la scalabilité des données, y compris dans les environnements pilotés par l’IA.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","columnClassNames":{"person_chip_343411615":"aem-GridColumn aem-GridColumn--default--12","person_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f8305f7d1f",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"person_chip_343411615":{"id":"person-chip-34eb4efa19","personName":"Laurie Macpherson","personTitle":"Technical Writer, Snowflake","layoutStyle":"horizontal","containerStyle":"left-aligned","avatar":{"id":"avatar","alt":"Laurie MacPherson","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","height":"800","width":"800","lazyEnabled":true,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/person-chip"},"person_chip":{"id":"person-chip-8bb4fe0312","personName":"Tracy Kabuya","personTitle":"Contributeur local, Snowflake","layoutStyle":"horizontal","containerStyle":"left-aligned","avatar":{"id":"avatar","alt":"Tracy Kabuya","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","height":"709","width":"709","lazyEnabled":true,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/person-chip"}},":itemsOrder":["person_chip_343411615","person_chip"]}},":itemsOrder":["text_1842995591","title_v2","text","container"]},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-5849896afe",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-9aec059f18","youtubeVideoId":"bF6FAMeGEZc","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_205440979":{"id":"flexible-column-container-3bdbf72704","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container","layout":"SIMPLE","id":"container-0cea4d34e8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-91c83b4f3a","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes liés à la gouvernance des données :\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-320d5532ac","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/\"\u003EFrameworks de gouvernance de l’IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/policy/\"\u003EPolitique de gouvernance des données\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/data-privacy/\"\u003EConfidentialité des données\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/data-lineage/\"\u003ETraçabilité des données\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/policy/\"\u003EPolitique de données\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_549182334":{"id":"flexible-column-container-7e66030ed4","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout_what-is-data-governance_0":{"id":"text-701525ac2e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDÉFINITION DE LA GOUVERNANCE DES DONNÉES\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa gouvernance des données désigne l’ensemble des politiques, des rôles, des processus et des technologies qui garantissent une gestion, une protection et une utilisation cohérentes et responsables des données tout au long de leur cycle de vie, couvrant notamment la propriété, l’accès, la qualité, la classification et l’auditabilité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_overview_0":{"id":"text-246e5c7e96","text":"\u003Cp\u003ELorsqu’un incident de données survient (constat de non-conformité, modèle entraîné sur la mauvaise population, indicateur interprété différemment d’une équipe à l’autre), les entreprises se retrouvent souvent incapables de répondre aux questions suivantes. La propriété des données n’est pas clairement établie, la traçabilité est incomplète et les registres d’accès n’existent pas ou ne remontent pas assez loin dans le temps. Le problème finit alors par faire surface lors d’un contrôle réglementaire ou d’une revue post-incident.\u003C/p\u003E\r\n\u003Cp\u003ELa gouvernance des données consiste à mettre en place la propriété, la classification, la traçabilité et l’audit qui rendent ces réponses disponibles avant qu’elles ne soient demandées. À mesure que les données transitent entre les systèmes, les environnements cloud, les partenaires et les workflows d’IA, les exigences en matière de gouvernance continuent de croître. Dans la plupart des entreprises, obtenir des réponses concernant les données est bien plus difficile que cela ne devrait l’être. Ce guide vous explique comment y remédier.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-data-governance":{"id":"title-v2-e56818b9e7","additionalClasses":"anchor-title anchor-title--what-is-data-governance","type":"heading2","lines":["Qu’est-ce que la gouvernance des données ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-data-governance_0":{"id":"text-8305a4fe9b","text":"\u003Cp\u003ELa gouvernance des données désigne l’ensemble des politiques, des rôles, des processus et des technologies qui définissent la manière dont une entreprise gère les données tout au long de leur cycle de vie. Elle établit à qui appartiennent les données, ce qu’elles signifient, comment elles sont classifiées, qui peut y accéder, comment leur qualité est mesurée et comment leur utilisation est auditée. Concrètement, la gouvernance des données est un modèle opérationnel qui permet de garantir la fiabilité, la protection et l’exploitabilité des données à  l’échelle.\u003C/p\u003E\n\u003Cp\u003EUn programme de gouvernance des données mature apporte des réponses à quatre questions clés :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EQuelles données existent et que signifient-elles ?\u003C/li\u003E\n\u003Cli\u003EÀ qui appartiennent-elles et qui est garant de leur exploitation ?\u003C/li\u003E\n\u003Cli\u003EQui peut y accéder, les partager ou les exploiter dans des workflows d’IA ?\u003C/li\u003E\n\u003Cli\u003EL’entreprise peut-elle justifier la façon dont elles ont été transformées, protégées et exploitées ?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EPour répondre à ces questions, il faut tenir compte des métadonnées, de l’intendance des données, de la qualité des données, des contrôles de confidentialité, des processus de conformité et d’une responsabilisation claire. Sans la synergie de ces composants, les équipes ne peuvent pas gouverner les données de manière fiable.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-data-governance-matters-now":{"id":"title-v2-10971c2eeb","additionalClasses":"anchor-title anchor-title--why-data-governance-matters-now","type":"heading2","lines":["Pourquoi la gouvernance des données est importante"],":type":"snowflake-site/components/title-v2"},"text_why-data-governance-matters-now_0":{"id":"text-087f721614","text":"\u003Cp\u003ELa gouvernance des données est devenue plus complexe car les données ne sont plus cloisonnées au sein d’un unique environnement de reporting. Une table d’usage produit peut ainsi alimenter des analyses, des workflows de support client, le reporting partenaires, des variables de machine learning et des tableaux de bord de direction. Une \u003Ca href=\"https://www.snowflake.com/fr/data-governance/policy/\"\u003Epolitique de gouvernance des données\u003C/a\u003E adaptée à un tableau de bord ne régit pas automatiquement chaque copie, transformation ou prompt d’IA en aval qui exploite ces mêmes données.\u003C/p\u003E\r\n\u003Cp\u003ECette fragmentation constitue l’un des principaux obstacles à une gouvernance efficace et à une IA évolutive. « Quand on conserve ses données à un endroit pour une chose, et à un autre endroit pour une autre, gouverner et sécuriser ces données devient vraiment difficile », explique Baris Gultekin, VP Product, AI chez Snowflake. Il fait valoir que l’investissement dans un socle de données unique et standardisé au sein de l’entreprise permet des cas d’usage plus puissants de l’IA générative tout en simplifiant la gouvernance et la sécurité.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-data-governance-matters-now_0":{"id":"quote-item-3475e18ba3","alignment":"left","layout":"simple","quoteSourceName":"Baris Gultekin","quoteSourceTitle":"VP Product, AI à Snowflake","showQuoteIcon":true,"quote":"Quand on conserve ses données à un endroit pour une chose, et à un autre endroit pour une autre, gouverner et sécuriser ces données devient vraiment difficile.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-data-governance-matters-now_1":{"id":"text-a9af35c673","text":"\u003Cp\u003EPour que ce type de stratégie fonctionne dans la pratique, centraliser les données ne suffit pas : cela exige une gouvernance cohérente et évolutive. Les équipes doivent classifier les colonnes sensibles, définir les responsabilités, assurer la traçabilité, appliquer le masquage des données, surveiller la fraîcheur des données et auditer l’usage sans s’appuyer sur le savoir informel ou des feuilles de calcul éparpillées. Avec une gouvernance efficace, il est plus facile de trouver des données fiables et plus sûr de les utiliser, sans friction supplémentaire pour les équipes qui en ont besoin.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-data-governance-matters-now_0":{"id":"text-764fe62884","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EPIÈGE COURANT\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELes organisations considèrent souvent la gouvernance des données comme un projet ponctuel, plutôt que comme une discipline opérationnelle permanente intégrée aux workflows quotidiens. Il en résulte un flou quant à la propriété des données, des métadonnées incomplètes et des contrôles qui ne se propagent pas à mesure que les données circulent entre les systèmes et les cas d’usage de l’IA. Cela rend les anomalies difficiles à retracer et complique le respect des exigences d’audit.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_data-governance-for-ai":{"id":"title-v2-6843e818ad","additionalClasses":"anchor-title anchor-title--data-governance-for-ai","type":"heading2","lines":["Gouvernance pour l’IA"],":type":"snowflake-site/components/title-v2"},"text_data-governance-for-ai_0":{"id":"text-c3cc6524b3","text":"\u003Cp\u003EL’IA soulève de nouveaux enjeux pour la gouvernance des données, car les données gouvernées peuvent désormais être extraites, synthétisées, transformées ou exploitées par des modèles et des agents. Lorsqu'un agent IA agit pour le compte d'un utilisateur, les questions se multiplient : quelles lignes a-t-il extraites, qu'a-t-il inclus dans un prompt et existe-t-il une piste d'audit pour tout cela ?\u003C/p\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/fr/data-governance/ai/\"\u003Egouvernance des données\u003C/a\u003E se concentre sur l’utilisation des systèmes de données et d'IA. Cela inclut la provenance des données d’entraînement ; la classification des informations personnelles identifiables (PII) et des données sensibles ; les contrôles du consentement et de l’utilisation autorisée ; le biais et la représentativité des données sources ; la traçabilité entre les jeux de données, les features, les prompts, les sorties et les décisions en aval ; et les contrôles d’accès des agents et les pistes d’audit pour la récupération, le contexte du prompt et les sorties générées.\u003C/p\u003E\r\n\u003Cp\u003ECela concerne la \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003Egouvernance de l’IA\u003C/a\u003E, mais ce n'est pas la même chose. La gouvernance des données pour l’IA régit les données que les systèmes d’IA utilisent. La gouvernance de l’IA régit le modèle ou le système lui-même : approbations de modèles, évaluations, fiches modèles, surveillance, dérive, supervision humaine et \u003Ca href=\"https://www.snowflake.com/en/data-governance/strategy/data-risk-management/\"\u003Egestion des risques\u003C/a\u003E. Les deux programmes doivent se connecter. Un workflow d’IA à haut risque ne peut faire l’objet d’une gouvernance efficace si l’organisation ignore quelles sources de données l’alimentent, quels attributs sensibles peuvent apparaître lors de la restitution, qui a autorisé l’utilisation de ces données et si les résultats peuvent être tracés jusqu’à des sources gouvernées.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EVoici comment Snowflake aide à gouverner les données pour une IA fiable :\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-for-ai_0":{"id":"embed-f186e7fc44","youtubeVideoId":"42GWL5x3Xpg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_data-governance-principles":{"id":"title-v2-c7c935cf43","additionalClasses":"anchor-title anchor-title--data-governance-principles","type":"heading2","lines":["Principes de la gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-governance-principles_0":{"id":"text-f47df56f9d","text":"\u003Cp\u003ELes principes de gouvernance des données guident les choix d’un programme en matière de politiques, de propriété, de technologie et de processus. Les principes communs sont les suivants :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EResponsabilisation\u003C/b\u003E : Chaque actif de données critique dispose d’un propriétaire désigné, responsable des décisions liées aux accès, aux anomalies de qualité et aux arbitrages des définitions.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETransparence : \u003C/b\u003ELes utilisateurs peuvent voir les définitions, la traçabilité, les signaux de qualité et le cadre réglementaire pour les actifs de données avec lesquels ils travaillent.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EQualité des données :\u003C/b\u003E\u003Cspan style=\"font-weight: bolder;\"\u003E \u003C/span\u003E\u003Cspan\u003ELes données gouvernées sont évaluées au regard d’exigences explicites ; leur fiabilité ne saurait être simplement présumée.\u003C/span\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESécurité et confidentialité des données :\u003C/b\u003E\u003Cspan style=\"font-weight: bolder;\"\u003E \u003C/span\u003E\u003Cspan\u003ELes données sensibles sont classifiées, protégées et contrôlées tout au long de leur cycle de vie.\u003C/span\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIntendance des données :\u003C/b\u003E\u003Cspan\u003E Les data stewards désignés maintiennent à jour les définitions, résolvent les anomalies et encadrent l’utilisation responsable des données au sein des différents domaines.\u003C/span\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStandardisation :\u003C/b\u003E\u003Cspan\u003E La terminologie, les politiques et les contrôles sont harmonisés entre les domaines, sauf exception documentée et approuvée.\u003C/span\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditabilité :\u003C/b\u003E\u003Cspan style=\"font-weight: bolder;\"\u003E \u003C/span\u003E\u003Cspan\u003EL’entreprise peut démontrer comment les données ont été consultées, modifiées, partagées et utilisées.\u003C/span\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUtilisation éthique :\u003C/b\u003E\u003Cspan\u003E \u003C/span\u003E\u003Ca style=\"background-color: rgb(255,255,255);\" href=\"https://www.snowflake.com/en/data-governance/policy/data-ethics/\"\u003EL’éthique des données\u003C/a\u003E\u003Cspan\u003E signifie que les données sont utilisées d’une manière équitable, non discriminatoire et alignée sur les attentes des utilisateurs, avec des mécanismes pour identifier et atténuer les résultats préjudiciables ou non intentionnels.\u003C/span\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ECes principes se manifestent dans des mécanismes concrets : champs de propriété dans un catalogue, étiquettes de sensibilité sur les colonnes, politiques de masquage associées aux données réglementées, chemins de traçabilité pour les rapports critiques et journaux d’audit pour les revues d’accès.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-frameworks-and-standards":{"id":"title-v2-7436464c2e","additionalClasses":"anchor-title anchor-title--data-governance-frameworks-and-standards","type":"heading2","lines":["Cadres et normes de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-governance-frameworks-and-standards_0":{"id":"text-d116587d65","text":"\u003Cp\u003ELes cadres et normes de gouvernance des données aident les entreprises à structurer un programme, à définir des capacités et à décider ce qu’elles doivent mettre en œuvre en premier. Certains se concentrent sur la maturité de la gestion des données, tandis que d’autres se concentrent sur la gouvernance IT, l’architecture, la qualité ou les contrôles cloud.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework ou norme\u003C/th\u003E\r\n\u003Cth\u003EUsage le plus adapté\u003C/th\u003E\r\n\u003Cth\u003ECe qu’il aide à gouverner\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EConception d’un programme global de gestion des données\u003C/td\u003E\r\n\u003Ctd\u003EDomaines de connaissances, les rôles et les disciplines de la gestion des données\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EMaturité de la gestion des données d’entreprise\u003C/td\u003E\r\n\u003Ctd\u003EModèle opérationnel, contrôles, responsabilisation et maturité\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EContrôles de gestion des données dans le cloud\u003C/td\u003E\r\n\u003Ctd\u003EContrôles de gouvernance pour les environnements de données cloud et hybrides\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAlignement de la gouvernance IT\u003C/td\u003E\r\n\u003Ctd\u003ERisques, contrôles, responsabilisation et gouvernance d’entreprise\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAlignement de l’architecture d’entreprise\u003C/td\u003E\r\n\u003Ctd\u003EArchitecture de données, dépendances applicatives et gouvernance de l’architecture\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EPrincipes FAIR\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ERéutilisation des données scientifiques et de recherche\u003C/td\u003E\r\n\u003Ctd\u003ETrouvabilité, accessibilité, interopérabilité et réutilisation\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.iso.org/standard/81745.html\"\u003EISO 8000\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EQualité des données et données de référence\u003C/td\u003E\r\n\u003Ctd\u003EExigences de qualité, échanges de données et pratiques relatives aux données de référence\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://datagovernance.com/the-dgi-data-governance-framework/\"\u003ELe framework de gouvernance des données de la DGI\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EConception du programme de gouvernance\u003C/td\u003E\r\n\u003Ctd\u003EDroits décisionnels, responsabilisation et processus relatifs aux politiques\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EUn organisme du secteur de la santé pourrait utiliser le DAMA-DMBOK pour définir ses capacités fondamentales de gestion des données, le CDMC pour cartographier les contrôles cloud, et les exigences de gouvernance des données de la certification HDS (Hébergement de Données de Santé) pour définir les attentes en matière d'accès, de conservation et d'audit.\u003C/p\u003E\r\n\u003Cp\u003ELes frameworks fournissent une structure, mais un programme a encore besoin de propriétaires, de métadonnées, de classification, de règles de qualité, de politiques d’accès, de processus d’audit et de technologies capables d’appliquer les décisions lorsque les données sont utilisées.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/\"\u003EEn savoir plus sur les frameworks de gouvernance des données →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-operating-models":{"id":"title-v2-af8c1d28d1","additionalClasses":"anchor-title anchor-title--data-governance-operating-models","type":"heading2","lines":["Modèles opérationnels de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-governance-operating-models_0":{"id":"text-7b259ad024","text":"\u003Cp\u003EUn programme de gouvernance a besoin d’un modèle opérationnel adapté au fonctionnement de l’entreprise. Une entreprise mondiale comptant des dizaines de directions métiers ne peut pas gouverner chaque table via une équipe centrale unique, mais un modèle entièrement décentralisé pourrait entraîner des définitions incohérentes, des politiques redondantes et un niveau de contrôle hétérogène.\u003C/p\u003E\n\u003Cp\u003ELa plupart des entreprises choisissent l’un des trois modèles suivants :\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EModèle\u003C/th\u003E\u003Cth\u003EFonctionnement\u003C/th\u003E\u003Cth\u003EUsage le plus adapté\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ECentralisé\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EUne équipe centrale de gouvernance définit les politiques, les normes et les approbations.\u003C/td\u003E\u003Ctd\u003EProgrammes plus petits, données fortement réglementées ou gouvernance à un stade initial\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EFédéré\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ELes domaines sont propriétaires de leurs données au niveau local, tout en respectant des normes de gouvernance partagées.\u003C/td\u003E\u003Ctd\u003EGrandes entreprises dotées d’une forte responsabilisation des domaines\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EHybride\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EUne équipe centrale définit les politiques et les normes de la plateforme, tandis que les domaines assurent la gouvernance opérationnelle au quotidien.\u003C/td\u003E\u003Ctd\u003EProgrammes d'entreprise à forte maturité\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EUn modèle hybride est souvent le plus pratique. Une équipe centrale définit les normes de classification, les modèles de politique, les exigences en matière de catalogue et les attentes en matière d’audit. Les équipes de domaine sont propriétaires de leurs produits de données : elles gèrent les définitions, résolvent les problèmes de qualité et valident les accès en fonction du contexte local.\u003C/p\u003E\n\u003Cp\u003EQuel que soit le modèle utilisé par une entreprise, les droits décisionnels doivent être explicites. Si deux équipes définissent différemment le terme « client actif », le modèle de gouvernance doit spécifier qui résout le conflit. Si un partenaire demande l’accès à un jeu de données sensibles, le modèle doit identifier qui approuve la demande, quelles preuves sont requises et comment la décision est enregistrée.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_data-governance-operating-models_0":{"id":"card-v2-e3d61f80c6","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"image":{"id":"image","alt":"Town of Gilbert Logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","height":"351","width":"624","lazyEnabled":true,":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["TÉMOIGNAGE CLIENT : Ville de Gilbert"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/town-of-gilbert/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire l’étude de cas complète"},"text":{"id":"text","text":"\u003Cp\u003ELa Ville de Gilbert a lancé 11 tableaux de bord destinés au public avec Snowflake afin d’améliorer la transparence des services de prévention de la criminalité, d’intervention en santé mentale, de réparation de nids-de-poule et de recyclage, par exemple. La ville s’appuie sur des fonctionnalités de gouvernance et de sécurité telles que le contrôle des accès basé sur les rôles, les champs de métadonnées et le masquage dynamique des données pour garantir un accès de confiance aux données sensibles du secteur public.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal",":type":"snowflake-site/components/card-v2"},"title_the-core-components-of-data-governance":{"id":"title-v2-7a8c12343a","additionalClasses":"anchor-title anchor-title--the-core-components-of-data-governance","type":"heading2","lines":["Les composants essentiels de la gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_the-core-components-of-data-governance_0":{"id":"text-060db184d4","text":"\u003Cp\u003EUn programme de gouvernance des données repose sur des principes et des frameworks, mais il s’appuie sur des composants opérationnels spécifiques. Ces composants rendent la gouvernance visible dans les systèmes que les utilisateurs utilisent au quotidien : catalogues, balises, graphiques de traçabilité, politiques d’accès, contrôles de qualité, workflows de gestion et pistes d’audit.\u003C/p\u003E\r\n\u003Ch3\u003EGestion des métadonnées\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/metadata-management/\"\u003ELes métadonnées\u003C/a\u003E sont le contexte qui indique aux utilisateurs et aux systèmes ce qu’est un actif de données, d’où il provient et comment il doit être utilisé. Elles peuvent spécifier le nom d’une table, le type de colonne, le propriétaire, la définition métier, l’étiquette de sensibilité, l’objectif de fraîcheur, le parcours de traçabilité, les profils d’utilisation ou la structure de coûts.\u003C/p\u003E\r\n\u003Cp\u003ELa plupart des programmes de gouvernance reposent sur trois types de métadonnées :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003ELes \u003Cb\u003Emétadonnées métier\u003C/b\u003E couvrent les définitions, les propriétaires, les domaines, les termes du glossaire et le statut de certification. Elles aident les équipes à comprendre si un actif de données est pertinent et approuvé pour son utilisation.\u003C/li\u003E\r\n\u003Cli\u003ELes \u003Cb\u003Emétadonnées techniques\u003C/b\u003E couvrent les schémas, les types de données, les transformations, les dépendances et la traçabilité. Elles permettent aux ingénieurs et architectes de comprendre comment les données circulent et évoluent.\u003C/li\u003E\r\n\u003Cli\u003ELes \u003Cb\u003Emétadonnées opérationnelles\u003C/b\u003E couvrent la fraîcheur des données, l’usage, les coûts, les résultats de qualité et les profils d’accès. Elles aident les équipes à vérifier si les données sont à jour, fiables et utilisées de manière appropriée.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/vs-technical-governance/\"\u003EFaire la différence entre gouvernance des données et gouvernance technique →\u003C/a\u003E\u003C/p\u003E\r\n\u003Ch3\u003EClassification des données\u003C/h3\u003E\r\n\u003Cp\u003ELa classification des données associe des étiquettes aux données en fonction de leur niveau de sensibilité, de leur domaine métier, des réglementations applicables, y compris la \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-data-sovereignty/\"\u003Esouveraineté des données\u003C/a\u003E (un enjeu crucial en France pour répondre aux exigences d'immunité extraterritoriale), ou de leurs usages autorisés. Par exemple, une colonne peut être étiquetée comme contenant des PII, des données de santé protégées, des données de carte de paiement, des données financières confidentielles ou des données d'entraînement approuvées. Ces étiquettes pilotent ensuite de manière dynamique les revues d’accès, les politiques de masquage, les règles de conservation, les approbations de partage ainsi que les restrictions d’usage par l’IA.\u003C/p\u003E\r\n\u003Cp\u003ELa classification est particulièrement importante, car les données sensibles sont rarement isolées au même endroit. Les adresses e-mail, les identifiants clients, les codes de diagnostic, les données de géolocalisation et le détail des transactions circulent fréquemment à travers les pipelines, les tableaux de bord et les tables applicatives. Un programme de gouvernance doit identifier ces domaines avant de pouvoir les protéger de manière cohérente.\u003C/p\u003E\r\n\u003Ch3\u003ECatalogue de données\u003C/h3\u003E\r\n\u003Cp\u003EUn \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-catalog/\"\u003Ecatalogue de données\u003C/a\u003E constitue l’inventaire interrogeable qui rend la gouvernance opérationnelle. Il offre aux analystes, aux ingénieurs, aux data stewards et aux utilisateurs professionnels un endroit où trouver des actifs de données, lire les définitions, examiner la traçabilité, vérifier les propriétaires, inspecter les signaux de qualité et demander l’accès.\u003C/p\u003E\r\n\u003Cp\u003ELes catalogues modernes mettent en évidence les produits de données certifiés, y associent le contexte de gouvernance, indiquent si une table est suffisamment fraîche pour être exploitée et permettent aux équipes d’éviter la duplication de jeux de données similaires. Un bon catalogue répond à des questions concrètes avant même qu’un utilisateur ne rédige une requête :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EQue signifie cette table ?\u003C/li\u003E\r\n\u003Cli\u003EÀ qui appartient-elle ?\u003C/li\u003E\r\n\u003Cli\u003EEst-elle approuvée ?\u003C/li\u003E\r\n\u003Cli\u003EQuels actifs en aval en dépendent ?\u003C/li\u003E\r\n\u003Cli\u003EContient-elle des données sensibles ?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ETraçabilité des données\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-lineage/\"\u003ELa traçabilité des données\u003C/a\u003E retrace les données de la source à la consommation. Elle montre comment un champ, une table ou un indicateur circule dans les flux d’ingestion, de transformation, de modélisation, de reporting, de partage et d’IA. La traçabilité peut s’opérer au niveau de la table, en révélant les liens de dépendance avec d’autres tables ou sources ; au niveau de la colonne, en explicitant la transformation ou la réutilisation de champs spécifiques ; ou à l'échelle des systèmes, en cartographiant la circulation des données entre les différents outils, environnements cloud ou plateformes.\u003C/p\u003E\r\n\u003Cp\u003ELorsqu’une colonne réglementée alimente un rapport, un modèle ou un produit de données externe, la traçabilité permet de montrer l'origine, d’en suivre les transformations et d’évaluer l’impact potentiel d’une modification de la source.\u003C/p\u003E\r\n\u003Ch3\u003EGestion des politiques\u003C/h3\u003E\r\n\u003Cp\u003ELa gestion des politiques consiste à transformer les règles de gouvernance en contrôles applicables. Elle inclut les politiques d’accès, les politiques de masquage, les restrictions au niveau des lignes, les règles de rétention, les règles de partage de données, les politiques d’utilisation autorisée et les workflows d’exception.\u003C/p\u003E\r\n\u003Cp\u003EUne politique doit définir qui peut accéder à quelles données, à quelles conditions, à quelle fin et selon quel processus d’examen. Une gestion rigoureuse des politiques inclut également des exceptions : certains utilisateurs peuvent avoir besoin d’un accès temporaire pour un audit, une migration ou la gestion des incidents. La gouvernance doit indiquer qui a approuvé l’exception, pourquoi elle a été accordée et quand elle expire.\u003C/p\u003E\r\n\u003Ch3\u003EQualité des données\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/a-practical-guide-to-data-quality/\"\u003Equalité des données\u003C/a\u003E mesure si les données sont exactes, complètes, cohérentes, à jour, uniques et suffisamment valides pour l’usage prévu. Une table des produits utilisée pour l’expérimentation interne peut avoir un seuil de qualité différent d’une table des revenus utilisée pour le reporting financier ou d’une table des demandes de remboursement utilisée pour l’analyse des données de santé.\u003C/p\u003E\r\n\u003Cp\u003EUne table peut avoir un propriétaire, une définition de glossaire et une politique d’accès, mais si ses enregistrements sont obsolètes ou incomplets, les utilisateurs ne peuvent pas s’y fier. Les programmes modernes intègrent la qualité plus tôt dans le cycle de vie grâce à des contrats de données, des tests de pipeline et une surveillance continue.\u003C/p\u003E\r\n\u003Ch3\u003EConfidentialité et sécurité des données\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/fr/data-governance/data-privacy/\"\u003Econfidentialité des données\u003C/a\u003E régit la manière dont les données personnelles et sensibles sont collectées, utilisées, conservées, partagées et supprimées. La sécurité des données régit la manière dont les données sont protégées contre les accès non autorisés, les usages abusifs ou l'exposition. Tous deux dépendent de la classification, de la propriété, des politiques et de l’auditabilité, c’est pourquoi ils sont généralement gérés dans le même cadre de gouvernance.\u003C/p\u003E\r\n\u003Cp\u003ELes contrôles de confidentialité peuvent inclure la gestion du consentement, les flux de demande de données, les règles de conservation, la tokénisation et le masquage. Les contrôles de sécurité peuvent inclure le contrôle d'accès basé sur les rôles, les politiques d'accès aux lignes, le chiffrement, la surveillance et les procédures de réponse aux incidents. La gouvernance relie ces contrôles aux actifs de données, en indiquant quelles tables contiennent des données sensibles, qui peut y accéder, quelles politiques s’appliquent et si l’utilisation peut faire l'objet d'un audit ultérieur.\u003C/p\u003E\r\n\u003Ch3\u003EPartage de données et collaboration\u003C/h3\u003E\r\n\u003Cp\u003ELa gouvernance des données doit permettre une réutilisation sécurisée entre les différents domaines, partenaires et écosystèmes externes, au lieu de se cantonner au simple contrôle des accès au sein d’un environnement unique.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003ELe data mesh attribue la propriété du domaine tout en préservant les normes de gouvernance fédérée.\u003C/li\u003E\r\n\u003Cli\u003ELes produits de données regroupent les données avec un propriétaire, une définition, un objectif de qualité et un cycle de vie.\u003C/li\u003E\r\n\u003Cli\u003ELes contrats de données définissent les attentes entre les producteurs et les consommateurs, notamment en matière de schéma, de fraîcheur et de qualité.\u003C/li\u003E\r\n\u003Cli\u003ELes clean rooms permettent aux parties de collaborer sur des données gouvernées sans exposer d’enregistrements bruts.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EChaque actif de données partagé comporte des hypothèses : qui le possède, ce qu’il signifie, si elle est à jour, quelles politiques s’appliquent et si le destinataire est autorisé à l’utiliser aux fins prévues. La gouvernance contribue à rendre ces hypothèses explicites et applicables.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-core-components-of-data-governance_0":{"id":"card-v2-3084248bba","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"image":{"id":"image","alt":"Siemens logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--545509bc-55d0-4a6e-836b-9a6f731cd72b/siemens-transparent.png?quality=85&preferwebp=true","height":"351","width":"624","lazyEnabled":true,":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["TÉMOIGNAGE CLIENT : Siemens"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/siemens-1/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire l’étude de cas complète"},"text":{"id":"text","text":"\u003Cp\u003ESiemens, leader technologique mondial, a construit un data mesh à l’échelle de l’entreprise avec Snowflake, permettant ainsi plus de 600 projets de données, l’intégration de milliers d’entrepôts de données et la création d’un Data Cloud évolutif qui alimente l’IA, l’automatisation et la collaboration inter-entreprises.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal",":type":"snowflake-site/components/card-v2"},"title_data-stewardship-and-governance-roles":{"id":"title-v2-14dffa6884","additionalClasses":"anchor-title anchor-title--data-stewardship-and-governance-roles","type":"heading2","lines":["Rôles liés à l’intendance et à la gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-stewardship-and-governance-roles_0":{"id":"text-5a7f4e8e58","text":"\u003Cp\u003ELa gouvernance des données dépend de personnes nommées, disposant de droits de décision clairs. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003EL’intendance des données\u003C/a\u003E est la couche opérationnelle qui relie les décisions de gouvernance aux opérations quotidiennes sur les données. Dans le cadre d’un programme mature, les data stewards travaillent avec les data owners, les data custodians, les responsables de la conformité, les équipes de sécurité et un comité de gouvernance pour maintenir les définitions, surveiller la qualité, revoir les profils d’accès et remonter les conflits entre les domaines.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPoste\u003C/th\u003E\r\n\u003Cth\u003EResponsabilité de la gouvernance\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Data Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDéfinit la stratégie data de l’entreprise, sponsorise le programme de gouvernance et assume la responsabilité exécutive des résultats liés aux données.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData owner\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDétient l’autorité métier sur un domaine de données, un produit de données, un indicateur ou un jeu de données critiques.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData steward\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EGère les définitions, les attentes en matière de qualité, les métadonnées, les conseils d’accès et la résolution des problèmes pour un domaine ou un actif.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData custodian\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EGère l’environnement technique dans lequel les données sont stockées, traitées, sécurisées et maintenues\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EDélégué à la protection des données\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESupervise les obligations de conformité liées aux données personnelles réglementées, notamment lorsque la législation impose la nomination d'un responsable officiel de la protection des données\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Privacy Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDirige une stratégie, une politique et une gestion des risques plus larges en matière de confidentialité dans l’ensemble de l’entreprise\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAnalyste en gouvernance\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAssure la documentation des politiques, la maintenance du catalogue de données, le reporting, le suivi des incidents et le pilotage des indicateurs de gouvernance\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EComité de gouvernance\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ERésoudre les conflits inter-domaines, approuver les normes et prioriser les tâches de gouvernance\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003ELa répartition exacte des rôles varie selon l’entreprise, mais une constante demeure : la gouvernance requiert à la fois une autorité métier et une intendance technique. Les grandes entreprises formalisent souvent cela par le biais d’un comité de gouvernance, de voies d’escalade documentées et d’une gestion au niveau du domaine.\u003C/p\u003E\r\n\u003Cp\u003ELes détails concrets comptent. Si deux équipes ne sont pas d’accord sur une définition d’indicateur, le programme doit définir qui décide. Si un domaine réglementé a besoin d’une nouvelle politique de masquage, le délégué doit savoir quel partenaire de sécurité ou de confidentialité impliquer. Si un problème de qualité des données affecte un rapport en aval, la traçabilité doit montrer l’impact et l’intendance des données doit déterminer à qui appartient le correctif.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-process-and-strategy":{"id":"title-v2-139ec89cea","additionalClasses":"anchor-title anchor-title--data-governance-process-and-strategy","type":"heading2","lines":["Processus et stratégie de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-governance-process-and-strategy_0":{"id":"text-522871610e","text":"\u003Cp\u003ELe déploiement pratique d’une \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance-strategy/\"\u003Estratégie de gouvernance des données\u003C/a\u003E suit généralement cette séquence :\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EChoisissez un domaine prioritaire :\u003C/b\u003E Commencez par la vision client à 360 degrés, les rapports financiers, les données réglementées, l’analyse de la chaîne d’approvisionnement ou les données d’entraînement pour l’IA, là où le risque métier ou la pression en matière de conformité sont les plus élevés.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EInventaire des actifs de données stratégiques :\u003C/b\u003E Identifiez les tables, les vues, les fichiers, les métriques et rapports qui comptent le plus dans ce domaine.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EClassifier les données sensibles et réglementées :\u003C/b\u003E Étiquetez les PII, les PHI, les données de paiement, les enregistrements confidentiels et d’autres types de données contrôlées.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAffecter les propriétaires et les intendants :\u003C/b\u003E Nommez qui est responsable des définitions, des décisions en matière d’accès, des attentes en matière de qualité et de la résolution des problèmes.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDéfinir des politiques :\u003C/b\u003E Établissez des règles d’accès, de masquage, de conservation, de partage, d’utilisation de l’IA et d’exceptions.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECapture de la traçabilité et des signaux de qualité :\u003C/b\u003E Suivez les flux de données critiques et surveillez l’actualité, l’exhaustivité et la validité.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExaminer l’accès et l’utilisation :\u003C/b\u003E Utiliser les pistes d’audit pour valider qui a accédé aux données sensibles et si les politiques ont fonctionné comme prévu.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDévelopper domaine par domaine :\u003C/b\u003E Réutilisez les normes, les modèles et les leçons apprises à mesure que le programme se développe.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003ELes indicateurs de réussite utiles incluent l’adoption du catalogue, le pourcentage d'actifs de données critiques avec les propriétaires, la couverture de classification, la couverture des politiques, le temps de résolution des problèmes de qualité, l’achèvement de l’examen d’accès et la réduction des résultats d’audit.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/best-practices/\"\u003EEn savoir plus sur les bonnes pratiques de gouvernance des données →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-and-regulatory-compliance":{"id":"title-v2-46564bcfca","additionalClasses":"anchor-title anchor-title--data-governance-and-regulatory-compliance","type":"heading2","lines":["Gouvernance et conformité réglementaire"],":type":"snowflake-site/components/title-v2"},"text_data-governance-and-regulatory-compliance_0":{"id":"text-294de8579a","text":"\u003Cp\u003ELes autorités de régulation peuvent demander si une entreprise peut montrer quelles données réglementées elle détient, qui y a accédé, comment elles ont été protégées, pendant combien de temps elles ont été conservées et si les contrôles requis ont été appliqués. La gouvernance soutient \u003Ca href=\"https://www.snowflake.com/en/fundamentals/governance-risk-compliance/\"\u003Ele GRC\u003C/a\u003E en reliant les actifs de données aux politiques, propriétaires, contrôles et pistes d’audit.\u003C/p\u003E\r\n\u003Cp\u003EEn voici quelques exemples :\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EObligation de gouvernance\u003C/th\u003E\r\n\u003Cth\u003EExemples de réglementations ou de normes\u003C/th\u003E\r\n\u003Cth\u003ECe que prouve la gouvernance\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EProtéger les données personnelles et sensibles\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ERGPD, CCPA/CPRA, LGPD, PDPA, HIPAA\u003C/td\u003E\r\n\u003Ctd\u003EQuelles données personnelles existent, où elles sont stockées, qui peut y accéder et comment les demandes d’exercice de droits sont traitées.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMaintenir l’intégrité des rapports\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESOX, BCBS 239, Basel III\u003C/td\u003E\r\n\u003Ctd\u003EComment les données financières ou de risques sont définies, transformées, contrôlées et rapprochées.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EProtéger les données de paiement\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPCI DSS\u003C/td\u003E\r\n\u003Ctd\u003EOù apparaissent les données des titulaires de carte et quels contrôles s’appliquent\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGérer la résilience opérationnelle\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDORA, NIS2\u003C/td\u003E\r\n\u003Ctd\u003ESurveillance des systèmes critiques, des tiers et des risques liés aux technologies de l’information et de la communication (TIC)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGouverner l’utilisation des données liées à l’IA\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe règlement européen sur l'IA et les réglementations émergentes sur l'IA\u003C/td\u003E\r\n\u003Ctd\u003EQuelles données sont utilisées dans les systèmes d’IA, si elles sont appropriées et comment l’utilisation à haut risque est contrôlée\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003ELe \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://artificialintelligenceact.eu/fr/\"\u003ERèglement européen sur l'IA (EU AI Act)\u003C/a\u003E est un exemple utile qui illustre l’importance des calendriers de mise en conformité en matière de gouvernance. Le règlement entre en vigueur de manière progressive : les dispositions générales et les interdictions prennent effet dès le 2 février 2025, les règles relatives aux systèmes d'IA à usage général s'appliquent à compter du 2 août 2025, et un déploiement plus large se poursuivra jusqu'au 2 août 2027. Pour les entreprises qui utilisent des données d’entreprise gouvernées dans des systèmes d’IA, cela crée un besoin pratique de comprendre la provenance des données d’entraînement, la classification des données sensibles, les autorisations d’accès et l’auditabilité.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ERegardez comment les fonctionnalités de Snowflake, telles que la surveillance des données sensibles, permettent de détecter et d’obtenir une vue complète de vos données sensibles en quelques clics.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-and-regulatory-compliance_0":{"id":"embed-59ab32c76a","youtubeVideoId":"HeZUJ1aBuz0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_why-run-data-governance-on-snowflake":{"id":"title-v2-87842e4d41","additionalClasses":"anchor-title anchor-title--why-run-data-governance-on-snowflake","type":"heading2","lines":["Pourquoi piloter la gouvernance des données au sein de Snowflake ?"],":type":"snowflake-site/components/title-v2"},"text_why_run_data_go":{"id":"text-e137cdae22","text":"\u003Cp\u003ELa gouvernance des données est plus facile à mettre en œuvre lorsque les politiques, les métadonnées, la traçabilité, le suivi de la qualité et les contrôles d’accès se trouvent à proximité de l’endroit où les données sont stockées, traitées, partagées et utilisées. Les fonctionnalités de gouvernance de Snowflake sont intégrées dans l’environnement même où les entreprises gèrent les données, les applications et les workloads d’IA, au lieu d’être appliquées via un outil distinct qui doit rester synchronisé.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ECatalogue unifié avec traçabilité intégrée :\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E fournit le catalogue, la traçabilité au niveau des colonnes, les métadonnées actives et l’application des politiques sur une surface unique. Snowflake Horizon Catalog peut réduire le besoin d'un outil de catalogue dédié, car le contexte de gouvernance est hébergé au plus près des données.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa conformité dès la conception :\u003C/b\u003E \u003Ca href=\"https://trust.snowflake.com/\"\u003ELe centre de conformité\u003C/a\u003E Snowflake assure la surveillance des postures de sécurité en plus des attestations qui soutiennent les normes telles que \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/index.html\"\u003EHIPAA\u003C/a\u003E, PCI DSS, SOC 2 Type II, ISO 27001, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.fedramp.gov/\"\u003EFedRAMP\u003C/a\u003E Modéré et IRAP. Selon le mode de mise en œuvre, certaines fonctionnalités ainsi que des configurations spécifiques au client peuvent être requises.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EApproche Policy-as-Code pour les données sensibles :\u003C/b\u003E Le masquage dynamique des données, les politiques d'accès aux lignes, le masquage basé sur des étiquettes et la tokenisation externe appliquent des contrôles de protection au niveau de la couche de données. Avec une configuration appropriée, la classification peut aider les politiques à s’appliquer uniformément aux requêtes, aux applications, au partage et aux workflows d’IA.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAuditabilité des usages :\u003C/b\u003E L’historique des accès et l’historique des requêtes aident à capturer des enregistrements détaillés d’accès et de transformation qui soutiennent les audits et les examens réglementaires. Lorsqu'ils sont correctement configurés, les entreprises peuvent plus facilement identifier l’auteur et le moment de chaque accès à une colonne sensible.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EConçu avec des contrôles de gouvernance pour les workflows d’IA :\u003C/b\u003E Cortex Guard applique des contrôles de politique aux entrées et sorties des LLM afin de réduire le risque d’introduction de données sensibles dans des contextes de modèles inappropriés. Les fonctions d’indicateurs de données peuvent être utilisées pour surveiller la qualité des données d’entraînement au fil du temps, afin que les données injectées dans les systèmes d'IA respectent les mêmes standards que les données de reporting.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPartage gouverné sans copies :\u003C/b\u003E Secure Data Sharing, les annonces et les data clean rooms permettent aux entreprises de partager des informations avec leurs partenaires et collaborateurs externes sans déplacer les données brutes au-delà des frontières de sécurité. Les contrôles de gouvernance peuvent rester plus faciles à appliquer de manière cohérente, car les données ne quittent pas la plateforme.\u003C/p\u003E\r\n\u003Cp\u003EEnsemble, ces fonctionnalités offrent une surface de gouvernance unifiée qui englobe les entrepôts de données, les data lakes, les formats de table ouverts (dont Iceberg), les applications et l'IA. Ainsi, il devient inutile de recréer ces contrôles à chaque fois que la donnée migre vers un nouvel environnement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-governance-as-an-operating-discipline":{"id":"title-v2-7bfc58b9ed","additionalClasses":"anchor-title anchor-title--build-governance-as-an-operating-discipline","type":"heading2","lines":["Faire de la gouvernance une discipline opérationnelle"],":type":"snowflake-site/components/title-v2"},"text_build-governance-as-an-operating-discipline_0":{"id":"text-43419a8c4b","text":"\u003Cp\u003ELa maturité en matière de gouvernance n’est pas un état binaire. La plupart des programmes couvrent certains domaines et présentent des lacunes dans d’autres. Les lacunes ont tendance à devenir visibles au pire moment possible. Une constatation de conformité peut exposer une colonne qui n’a jamais été classifiée. De même, les résultats d'une IA peuvent être remis en question sans que personne ne soit en mesure de retracer les données qui y ont contribué.\u003C/p\u003E\r\n\u003Cp\u003ELes entreprises qui évitent ces situations ne sont pas celles qui ont terminé une \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/implementation/\"\u003Emise en œuvre de gouvernance\u003C/a\u003E. Ce sont celles qui ont intégré les contrôles de propriété, de traçabilité, de classification et d’audit dans leur fonctionnement, de sorte que, lorsque la question se pose, la réponse existe déjà.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/use-cases/\"\u003EEn savoir plus sur les cas d’usage de la gouvernance des données →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-governance-as-an-operating-discipline_0":{"id":"text-07d8144615","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EÀ RETENIR\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa gouvernance des données n'est pas un projet ponctuel, mais une discipline permanente qui permet de garantir la fiabilité, l'exploitabilité et la traçabilité des données à grande échelle. En définissant clairement les responsabilités, en mettant en place des politiques cohérentes et en assurant la traçabilité, la qualité et l'accessibilité des données, les entreprises peuvent répondre à des questions essentielles concernant leurs données avant même que des problèmes ne surviennent. Face à l’expansion de l’utilisation des données et de l’IA, l’intégration de la gouvernance dans les workflows quotidiens, plutôt que de la traiter comme une couche de contrôle distincte, est ce qui permet aux équipes d’avancer plus rapidement tout en réduisant les risques.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-8005e2febb",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-8876a6766a","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EDans ce guide\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-67b0e38393","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-data-governance\"\u003EQu’est-ce que la gouvernance des données ?\u003C/li\u003E\n\u003Cli data-anchor=\"why-data-governance-matters-now\"\u003EPourquoi la gouvernance des données est importante\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-for-ai\"\u003EGouvernance pour l’IA\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-principles\"\u003EPrincipes de la gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-frameworks-and-standards\"\u003ECadres et normes de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-operating-models\"\u003EModèles opérationnels de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"the-core-components-of-data-governance\"\u003ELes composants essentiels de la gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"data-stewardship-and-governance-roles\"\u003ERôles liés à l’intendance et à la gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-process-and-strategy\"\u003EProcessus et stratégie de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-and-regulatory-compliance\"\u003EGouvernance et conformité réglementaire\u003C/li\u003E\n\u003Cli data-anchor=\"why-run-data-governance-on-snowflake\"\u003EPourquoi piloter la gouvernance des données au sein de Snowflake ?\u003C/li\u003E\n\u003Cli data-anchor=\"build-governance-as-an-operating-discipline\"\u003EFaire de la gouvernance une discipline opérationnelle\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-f293c2fcca","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes réponses de nos experts Snowflake à vos questions les plus fréquentes sur la gouvernance des données.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-d6e7707eaa","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Quelle est la différence entre gouvernance des données et gestion des données ?","richText":"\u003Cp\u003ELa gestion des données englobe l’ensemble des processus de collecte, de stockage, de transformation, d’intégration et de mise à disposition des données. La gouvernance des données établit le cadre de référence de ces activités : elle détermine la propriété des données, leur définition sémantique, les droits d’accès et d'usage, les modalités de mesure de la qualité, ainsi que les mécanismes de démonstration de la conformité. Pour plus de détails, lisez notre guide sur la \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance-vs-data-management/\"\u003Egouvernance des données\u003C/a\u003E.\u003C/p\u003E\n"},{"title":"Qui est responsable de la gouvernance des données ?","richText":"\u003Cp\u003ELa responsabilité stratégique incombe souvent à un Chief Data Officer ou à un responsable similaire, mais la responsabilité opérationnelle au quotidien est répartie entre les data owners, les data stewards, les data custodians, les équipes de sécurité, de confidentialité, de conformité et les comités de gouvernance.\u003C/p\u003E\n"},{"title":"Quels sont les plus grands défis en matière de gouvernance des données ?","richText":"\u003Cp\u003ELes défis les plus complexes sont généralement d’ordre organisationnel : un manque de clarté quant à la propriété des données, un faible sponsorship exécutif, l’incohérence des définitions et une gouvernance traitée comme un sujet purement informatique plutôt que comme un enjeu métier. Les problèmes techniques tels que les métadonnées incomplètes, la traçabilité limitée ou la classification inégale sont plus faciles à résoudre une fois la responsabilisation et le processus clairement définis.\u003C/p\u003E\n"},{"title":"Comment la gouvernance des données prend-elle en charge l’IA ?","richText":"\u003Cp\u003ELa gouvernance des données contrôle les données que les systèmes d’IA utilisent. Elle aide les équipes à comprendre la provenance des données, à classer les champs sensibles, à appliquer les politiques d'accès, à piloter la qualité, à documenter les utilisations autorisées et à retracer les sources de données qui contribuent aux résultats ou aux décisions générés par l'IA.\u003C/p\u003E\n"},{"title":"La gouvernance des données est-elle nécessaire pour garantir la conformité ?","richText":"\u003Cp\u003ELa plupart des réglementations n’imposent pas de modèle de gouvernance spécifique, mais la mise en conformité exige généralement le déploiement de capacités de gouvernance. Les entreprises doivent savoir quelles données réglementées elles détiennent, où elles se trouvent, qui peut y accéder, comment elles sont protégées et si elles peuvent produire des preuves lors d’un audit.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_623474982":{"id":"flexible-column-container-b9556e852f","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c9bc831731",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-34865dff40","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Découvrez nos ressources sur la gouvernance des 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la suite"},"headline":{"id":"title","type":"heading5","lines":["5 composants essentiels pour une gouvernance des données efficace"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"resource_chip_1":{"id":"content-chip-6f7f4feb85","tagText":"RAPPORT","tagColor":"#C6EDF1","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/resource/best-practices-report-achieving-scalable-agile-and-comprehensive-data-management-and-data-governance/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Mettre en place une gestion et une gouvernance des données évolutives, agiles et complètes"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"resource_chip_2":{"id":"content-chip-e5487fcf97","tagText":"WEBINAIRE","tagColor":"#29B5E8","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/webinars/virtual-hands-on-labs/unify-your-governance-strategy-with-snowflake-horizon-2024-09-25/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Unifiez votre stratégie de gouvernance avec Snowflake Horizon Catalog"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"resource_chip_3":{"id":"content-chip-e2a8ff464d","tagText":"FONCTIONNALITÉ","tagColor":"#71D3DC","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/product/features/horizon/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"En savoir plus"},"headline":{"id":"title","type":"heading5","lines":["Snowflake Horizon Catalog"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"}},":itemsOrder":["resource_chip_0","resource_chip_1","resource_chip_2","resource_chip_3"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1026756049":{"id":"flexible-column-container-f67534fad7","propertiesId":"hub-explore-topics-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ba35fca2df",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy_copy":{"id":"title-v2-dd476c72d5","additionalClasses":"hub-explore-topics-header__headline","type":"heading2","lines":["Explorez les sujets liés à la gouvernance des données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-6c701bafbb","additionalClasses":"hub-explore-topics-header__subheadline","text":"\u003Cp\u003EAnalyses approfondies de tous les aspects de la gouvernance des données\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1337071273":{"id":"flexible-column-container-c9cce264ca","propertiesId":"hub-explore-topics-grid","type":"3-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-025ae8fbd7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-8df931cab2","configurationStatus":{"configured":true,"message":""},"type":"content-card","title":{"id":"title","type":"heading4","lines":["Frameworks 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