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données",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-f6fd9c42da-item-4897ed08f5","link":{"valid":true,"url":"/fr/data-governance/frameworks/"},"active":true,"current":true,"title":"Frameworks",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}}},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1522565878":{"id":"flexible-column-container-5f16930aca","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-55629f80ea",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":itemsOrder":["text_308678774","title_v2","text","container"],":items":{"text_308678774":{"id":"text-8e34e88cbd","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuide de référence\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-c1eb41e110","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Frameworks de gouvernance des données : de la règle à la responsabilisation"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-c900bc6df3","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EUn guide complet des principaux frameworks de gouvernance des données, de leurs composants essentiels et de la manière dont les entreprises mettent en œuvre la gouvernance dans leurs environnements de données, de cloud et d’IA.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-4cb2a10ca8",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":itemsOrder":["content_chip","content_chip_copy"],":items":{"content_chip":{"id":"content-chip-5324a9d725","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Laurie Macpherson","width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie Macpherson","Staff Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-63b385831c","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/tracy-kabuya/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Tracy Kabuya","width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributeur local, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}}}}},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-3f79a39c74",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["youtube"],":items":{"youtube":{"id":"embed-2b92c39b69","youtubeVideoId":"SI4XqQCrQEI","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"}}},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_205440979":{"id":"flexible-column-container-07f87b4ec1","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-4779e5c7ea",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["text_894059747","text"],":items":{"text_894059747":{"id":"text-dbfde9b823","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EFrameworks de gouvernance des données populaires :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-fd64c721be","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/fair-principles/\"\u003EPrincipes FAIR\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}}},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_549182334":{"id":"flexible-column-container-34d9a96a80","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",":itemsOrder":["text","text_overview_0","title_what-is-a-data-governance-framework","text_what-is-a-data-governance-framework_0","title_data-governance-framework-purpose-and-value","text_data-governance-framework-purpose-and-value_0","quote_data-governance-framework-purpose-and-value_0","title_core-components-of-a-data-governance-framework","text_core-components-of-a-data-governance-framework_0","title_examples-of-data-governance-frameworks","text_examples-of-data-governance-frameworks_0","title_how-to-evaluate-and-choose-a-data-governance-framework","text_how-to-evaluate-and-choose-a-data-governance-framework_0","card_v2_how-to-evaluate-and-choose-a-data-governance-framework_0","title_the-6-phases-of-data-governance-framework-implementation","text_the-6-phases-of-data-governance-framework-implementation_0","callout_the-6-phases-of-data-governance-framework-implementation_0","text_the-6-phases-of-data-governance-framework-implementation_1","title_implementation-timeline","text_implementation-timeline_0","title_ai-governance-frameworks-and-data-governance","text_ai-governance-frameworks-and-data-governance_0","title_industry-specific-framework-guidance","text_industry-specific-framework-guidance_0","title_framework-maturity-assessment","text_framework-maturity-assessment_0","title_bringing-structure-to-governance-decisions","text_bringing-structure-to-governance-decisions_0","callout_bringing-structure-to-governance-decisions_0"],":items":{"text":{"id":"text-657cbdb95f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDÉFINITION DES FRAMEWORKS DE GOUVERNANCE DES DONNÉES\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELes frameworks de gouvernance des données offrent la structure dont les entreprises ont besoin pour gérer, protéger et gouverner leurs données de manière cohérente dans toute l’organisation. Ils définissent les rôles, les règles, les processus, les contrôles et les indicateurs qui orientent la prise de décision, l’application des règles et la mesure des résultats à grande échelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_overview_0":{"id":"text-30b386f785","text":"\u003Cp\u003ELa plupart des programmes de gouvernance génèrent plus de documentation que de responsabilisation effective. Les règles sont publiées, le catalogue est alimenté, un conseil est formé, mais les données continuent de circuler au sein de l’entreprise de manière opaque, ce qui rend la propriété floue, l’application des politiques incohérente et les preuves d’audit dispersées entre différentes équipes et différents outils.\u003C/p\u003E\n\u003Cp\u003EUn framework de gouvernance des données aide les entreprises à résoudre ce problème opérationnel. Il définit qui prend les décisions relatives aux données, comment ces décisions se traduisent en contrôles exécutoires et comment la gouvernance passe à l’échelle des domaines fonctionnels, des plateformes et des workloads d’IA, plutôt que de laisser les équipes dépendre d’une coordination informelle. Le framework constitue la structure qui relie l’intention des règles de gouvernance à leur application concrète.\u003C/p\u003E\n\u003Cp\u003ECe guide présente le rôle des frameworks de gouvernance des données, les composants essentiels requis par la plupart des programmes, les différences entre les principaux frameworks existants, ainsi que la manière de choisir et de mettre en œuvre le modèle le mieux adapté à votre architecture, votre secteur d’activité et votre niveau de maturité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-data-governance-framework":{"id":"title-v2-bff2fc74ca","additionalClasses":"anchor-title anchor-title--what-is-a-data-governance-framework","type":"heading2","lines":["Qu’est-ce qu’un framework de gouvernance des données ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-data-governance-framework_0":{"id":"text-e7a3c28d05","text":"\u003Cp\u003EUn framework de gouvernance des données est un modèle structuré qui définit la manière dont une entreprise gère, protège, utilise et mesure les données dans l’ensemble de son environnement. Il englobe généralement les principes de gouvernance, les rôles, les politiques, les normes, les processus, les contrôles, les technologies et les indicateurs qui déterminent la gestion des données, de leur création à leur conservation ou suppression.\u003C/p\u003E\n\u003Cp\u003EUn framework ne doit pas être confondu avec une \u003Ca href=\"https://snowflake.com/fr/data-governance/policy/\"\u003Epolitique\u003C/a\u003E, une norme ou un contrôle. Le framework fournit la structure globale permettant à une entreprise de gouverner ses données. Les politiques, les normes internes, les procédures et les contrôles s’inscrivent généralement dans ce framework pour le rendre opérationnel. D’autres normes externes officielles, telles que les normes ISO, peuvent également orienter le framework, voire en constituer le socle.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EUne politique\u003C/b\u003E énonce les attentes de l’entreprise, par exemple en définissant qui peut accéder aux données clients réglementées.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUne norme interne\u003C/b\u003E définit la manière dont cette attente doit être appliquée, comme les règles de classification des identifiants clients, des enregistrements de transactions ou des informations de santé protégées.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUn contrôle\u003C/b\u003E applique ou vérifie le respect de cette exigence, par exemple via le \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rbac/\"\u003Econtrôle d’accès basé sur les rôles\u003C/a\u003E (RBAC), le masquage dynamique, le chiffrement, les examens d’accès ou la journalisation des audits.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUne norme externe officielle\u003C/b\u003E fournit des directives ou des exigences reconnues qui aident à structurer un framework, voire à s’y substituer, à l’instar des normes ISO pour la sécurité, la \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/\"\u003Econfidentialité\u003C/a\u003E ou la gestion des données.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECette distinction est essentielle, car de nombreux programmes de gouvernance échouent par manque de structure opérationnelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-framework-purpose-and-value":{"id":"title-v2-e0dd2faa10","additionalClasses":"anchor-title anchor-title--data-governance-framework-purpose-and-value","type":"heading2","lines":["Objectif et valeur d’un framework de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_data-governance-framework-purpose-and-value_0":{"id":"text-f9b12d43fd","text":"\u003Cp\u003ELes frameworks de gouvernance des données aident les entreprises à prendre des décisions de gouvernance cohérentes entre les équipes, les systèmes et les domaines de données. En l’absence de framework, l’équipe chargée des données clients pourrait gérer les définitions d’une certaine manière, la finance pourrait utiliser un processus d’examen différent, et l’équipe d’IA pourrait suivre la traçabilité des données d’entraînement dans un tableur impossible à auditer pour quiconque en dehors du projet.\u003C/p\u003E\r\n\u003Cp\u003EL’intérêt d’un framework est de transformer la gouvernance, d’un ensemble de pratiques locales, en un véritable modèle opérationnel d’entreprise. Il aide les entreprises à :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EAttribuer les responsabilités liées aux domaines de données, aux tables, aux indicateurs, aux politiques et aux exceptions.\u003C/li\u003E\r\n\u003Cli\u003EDéfinir un langage commun pour la qualité, la propriété, la classification, la \u003Ca href=\"https://snowflake.com/en/data-governance/data-lineage/\"\u003Etraçabilité\u003C/a\u003E et le risque.\u003C/li\u003E\r\n\u003Cli\u003EAligner les initiatives de gouvernance sur les objectifs de conformité, d’analytique, d’IA, de sécurité et d’exploitation.\u003C/li\u003E\r\n\u003Cli\u003EFavoriser une application plus homogène des contrôles sur l’ensemble des produits de données, des pipelines, des applications et des modèles.\u003C/li\u003E\r\n\u003Cli\u003EDocumenter les décisions d’accès, les exceptions aux politiques et les activités de remédiation à des fins d’audit.\u003C/li\u003E\r\n\u003Cli\u003EMesurer si la gouvernance s’améliore, et non pas seulement constater l'existence d'artefacts.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EUn framework aide également les équipes de gouvernance à éviter deux écueils courants : un programme axé sur les règles qui produit de la documentation mais peu d’application réelle, et un programme axé sur les outils qui catalogue les actifs sans clarifier la responsabilité décisionnelle. Les frameworks les plus utiles relient le modèle opérationnel à l’environnement technique, de sorte que l’approbation d’un data steward, une étiquette de classification, un parcours de lignage et une politique d’accès fonctionnent en synergie.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_data-governance-framework-purpose-and-value_0":{"id":"quote-item-78829eff65","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"Le véritable moteur de la gouvernance de l’IA et des données est déjà à l’œuvre : l’exigence des clients.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_core-components-of-a-data-governance-framework":{"id":"title-v2-bd104665dc","additionalClasses":"anchor-title anchor-title--core-components-of-a-data-governance-framework","type":"heading2","lines":["Composants clés d’un framework de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_core-components-of-a-data-governance-framework_0":{"id":"text-9a4b4e5888","text":"\u003Cp\u003EBien que la plupart des frameworks de gouvernance des données utilisent un langage différent, ils couvrent généralement un ensemble commun de composants. Le modèle exact dépend de l’organisation, mais un framework pratique doit généralement définir les domaines suivants :\u003C/p\u003E\r\n\u003Ch3\u003EStratégie et principes de gouvernance\u003C/h3\u003E\r\n\u003Cp\u003ELe framework doit expliquer la raison d’être de la \u003Ca href=\"https://www.snowflake.com/fr/data-governance/\"\u003Egouvernance des données\u003C/a\u003E et les objectifs stratégiques qu’elle soutient. Ces objectifs peuvent ainsi englober la conformité réglementaire, la fiabilité des analyses, la maturité face à l'IA, l'adoption des produits de données, la résilience opérationnelle ou le partage sécurisé des données. Les principes permettent aux équipes de prendre des décisions lorsqu’une politique ne couvre pas tous les scénarios, par exemple pour déterminer si un attribut sensible doit être masqué, tokenisé, exclu du jeu de variables d’un modèle ou rendu disponible uniquement via une vue approuvée.\u003C/p\u003E\r\n\u003Ch3\u003ERôles et responsabilités\u003C/h3\u003E\r\n\u003Cp\u003ELa gouvernance repose sur une responsabilisation claire. Un framework doit définir les responsabilités des data owners, des \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003Edata stewards\u003C/a\u003E, des data custodians, des membres du conseil de gouvernance, des équipes de sécurité, des équipes chargées de la confidentialité, des équipes de plateforme et des parties prenantes métier. Il doit également définir les droits décisionnels, par exemple qui approuve un nouveau domaine de données, qui résout un conflit de définition d’indicateur et qui peut accorder une dérogation à une règle de conservation ou d’accès.\u003C/p\u003E\r\n\u003Ch3\u003EPolitiques, normes et procédures\u003C/h3\u003E\r\n\u003Cp\u003ELes politiques fixent les règles relatives à la création, à la classification, à l’accès, à l’utilisation, au partage, à la conservation et à la suppression des données. Les normes concrétisent ces règles en définissant les niveaux de classification approuvés, les conventions de nommage, les seuils de qualité, les exigences en matière de métadonnées, les catégories de conservation et les modèles d’accès. Les procédures décrivent la manière dont les équipes effectuent le travail, comme l’intégration d’un nouveau produit de données, l’examen des demandes d’accès ou la résolution d’un problème de qualité.\u003C/p\u003E\r\n\u003Ch3\u003EGestion de la qualité des données\u003C/h3\u003E\r\n\u003Cp\u003ELe framework doit définir la manière dont la qualité est mesurée, surveillée et corrigée. Les dimensions courantes comprennent l’exactitude, l’exhaustivité, la cohérence, la fraîcheur, la validité et l’unicité. Concrètement, cela consiste à identifier les données critiques, à leur associer des règles de qualité, à surveiller les anomalies et à définir les responsabilités d'investigation lorsqu'une métrique de revenu fluctue en raison d'un défaut de mise à jour d'un champ source.\u003C/p\u003E\r\n\u003Ch3\u003EClassification des données et gestion des métadonnées\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-classification/\"\u003Eclassification\u003C/a\u003E et les métadonnées fournissent un contexte destiné à favoriser une utilisation appropriée et conforme des données. Un framework doit définir la manière dont l’entreprise recueille la sensibilité, la signification métier, la propriété, la traçabilité, la fraîcheur, l’usage et le contexte des politiques. Le seul nom d'une table permet rarement de savoir si une colonne contient des identifiants clients, si les données sont autorisées pour l'entraînement d'IA, ou si la définition d'un indicateur a évolué depuis le dernier cycle de reporting.\u003C/p\u003E\r\n\u003Ch3\u003EConfidentialité, sécurité et contrôles d’accès aux données\u003C/h3\u003E\r\n\u003Cp\u003ELes frameworks de gouvernance doivent relier l’intention des politiques aux contrôles de sécurité. Cela inclut la gestion des identités et des accès, le principe du moindre privilège, le chiffrement, le masquage, la sécurité au niveau des lignes, la surveillance, les contrôles de conservation et la conformité aux exigences de confidentialité des données. Le framework doit également définir la manière dont l’accès est demandé, approuvé, examiné et révoqué.\u003C/p\u003E\r\n\u003Ch3\u003EGestion du cycle de vie des données\u003C/h3\u003E\r\n\u003Cp\u003ELes données ont un cycle de vie : création, ingestion, transformation, stockage, usage, partage, conservation, archivage et suppression. Un framework de gouvernance doit définir la manière dont les données évoluent à travers ces étapes, quelles règles de conservation s’appliquent, comment les gels juridiques sont gérés et quels éléments probants attestent que les données ont été conservées ou purgées conformément aux règles.\u003C/p\u003E\r\n\u003Ch3\u003EGestion des données de référence et des données maîtres\u003C/h3\u003E\r\n\u003Cp\u003ELes programmes de gouvernance nécessitent des définitions harmonisées pour leurs entités clés, telles que les clients, les produits, les collaborateurs, les fournisseurs, les sites ou les comptes financiers. La gestion des données de référence et des données maîtres définit les sources de confiance, les règles de survivance et les processus d’intendance des données qui garantissent la cohérence de ces entités à travers les systèmes et les cas d’usage de reporting.\u003C/p\u003E\r\n\u003Ch3\u003EArchitecture des données et normes d’intégration\u003C/h3\u003E\r\n\u003Cp\u003EUn framework doit s’aligner sur l’architecture de l’entreprise, et non se juxtaposer à elle. Cela inclut les normes de modélisation, d’ingestion, de transformation, d’interopérabilité, d’utilisation des API, de partage de données, de couches sémantiques et de conception de produits de données. Les normes architecturales favorisent le passage à l'échelle de la gouvernance, car les équipes peuvent reproduire des modèles éprouvés au lieu de devoir concevoir chaque pipeline ou frontière de domaine à partir d'une page blanche.\u003C/p\u003E\r\n\u003Ch3\u003EGestion de la conformité, des risques et des audits\u003C/h3\u003E\r\n\u003Cp\u003ELes frameworks de gouvernance doivent définir la manière dont les obligations réglementaires, les contrôles internes, les risques, les exceptions et les preuves d’audit sont suivis. C’est particulièrement important dans les secteurs où les organismes de réglementation attendent des entreprises qu’elles démontrent non seulement que des contrôles sont en place, mais aussi qu’ils fonctionnent comme prévu.\u003C/p\u003E\r\n\u003Ch3\u003EOutils et technologies\u003C/h3\u003E\r\n\u003Cp\u003ELa technologie ne crée pas la gouvernance à elle seule, mais elle permet de la mettre à l’échelle. \u003Ca href=\"https://snowflake.com/fr/data-governance/data-catalog/\"\u003ELes catalogues de données\u003C/a\u003E, les outils de lignage, les systèmes de gouvernance des accès, les plateformes de qualité des données, les moteurs de règles et les outils de surveillance permettent de capturer les métadonnées, d’appliquer des contrôles, de faire remonter les anomalies et de conserver des preuves. Le framework doit préciser quels systèmes détiennent les métadonnées de référence et comment les décisions de gouvernance sont transposées en contrôles techniques.\u003C/p\u003E\r\n\u003Ch3\u003EMétriques et amélioration continue\u003C/h3\u003E\r\n\u003Cp\u003EUn framework doit inclure des mesures indiquant si la gouvernance est efficace. Parmi les métriques utiles, on peut citer la couverture du catalogue, la couverture de la classification, les scores de qualité des données, le taux de finalisation des examens d’accès, l’ancienneté des exceptions, le délai de résolution des anomalies, les taux de respect des règles et le pourcentage d’éléments de données critiques ayant un garant désigné.\u003C/p\u003E\r\n\u003Cp\u003EUne façon simple de regrouper ces composants consiste à les diviser en : personnes, règles, processus, contrôles des données, technologies et mesures.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_examples-of-data-governance-frameworks":{"id":"title-v2-c3f613ba60","additionalClasses":"anchor-title anchor-title--examples-of-data-governance-frameworks","type":"heading2","lines":["Exemples de frameworks de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_examples-of-data-governance-frameworks_0":{"id":"text-4e3b910b08","text":"\u003Cp\u003EChaque framework répond à des problématiques différentes. Certains constituent de vastes corpus de connaissances, d’autres sont des modèles de maturité ou des méthodes d’architecture, tandis que certains sont conçus spécifiquement pour le cloud, la recherche ou la conformité réglementaire.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework\u003C/th\u003E\r\n\u003Cth\u003EObjectif principal\u003C/th\u003E\r\n\u003Cth\u003ECas d’usage idéal\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ECorpus complet de connaissances sur la gestion des données\u003C/td\u003E\r\n\u003Ctd\u003EGrandes entreprises ayant besoin d’une couverture étendue des différentes disciplines de gestion des données\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.isaca.org/resources/cobit\"\u003ECOBIT\u003C/a\u003E\u003Cp\u003E \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003EGouvernance et gestion de l’IT d’entreprise\u003C/td\u003E\r\n\u003Ctd\u003EEntreprises qui souhaitent associer la gouvernance des données à la gouvernance IT, aux contrôles, aux risques et aux audits\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://edmcouncil.org/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ENiveau de maturité des capacités de gestion des données\u003C/td\u003E\r\n\u003Ctd\u003EServices financiers et autres entreprises de secteurs réglementés ayant besoin d’évaluer leur niveau de maturité et d’améliorer leurs capacités\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.opengroup.org/togaf\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAlignement de l’architecture d’entreprise\u003C/td\u003E\r\n\u003Ctd\u003EEntreprises qui intègrent la gouvernance des données dans leurs travaux d’architecture, de transformation et de modèle opérationnel\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.go-fair.org/fair-principles/\"\u003EFAIR\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDonnées faciles à trouver, accessibles, interopérables et réutilisables\u003C/td\u003E\r\n\u003Ctd\u003EEnvironnements liés à la recherche, à la science, au secteur public et au partage de données\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://edmcouncil.org/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ECapacités de gestion des données cloud\u003C/td\u003E\r\n\u003Ctd\u003EEntreprises cloud-first, hybrides et multi-cloud ayant besoin de mécanismes de contrôle pour la gestion de leurs données\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Ch3\u003EDAMA-DMBOK\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E est un vaste corpus de connaissances sur la gestion des données qui aide les entreprises à structurer leurs disciplines de gestion des données et à les aligner sur leur stratégie d’entreprise, leur conformité et le changement technologique. DAMA décrit le DMBOK comme une ressource pour structurer, gouverner et optimiser les actifs de données dans des domaines tels que la stratégie, la gouvernance, la qualité, les métadonnées et l’architecture.\u003C/p\u003E\r\n\u003Cp\u003ELe DAMA-DMBOK est souvent utile lorsqu’une entreprise a besoin d’un modèle de référence complet plutôt que d’un framework de contrôle restreint. Il peut aider les équipes à définir la portée d’un programme de gestion des données, à identifier les lacunes et à créer un vocabulaire commun entre les équipes de gouvernance, d’architecture, de qualité et d’intendance des données.\u003C/p\u003E\r\n\u003Ch3\u003ECOBIT\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E, créé par l’ISACA, est un framework de gouvernance pour l’information et la technologie d’entreprise. Il est souvent utilisé par les entreprises qui ont besoin de relier la gouvernance technologique, la gestion des risques, les objectifs de contrôle et les pratiques d’audit. Le champ d’action plus large de l’ISACA en matière de gouvernance IT, d’audit, de risques, de confidentialité et de sécurité rend COBIT pertinent lorsque la gouvernance des données doit s’aligner étroitement sur la supervision informatique de l’entreprise.\u003C/p\u003E\r\n\u003Cp\u003ECOBIT peut s’avérer utile lorsque la gouvernance des données doit s’intégrer dans un environnement de contrôle établi. Par exemple, une entreprise de services financiers pourrait utiliser des processus de gouvernance alignés sur COBIT pour relier les revues d’accès aux données, la gestion des changements, les tests de contrôle et les preuves d’audit.\u003C/p\u003E\r\n\u003Ch3\u003EDCAM\u003C/h3\u003E\r\n\u003Cp\u003ELe Data Management Capability Assessment Model (\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E), développé par l’EDM Council, est un framework de bonnes pratiques visant à évaluer et améliorer les capacités de gestion des données. L’EDM Council décrit le DCAM comme un framework visant à établir, pérenniser et optimiser une démarche mature de gestion des données, qui intègre désormais une prise en charge étendue de l’IA et du cloud.\u003C/p\u003E\r\n\u003Cp\u003ELe DCAM est particulièrement pertinent pour les services financiers et les autres secteurs réglementés, car il met l’accent sur la maturité, l’évaluation des capacités et l’amélioration fondée sur des éléments probants. Il aide les entreprises à identifier les domaines où leurs capacités de gouvernance sont solides, ceux où elles manquent de cohérence et les lacunes à combler en priorité.\u003C/p\u003E\r\n\u003Ch3\u003ETOGAF\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E, géré par The Open Group, est une méthodologie et un framework d’architecture d’entreprise. The Open Group décrit TOGAF comme une méthode détaillée et un ensemble d’outils d’accompagnement pour développer l’architecture d’entreprise, constituant un standard utilisé par de nombreuses grandes entreprises.\u003C/p\u003E\r\n\u003Cp\u003ETOGAF n’est pas un framework de gouvernance des données au sens strict, mais il s’avère précieux lorsque la gouvernance doit être intégrée à l’architecture. Il permet d’aligner la gouvernance des données avec les architectures métier, applicative, de données et technologique, garantissant ainsi que les décisions de gouvernance pilotent la conception et l’évolution du système d’information.\u003C/p\u003E\r\n\u003Ch3\u003EPrincipes FAIR\u003C/h3\u003E\r\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/data-governance/frameworks/fair-principles/\"\u003Eprincipes FAIR\u003C/a\u003E définissent un modèle visant à rendre les données faciles à trouver, accessibles, interopérables et réutilisables. Publiés en 2016, ces principes visent à améliorer la gestion et l’intendance des actifs numériques, en particulier dans les contextes scientifiques et de recherche.\u003C/p\u003E\r\n\u003Cp\u003ELes principes FAIR sont particulièrement utiles lorsque l’objectif est de partager et de réutiliser les données de manière responsable. Une institution de recherche, un organisme de santé ou une agence du secteur public peut utiliser les principes FAIR pour améliorer les métadonnées, les identifiants persistants, les normes d’interopérabilité et les conditions de réutilisation des produits de données.\u003C/p\u003E\r\n\u003Ch3\u003ECDMC\u003C/h3\u003E\r\n\u003Cp\u003ELe framework Cloud Data Management Capabilities (\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E), développé par l’EDM Council, se concentre sur la gestion et le contrôle des données dans les environnements cloud. L’EDM Council décrit le CDMC comme un framework permettant de gérer les données en toute sécurité dans des environnements cloud et multi-cloud. Son modèle comprend des preuves auditables, un système de notation et des capacités de contrôle spécifiques au cloud.\u003C/p\u003E\r\n\u003Cp\u003ELe CDMC est utile lorsque les organisations migrent des workloads de données gouvernées vers des architectures cloud, cloud hybride ou multi-cloud. Il donne aux équipes les moyens d’évaluer les mécanismes de contrôle sur l'ensemble des domaines clés : gouvernance et responsabilisation, catalogage et classification, accessibilité et protection, confidentialité, gestion du cycle de vie des données et architecture technique.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-evaluate-and-choose-a-data-governance-framework":{"id":"title-v2-86d68cf0f2","additionalClasses":"anchor-title anchor-title--how-to-evaluate-and-choose-a-data-governance-framework","type":"heading2","lines":["Comment évaluer et choisir un framework de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_how-to-evaluate-and-choose-a-data-governance-framework_0":{"id":"text-07b9752516","text":"\u003Cp\u003ELe choix d’un ou de plusieurs frameworks commence par l’identification du problème opérationnel de l’organisation. Utilisez les critères suivants pour évaluer l’adéquation :\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ECritère d’évaluation\u003C/th\u003E\n\u003Cth\u003EQuestions à se poser\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EObjectif stratégique\u003C/td\u003E\n\u003Ctd\u003ELe besoin principal concerne-t-il la conformité, la confiance dans les analyses, la préparation à l’IA, la migration vers le cloud, le partage de données ou l’alignement avec l’architecture d’entreprise ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EContexte sectoriel\u003C/td\u003E\n\u003Ctd\u003ELe framework répond-il aux attentes spécifiques de secteurs tels que les services financiers, la santé, le gouvernement, la recherche ou les données du secteur public ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMaturité de la gouvernance\u003C/td\u003E\n\u003Ctd\u003EL’organisation a-t-elle besoin de rôles et de politiques fondatrices, ou a-t-elle besoin de mesurer sa maturité, de tester ses contrôles et d’optimiser ses processus ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EAdéquation avec l’architecture\u003C/td\u003E\n\u003Ctd\u003ELe framework reflète-t-il le patrimoine de données de l’organisation, y compris le cloud, le cloud hybride, le multi-cloud, les formats de table ouverts, les applications et les workloads d’IA ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EExigences de contrôle\u003C/td\u003E\n\u003Ctd\u003ELe framework se traduit-il par des contrôles applicables tels que des politiques d’accès, le masquage, la classification, la traçabilité et la rétention ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EBesoins en matière de preuves\u003C/td\u003E\n\u003Ctd\u003EL’organisation a-t-elle besoin de preuves auditables concernant les approbations, les exceptions, les contrôles de qualité, les examens d’accès et l’application des politiques ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEffort de mise en œuvre\u003C/td\u003E\n\u003Ctd\u003EL’entreprise dispose-t-elle des ressources humaines, des technologies et du soutien de la direction nécessaires pour mettre en œuvre ce framework ?\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EAlignement des outils\u003C/td\u003E\n\u003Ctd\u003ELes catalogues, systèmes de traçabilité, moteurs de politiques, outils de qualité des données et contrôles de plateforme existants peuvent-ils prendre en charge le framework ?\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EDe nombreuses entreprises combinent plusieurs frameworks. Par exemple, une entreprise peut utiliser le DAMA-DMBOK pour définir un large périmètre de gestion des données, le DCAM pour évaluer sa maturité, le CDMC pour les contrôles cloud et le NIST AI RMF pour la gouvernance des risques liés à l’IA.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-to-evaluate-and-choose-a-data-governance-framework_0":{"id":"card-v2-ad0921058b","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire l’étude de cas complète"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Town of Gilbert Logo","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELa ville de Gilbert a lancé 11 tableaux de bord publics avec Snowflake afin d’améliorer la transparence de ses services, notamment en matière de prévention de la criminalité, de soutien en santé mentale, de réparation des nids-de-poule et de recyclage. La ville utilise des fonctionnalités de gouvernance et de sécurité telles que le contrôle d’accès basé sur les rôles (RBAC), les champs de métadonnées et le Dynamic Data Masking pour garantir un accès sécurisé aux données sensibles du secteur public.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_the-6-phases-of-data-governance-framework-implementation":{"id":"title-v2-a0c5d5389e","additionalClasses":"anchor-title anchor-title--the-6-phases-of-data-governance-framework-implementation","type":"heading2","lines":["Les 6 phases de mise en œuvre d’un framework de gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_the-6-phases-of-data-governance-framework-implementation_0":{"id":"text-0d0aaff7a0","text":"\u003Cp\u003EUn framework de gouvernance des données est d’autant plus utile s’il transforme la gestion quotidienne des données. La mise en œuvre doit commencer par un périmètre ciblé, valider le modèle opérationnel, puis s’étendre.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 1 : Établir le leadership de la gouvernance\u003C/h3\u003E\r\n\u003Cp\u003ECommencez par obtenir le soutien de la direction et par définir la structure décisionnelle. Cette structure regroupe généralement un conseil de gouvernance, des data owners par domaine, des data stewards, des propriétaires de plateformes, des parties prenantes de la sécurité et de la confidentialité, ainsi que des représentants des équipes métiers.\u003C/p\u003E\r\n\u003Cp\u003EUne matrice RACI permet de formaliser qui est responsable, garant, consulté et informé pour chaque processus de gouvernance. Par exemple, un data owner est le garant du domaine de données clients, un data steward est chargé de la qualité des métadonnées, l’équipe de sécurité est consultée lors de la conception des politiques d’accès, et les équipes d’analyse en aval sont informées de la modification de la définition d’une métrique.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 2 : Réaliser l’inventaire et la classification des données\u003C/h3\u003E\r\n\u003Cp\u003EL’étape suivante consiste à identifier les données les plus importantes. Répertoriez les tables, vues, produits de données, pipelines, rapports, jeux de données d’entraînement d’IA et actifs partagés en externe les plus stratégiques. Classifiez ensuite ces données en fonction de leur sensibilité, de leur signification métier, de leur pertinence réglementaire, de leur propriété, de leur utilisation et des exigences liées à leur cycle de vie.\u003C/p\u003E\r\n\u003Cp\u003ECette phase doit donner la priorité aux données à forte valeur ajoutée et à haut risque. Les identifiants clients, les données financières réglementaires, les données de santé, les données d'entraînement des modèles d'IA et les indicateurs clés du comité de direction exigent généralement une gouvernance prioritaire par rapport aux logs opérationnels à faible risque.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-6-phases-of-data-governance-framework-implementation_0":{"id":"text-97fe53c368","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDe nombreuses équipes réalisent un inventaire et une classification des données, puis laissent les résultats dormir dans un tableur. Cela apporte de la visibilité, mais pas de responsabilisation. Si une colonne est étiquetée comme sensible, réglementée ou approuvée pour un usage restreint, cette classification doit déclencher des contrôles spécifiques, tels que le masquage, l’accès au niveau des lignes, un circuit d’approbation, des règles de conservation ou la journalisation des audits.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_the-6-phases-of-data-governance-framework-implementation_1":{"id":"text-b6a604dd39","text":"\u003Ch3\u003EPhase 3 : Définir des politiques, des normes et des procédures\u003C/h3\u003E\r\n\u003Cp\u003EUne fois que l’entreprise sait quelles données elle gouverne, elle peut en définir les règles. Les politiques doivent encadrer l’accès, la classification, la qualité et la rétention des données, ainsi que leur partage, leur usage acceptable, l’utilisation de l'IA et la gestion des dérogations. Les normes doivent traduire ces politiques en exigences spécifiques, telles que le renseignement obligatoire de métadonnées, les niveaux de classification approuvés, les conventions de nommage, les seuils de qualité des données et les classes de rétention.\u003C/p\u003E\r\n\u003Cp\u003ELes procédures doivent décrire la manière dont les équipes effectuent le travail de gouvernance. Par exemple, une procédure peut définir la manière dont un nouveau produit de données est approuvé, les champs de métadonnées requis avant sa publication et la manière dont une demande d’accès est acheminée pour examen.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 4 : Mettre en œuvre des contrôles de gouvernance et une application technique\u003C/h3\u003E\r\n\u003Cp\u003ELa mise en œuvre du framework nécessite une application technique. C’est ici que les balises de classification, les politiques d’accès, les politiques de masquage, les contrôles au niveau des lignes, le chiffrement, la traçabilité, la surveillance de la qualité des données et la journalisation des audits s’intègrent à l’environnement gouverné.\u003C/p\u003E\r\n\u003Cp\u003EDans Snowflake, par exemple, les fonctionnalités de gouvernance sont prises en charge par \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E, qui offre des fonctionnalités intégrées de gouvernance, de découverte et de sécurité pour l’AI Data Cloud. Horizon est conçu pour fédérer les moteurs de calcul et les formats, fournir des vues cohérentes des métadonnées et des autorisations, et propager les métadonnées de gouvernance aux workflows de partage de données pris en charge.\u003C/p\u003E\r\n\u003Cp\u003EL’objectif pratique est de réduire l’écart entre l’intention de gouvernance et le comportement réel de la plateforme. Si une colonne est étiquetée comme sensible, les entreprises peuvent configurer des contrôles d’accès et de masquage pour s’aligner sur cette classification, plutôt que de s’en remettre uniquement à l’application manuelle des politiques.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 5 : Piloter et valider les contrôles de gouvernance\u003C/h3\u003E\r\n\u003Cp\u003EPilotez le framework dans un ou deux domaines à forte valeur ajoutée avant de le déployer à l’échelle de l’entreprise. Un projet pilote peut se concentrer sur les données clients, le reporting financier, les données de santé réglementées ou un pipeline de données d’entraînement d’IA.\u003C/p\u003E\r\n\u003Cp\u003ELe pilote doit permettre de vérifier la clarté des rôles, l'exhaustivité des métadonnées, le caractère exécutoire des politiques, la bonne orientation des incidents de qualité et la capacité à fournir des preuves d’audit. Il doit également mettre en évidence les aspects du framework trop complexes, les tâches d’intendance qui manquent de ressources et les contrôles techniques à ajuster.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 6 : Déployer à l’échelle de l’entreprise et optimiser en continu\u003C/h3\u003E\r\n\u003Cp\u003EAprès le pilote, étendez le framework à d’autres domaines, produits de données et directions métiers. Le déploiement à l’échelle doit inclure des formations, des guides d’intégration, de l’automatisation, des indicateurs récurrents et des boucles de rétroaction.\u003C/p\u003E\r\n\u003Cp\u003EL’amélioration continue est essentielle, car les environnements de données évoluent. De nouvelles applications, de nouveaux cas d’usage de l’IA, de nouvelles réglementations, de nouveaux services cloud et de nouvelles définitions métier peuvent tous créer des écarts de gouvernance. Un framework mature permet aux équipes de détecter ces failles et d’ajuster les rôles, les politiques, les contrôles ou les indicateurs avant que la confiance ne s’érode.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_implementation-timeline":{"id":"title-v2-09d28f9766","additionalClasses":"anchor-title anchor-title--implementation-timeline","type":"heading2","lines":["Calendrier de mise en œuvre"],":type":"snowflake-site/components/title-v2"},"text_implementation-timeline_0":{"id":"text-fe3a9ad2e9","text":"\u003Cp\u003ELes calendriers de mise en œuvre varient selon la taille de l’entreprise, les contraintes réglementaires, la complexité des données et la maturité. Un plan pratique ressemble souvent à ceci :\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPhase\u003C/th\u003E\r\n\u003Cth\u003EDurée type\u003C/th\u003E\r\n\u003Cth\u003EPrincipal livrable\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EÉtablir le leadership de la gouvernance\u003C/td\u003E\r\n\u003Ctd\u003E4 à 6 semaines\u003C/td\u003E\r\n\u003Ctd\u003EParrainage stratégique, conseil de gouvernance, définitions des rôles et matrice RACI\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ERéaliser l’inventaire et la classification des données\u003C/td\u003E\r\n\u003Ctd\u003E6 à 8 semaines\u003C/td\u003E\r\n\u003Ctd\u003EInventaire des données critiques, modèle de classification et domaines prioritaires\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EDéfinir des politiques, des normes et des procédures\u003C/td\u003E\r\n\u003Ctd\u003E4 à 6 semaines\u003C/td\u003E\r\n\u003Ctd\u003ERègles de gouvernance, normes et procédures opérationnelles\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EImplémenter les contrôles de gouvernance\u003C/td\u003E\r\n\u003Ctd\u003E8 à 12 semaines\u003C/td\u003E\r\n\u003Ctd\u003ERègles d’accès, tags, masquage, traçabilité, contrôles de qualité et journalisation d’audit\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPiloter et valider les contrôles\u003C/td\u003E\r\n\u003Ctd\u003E6 à 8 semaines\u003C/td\u003E\r\n\u003Ctd\u003ERésultats du pilote, validation des contrôles et plan de remédiation\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMettre à l’échelle et optimiser\u003C/td\u003E\r\n\u003Ctd\u003EEn continu\u003C/td\u003E\r\n\u003Ctd\u003EDéploiement à l’échelle de l’entreprise, métriques et rythme d’amélioration continue\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E\u003Cspan\u003ELes 90 premiers jours doivent cibler des avancées concrètes plutôt qu’une exhaustivité globale. Parmi les premières victoires utiles, citons l’attribution de propriétaires pour les domaines de données stratégiques, la classification des colonnes sensibles, la publication d’un ensemble restreint de définitions de métriques approuvées, la mise en œuvre de revues d’accès pour les données à haut risque et la création d’un tableau de bord de gouvernance indiquant la couverture et les problèmes en suspens.\u003C/span\u003E\u003C/p\u003E\r\n\u003Cp\u003EAtteindre une maturité à l’échelle de l’entreprise prend généralement plus de temps, car la gouvernance exige autant de changements opérationnels que d’outils. Le programme doit s’intégrer à la manière dont les équipes conçoivent les produits de données, approuvent les accès, créent les pipelines, partagent les données et déploient les systèmes d’IA.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-governance-frameworks-and-data-governance":{"id":"title-v2-2f1c06f8f8","additionalClasses":"anchor-title anchor-title--ai-governance-frameworks-and-data-governance","type":"heading2","lines":["Frameworks de gouvernance de l’IA et gouvernance des données"],":type":"snowflake-site/components/title-v2"},"text_ai-governance-frameworks-and-data-governance_0":{"id":"text-9a60ed123a","text":"\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/\"\u003Egouvernance de l’IA\u003C/a\u003E dépend de la gouvernance des données, car le comportement des modèles est façonné par les données utilisées pour entraîner, affiner, récupérer, évaluer et surveiller le système. Un registre de modèles peut suivre les versions et les résultats d’évaluation, mais il ne peut pas déterminer si les données d’entraînement ont été approuvées pour le cas d’usage précis, si les colonnes sensibles ont été exclues, si la traçabilité est complète ou si une variable reflète un biais historique sous-jacent.\u003C/p\u003E\n\u003Cp\u003ELes frameworks traditionnels de gouvernance des données sont actuellement étendus de plusieurs manières pour s’adapter aux workloads d’IA. Les programmes de type DAMA-DMBOK peuvent étendre les pratiques de métadonnées, de qualité et d’intendance pour inclure la provenance des données d’entraînement, les définitions de variables et les jeux de données approuvés. Une gouvernance inspirée de COBIT permet d’aligner les contrôles de l’IA sur les exigences de gestion des risques, d’audit et de responsabilisation. Les frameworks DCAM et CDMC permettent aux équipes d'évaluer si les mécanismes de contrôle et les pratiques de maturité des données cloud sont suffisamment robustes pour soutenir les charges de travail d'IA\u003C/p\u003E\n\u003Cp\u003EDes frameworks spécifiques à l’IA sont également en cours de développement. Le \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003ENIST AI Risk Management Framework\u003C/a\u003E est conçu pour aider les organisations qui gèrent des systèmes d’IA à faire face aux risques et à améliorer la confiance tout au long de la conception, du développement, de l’utilisation et de l’évaluation. Pour les équipes chargées de la gouvernance des données, cela signifie que les contrôles traditionnels doivent être connectés à la gouvernance des modèles, à la traçabilité ML, à la provenance des données d’entraînement, à l’explicabilité, à la surveillance des biais, aux pratiques d’IA responsable et à la responsabilité algorithmique.\u003C/p\u003E\n\u003Cp\u003EUne approche de la gouvernance de l’IA centrée sur les données pose des questions concrètes avant qu’un modèle ne soit mis en production :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EQuels ensembles de données ont permis d’entraîner, d’affiner ou d’ancrer le modèle ?\u003C/li\u003E\n\u003Cli\u003EQui a approuvé ces jeux de données pour ce cas d’usage ?\u003C/li\u003E\n\u003Cli\u003EQuels chemins de lignage relient les données sources aux features, prompts, embeddings ou index de récupération ?\u003C/li\u003E\n\u003Cli\u003EQuels attributs sensibles ou proxies ont été inclus, exclus ou transformés ?\u003C/li\u003E\n\u003Cli\u003EQuelles règles de qualité, contrôles de dérive et processus de surveillance des biais s’appliquent ?\u003C/li\u003E\n\u003Cli\u003EQuels résultats nécessitent une révision humaine, une divulgation ou une journalisation d’audit ?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECes questions rendent la gouvernance de l’IA opérationnelle. Elles montrent également pourquoi un framework de gouvernance des données ne peut pas se limiter à des rapports et des tableaux de bord ; il doit prendre en compte les données à mesure qu’elles alimentent les modèles, les agents, les applications et les décisions automatisées.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_industry-specific-framework-guidance":{"id":"title-v2-33270aebd9","additionalClasses":"anchor-title anchor-title--industry-specific-framework-guidance","type":"heading2","lines":["Orientations sur les frameworks spécifiques à l'industrie"],":type":"snowflake-site/components/title-v2"},"text_industry-specific-framework-guidance_0":{"id":"text-1095706941","text":"\u003Cp\u003ELes différents secteurs d’activité utilisent des frameworks de gouvernance des données pour des raisons diverses. Les composants de base peuvent sembler similaires, mais les priorités en matière de contrôle varient.\u003C/p\u003E\r\n\u003Ch3\u003EServices financiers\u003C/h3\u003E\r\n\u003Cp\u003ELes entreprises de services financiers ont souvent besoin de preuves solides concernant la qualité des données, la traçabilité, la propriété, le reporting des risques et les contrôles réglementaires. Le modèle DCAM est généralement pertinent, car il met l’accent sur la maturité de la gestion des données et l’évaluation des capacités. Les institutions financières peuvent également aligner leurs pratiques de gouvernance sur la norme BCBS 239, qui se concentre sur l’agrégation des données sur les risques et les principes de reporting des risques pour les banques.\u003C/p\u003E\r\n\u003Cp\u003EEn pratique, la gouvernance dans les services financiers se concentre souvent sur les éléments de données stratégiques, la traçabilité du reporting des risques, les seuils de qualité des données, les contrôles d’accès, les exigences de rétention et les preuves d’audit. Le framework doit clairement définir qui possède une métrique de risque, d’où elle provient, comment elle est transformée et si les données sont adaptées au reporting réglementaire.\u003C/p\u003E\r\n\u003Ch3\u003ESanté et sciences de la vie\u003C/h3\u003E\r\n\u003Cp\u003ELes organisations de santé doivent souvent gouverner les informations de santé protégées, les données de remboursements, les données cliniques, les données de recherche et les données opérationnelles dans le cadre d'obligations de confidentialité et de sécurité. La loi \u003Ca rel=\"noopener noreferrer\" href=\"https://www.hhs.gov/hipaa/index.html\" target=\"_blank\"\u003EHIPAA\u003C/a\u003E régit les exigences de confidentialité et de sécurité aux États-Unis, tandis que les principes FAIR sont souvent pertinents pour le partage, l’interopérabilité et la réutilisation des données de recherche. Les principes FAIR sont particulièrement utiles lorsque les données doivent être faciles à trouver et réutilisables par les humains et les machines sans perte de contexte.\u003C/p\u003E\r\n\u003Cp\u003EEn France, le cadre juridique de la santé écarte la loi américaine HIPAA au profit de la certification obligatoire HDS (Hébergement de Données de Santé) de l’ANS. La mise en œuvre d'un framework de gouvernance dans ce secteur doit nativement intégrer ces exigences pour sécuriser le traitement des données de remboursement ou cliniques par des modèles d’IA sans enfreindre le secret médical.\u003C/p\u003E\r\n\u003Cp\u003ELes frameworks de gouvernance de la santé doivent associer les règles de classification, de consentement, d’accès, d’anonymisation, de conservation et de partage des données aux systèmes au sein desquels les données cliniques, opérationnelles et de recherche sont utilisées.\u003C/p\u003E\r\n\u003Ch3\u003EGouvernement et secteur public\u003C/h3\u003E\r\n\u003Cp\u003ELes organismes gouvernementaux et du secteur public ont souvent besoin de modèles de gouvernance favorisant la transparence, la sécurité, la gestion des documents, l’open data et la conformité. \u003Ca rel=\"noopener noreferrer\" href=\"https://www.fedramp.gov/\" target=\"_blank\"\u003EFedRAMP\u003C/a\u003E peut s’avérer pertinent lorsque des services cloud sont utilisés pour traiter des données gouvernementales, tandis que les programmes de données publiques exigent souvent des métadonnées, des normes de publication et des directives de réutilisation.\u003C/p\u003E\r\n\u003Cp\u003EDans ces environnements, le framework doit définir les données éligibles à une diffusion publique, celles nécessitant un accès restreint, les métadonnées devant accompagner les jeux de données publiés, ainsi que les modalités de conservation des éléments de preuve requis pour la conformité, les audits et la redevabilité publique.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_framework-maturity-assessment":{"id":"title-v2-89b212d270","additionalClasses":"anchor-title anchor-title--framework-maturity-assessment","type":"heading2","lines":["Évaluation de la maturité du framework"],":type":"snowflake-site/components/title-v2"},"text_framework-maturity-assessment_0":{"id":"text-b1f1587814","text":"\u003Cp\u003EUn modèle de maturité aide les entreprises à évaluer l’efficacité de leur gouvernance et à cibler leurs prochains investissements. Un modèle simple en cinq étapes permet de rendre les progrès visibles sans transformer l’évaluation de la maturité en une simple formalité administrative.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EÉtape\u003C/th\u003E\r\n\u003Cth\u003EDescription\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EInitiale\u003C/td\u003E\r\n\u003Ctd\u003ELa gouvernance est ad hoc. La propriété, les définitions, les règles d’accès et les contrôles de qualité varient selon les équipes.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EGéré\u003C/td\u003E\r\n\u003Ctd\u003ELes domaines clés ont des propriétaires désignés, des règles de base et quelques processus reproductibles.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EDéfinie\u003C/td\u003E\r\n\u003Ctd\u003ELes règles, les normes, les rôles d’intendance des données et les contrôles techniques sont documentés et appliqués aux domaines prioritaires.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMesurée\u003C/td\u003E\r\n\u003Ctd\u003ELa performance de la gouvernance se mesure à travers des scores de qualité, des métriques d’accès, la résolution des incidents et les preuves de contrôle.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOptimisé\u003C/td\u003E\r\n\u003Ctd\u003ELa gouvernance est intégrée à la conception des produits de données, aux contrôles de la plateforme, aux workflows d’IA et aux processus d’amélioration continue.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EUne auto-évaluation rapide peut aider les équipes à déterminer où elles en sont actuellement :\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003ELes éléments de données critiques ont-ils des responsables désignés et des définitions métier approuvées ?\u003C/li\u003E\r\n\u003Cli\u003ELes tables et colonnes sensibles sont-elles classifiées avec des règles d’accès et de masquage applicables ?\u003C/li\u003E\r\n\u003Cli\u003ELes équipes sont-elles en mesure d’assurer la traçabilité des données, depuis les systèmes sources jusqu’aux reportings, produits de données ou modèles d’IA ?\u003C/li\u003E\r\n\u003Cli\u003ELes problèmes liés à la qualité des données sont-ils identifiés, attribués et corrigés selon un processus défini ?\u003C/li\u003E\r\n\u003Cli\u003EL'entreprise est-elle en mesure de fournir des éléments probants relatifs aux décisions d'accès, aux exceptions et à l'application de la politique ?\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003ELa plupart des organisations présentent au départ des niveaux de maturité inégaux selon les domaines. Le service financier peut être soumis à des contrôles stricts en raison du risque élevé lié au reporting, tandis que les équipes chargées du marketing, des produits ou de l'IA peuvent adopter des pratiques de gestion des données plus dynamiques, avec une intendance des données moins formelle. L'objectif n'est pas d'atteindre un niveau de maturité uniforme partout, mais plutôt un niveau de maturité adapté en fonction de la valeur métier, des risques et de l'utilisation.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bringing-structure-to-governance-decisions":{"id":"title-v2-0203fb4acf","additionalClasses":"anchor-title anchor-title--bringing-structure-to-governance-decisions","type":"heading2","lines":["Structurer les décisions de gouvernance"],":type":"snowflake-site/components/title-v2"},"text_bringing-structure-to-governance-decisions_0":{"id":"text-fdd6aca9c6","text":"\u003Cp\u003ELes frameworks de gouvernance des données sont utiles car ils offrent aux entreprises une structure éprouvée pour des décisions qui, autrement, risqueraient de devenir incohérentes, manuelles ou difficiles à auditer. Un framework permet de déterminer clairement à qui appartient un domaine de données, quelles politiques s'appliquent à une table ou à une colonne, comment l'accès doit être contrôlé, où il convient de retracer la traçabilité des données et quels indicateurs permettent de vérifier si la gouvernance fonctionne réellement.\u003C/p\u003E\n\u003Cp\u003ELe bon framework ne remplace pas pour autant le jugement de l’entreprise. DAMA-DMBOK, COBIT, DCAM, TOGAF, FAIR et CDMC mettent chacun l’accent sur différents aspects de la gouvernance, et de nombreuses entreprises adaptent plusieurs modèles afin de les ajuster à leur secteur d'activité, à leur architecture et à leur niveau de maturité. À mesure que les données migrent vers des produits partagés, des environnements cloud et des workloads d'IA, les frameworks de gouvernance offrent aux équipes un moyen de maintenir les politiques liées à l'usage.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_bringing-structure-to-governance-decisions_0":{"id":"text-df1d3e797e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EÀ RETENIR\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EUn framework de gouvernance des données transforme la gouvernance d’une simple collection de règles en un système opérationnel de responsabilisation. En associant propriété, contrôles, métadonnées, sécurité et preuves d’audit, les frameworks aident les entreprises à appliquer les pratiques de gouvernance de manière plus cohérente sur les plateformes de données, les environnements cloud et les workloads d’IA, tout en améliorant la visibilité, l’évolutivité et la responsabilité opérationnelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}}},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":itemsOrder":["container"],":items":{"container":{"additionalClasses":"sticky-sidebar","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn 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data-anchor=\"core-components-of-a-data-governance-framework\"\u003EComposants clés d’un framework de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"examples-of-data-governance-frameworks\"\u003EExemples de frameworks de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"how-to-evaluate-and-choose-a-data-governance-framework\"\u003EComment évaluer et choisir un framework de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"the-6-phases-of-data-governance-framework-implementation\"\u003ELes 6 phases de mise en œuvre d’un framework de gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"implementation-timeline\"\u003ECalendrier de mise en œuvre\u003C/li\u003E\n\u003Cli data-anchor=\"ai-governance-frameworks-and-data-governance\"\u003EFrameworks de gouvernance de l’IA et gouvernance des données\u003C/li\u003E\n\u003Cli data-anchor=\"industry-specific-framework-guidance\"\u003EOrientations sur les frameworks spécifiques à l'industrie\u003C/li\u003E\n\u003Cli data-anchor=\"framework-maturity-assessment\"\u003EÉvaluation de la maturité du framework\u003C/li\u003E\n\u003Cli data-anchor=\"bringing-structure-to-governance-decisions\"\u003EStructurer les décisions de gouvernance\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-896e62fecd","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes réponses de nos experts Snowflake à vos questions les plus fréquentes sur les frameworks de gouvernance des données.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}}},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["simple_snowflake_acc"],":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-5a0fd49ca8","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Quelle est la différence entre un framework de gouvernance des données et une politique de gouvernance des données ?","richText":"\u003Cp\u003EUn framework organise le modèle opérationnel global de gouvernance. Une politique définit une règle ou une attente spécifique. Par exemple, un framework peut définir la propriété, la classification, la gouvernance des accès et les processus de mesure, tandis qu’une politique peut stipuler que l’accès aux données clients sensibles requiert une approbation préalable.\u003C/p\u003E"},{"title":"Quel est le meilleur framework de gouvernance des données ?","richText":"\u003Cp\u003EIl n’existe pas de framework unique idéal pour toutes les entreprises. 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Cette combinaison doit être pratique et alignée sur les objectifs commerciaux.\u003C/p\u003E"},{"title":"Combien de temps faut-il pour mettre en œuvre un framework de gouvernance des données ?","richText":"\u003Cp\u003ESa mise en œuvre dépend de la complexité des données, des exigences réglementaires, de la maturité de l’entreprise et de la préparation des outils. De nombreuses entreprises parviennent à établir une gouvernance, à inventorier les données stratégiques, à définir des politiques fondamentales et à piloter des contrôles en quelques mois, tandis qu’une maturité à l’échelle de l’entreprise exige généralement un investissement continu et une amélioration constante.\u003C/p\u003E"},{"title":"Comment la gouvernance des données soutient-elle la gouvernance de l’IA ?","richText":"\u003Cp\u003ELa gouvernance des données soutient la gouvernance de l’IA en contrôlant les données utilisées pour entraîner, affiner, récupérer, évaluer et surveiller les systèmes d’IA. Elle aide les équipes à documenter la provenance des données d’entraînement, à assurer la traçabilité des pipelines de machine learning, à classifier les données sensibles, à piloter la qualité des données et à appliquer des contrôles d’IA responsable avant la mise en production des modèles.\u003C/p\u003E"},{"title":"Quelles métriques utiliser pour mesurer la gouvernance des données ?","richText":"\u003Cp\u003ELes principaux indicateurs englobent la couverture du catalogue, le taux de classification, les scores de qualité des données, le taux de réalisation des revues d’accès, la conformité aux règles, le délai de résolution des anomalies, l’ancienneté des dérogations, la couverture de la traçabilité, ainsi que le pourcentage d’éléments de données critiques (CDE) dotés d’un garant attitré et d’une définition 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