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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-v2-550d1ae6f9","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Business Intelligence : des informations fiables à grande échelle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-f6a7bc0b66","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003ELa Business Intelligence va aujourd’hui bien au-delà des tableaux de bord et des rapports. Découvrez comment l’intégration des données, le contexte sémantique, le calcul évolutif et l’IA s’associent pour offrir une analyse cohérente à l’échelle de l’entreprise.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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border-top","layout":"SIMPLE","id":"container-029cd6e694",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-e5c8f3b007","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes liés à la Business Intelligence :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-85c0a03a84","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/okr/\"\u003EOKR\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/tools/\"\u003EOutils de BI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/kpi/\"\u003EKPI\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-6eaaf5cf7f","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","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",":items":{"callout__0":{"id":"text-d918aa36b1","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDÉFINITION DE LA BUSINESS INTELLIGENCE\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ELa Business Intelligence (BI), ou informatique décisionnelle, réunit l'infrastructure de données, les modèles analytiques et les outils logiciels qui permettent de collecter, préparer, interroger et présenter les données d'une entreprise pour éclairer ses décisions.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-a5f30aa795","text":"\u003Cp\u003EUne équipe financière est au beau milieu d’une revue trimestrielle lorsque quelqu’un remarque que le chiffre d’affaires total figurant sur le tableau de bord de la direction ne correspond pas à celui du tableau de bord de l’équipe commerciale. La réunion s’interrompt. Les analystes commencent à vérifier les filtres et les requêtes SQL, tandis que les responsables de département présents dans la salle attendent de savoir à quel chiffre se fier.\u003C/p\u003E\r\n\u003Cp\u003ELes deux tableaux de bord se sont actualisés sans erreur. Tous deux exploitent des données validées. L'écart se situe plus bas dans l'architecture BI : les tableaux de bord appliquent peut-être des définitions différentes du chiffre d'affaires, utilisent des dates de transaction différentes, ou s'appuient sur des jointures qui agrègent les enregistrements sous-jacents de manière distincte.\u003C/p\u003E\r\n\u003Cp\u003EÀ mesure que la Business Intelligence se déploie à travers les outils en libre-service, les applications intégrées, les interfaces en langage naturel et \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/agents/\"\u003El’IA agentique\u003C/a\u003E, ce type d’écart a plus de chances de se propager. La fiabilité des résultats dépend des couches sous-jacentes à l'interface : modèles de données, définitions des indicateurs, relations et infrastructure analytique qui déterminent la manière dont une question est interprétée et traitée.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-business-intelligence":{"id":"title-v2-269b557f8a","additionalClasses":"anchor-title anchor-title--qu-est-ce-que-business-intelligence","type":"heading2","lines":["Qu’est-ce que la Business Intelligence ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-business-intelligence_0":{"id":"text-1f969352d5","text":"\u003Cp\u003ELa Business Intelligence désigne l’ensemble des pratiques et technologies utilisées par les entreprises pour transformer leurs données en mesures, en analyses et en outils d’aide à la décision. La BI englobe les modèles de données et les processus analytiques qui sous-tendent ce travail, ainsi que les logiciels utilisés pour interroger, visualiser et diffuser les résultats.\u003C/p\u003E\r\n\u003Cp\u003EReprenons l'exemple du tableau de bord des revenus évoqué lors de la revue trimestrielle. Les systèmes transactionnels et financiers fournissent les enregistrements sous-jacents. Les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/modernizing-data-pipelines/\"\u003Epipelines de données\u003C/a\u003E rassemblent ces enregistrements et les préparent pour l'analyse, tandis qu'un modèle de données établit les relations entre commandes, clients, produits et périodes. L'indicateur du chiffre d’affaires applique le calcul convenu par l’organisation à cette structure. Un outil de BI interroge ensuite les données et restitue le résultat dans le tableau de bord, où les responsables financiers s'en servent pour évaluer la performance trimestrielle.\u003C/p\u003E\r\n\u003Cp\u003EChaque étape relève de la BI, même si le tableau de bord est généralement la seule partie visible pour les utilisateurs. Tout changement en amont, qu’il s’agisse d’un système source, de la définition d’un indicateur ou d’une relation entre des tables, peut modifier le chiffre présenté à la fin du processus. La BI recouvre donc à la fois l'expérience analytique et les données, la logique et les processus qui la produisent.\u003C/p\u003E\r\n\u003Cp\u003ELa Business Intelligence s'inscrit dans le champ plus large de \u003Ca href=\"https://www.snowflake.com/fr/analytics/\"\u003El'analytique\u003C/a\u003E. Une grande partie de la BI est axée sur la mesure reproductible, le reporting et l’exploration, tandis que la BI moderne intègre également des capacités telles que l’analyse statistique, les prévisions, le \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E et l’analyse assistée par l’IA. Dans la pratique, la frontière est de plus en plus floue : un tableau de bord peut intégrer une prévision ou une alerte d'anomalie, une interface conversationnelle peut répondre à des questions de suivi en langage naturel, et une application opérationnelle peut faire remonter des résultats analytiques directement au sein d'un processus métier. Le fil conducteur reste la prise de décision structurée, fondée sur les données. \u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EDécouvrez comment Pernod Ricard a démocratisé l’accès aux données pour ses équipes métier et techniques, de la BI à la data science :\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"wistia_video":{"id":"wistia-video-f5fb043744","title":{"id":"title","type":"heading1","lines":["Business Intelligence : des informations fiables à grande échelle"],":type":"snowflake-site/components/title"},"wistiaVideoId":"mfkp7p192e","captionDescription":"Anaïs Ghelfi, Data Platform Manager, et Marius Dauphin, Product Owner chez Pernod Ricard, expliquent comment le groupe a démocratisé l’accès aux données pour ses équipes métier et techniques : BI, qualité des données, optimisation du budget marketing et data sharing.","showVideoLength":false,"showRewatchButton":true,":type":"snowflake-site/components/wistia-video"},"title_how-business-intelligence-works":{"id":"title-v2-58d452e214","additionalClasses":"anchor-title anchor-title--fonctionnement-business-intelligence","type":"heading2","lines":["Comment fonctionne la Business Intelligence ?"],":type":"snowflake-site/components/title-v2"},"text_how-business-intelligence-works_0":{"id":"text-d0cabb6e15","text":"\u003Cp\u003EUn résultat de BI se situe au bout d'un parcours de données qui commence dans les systèmes opérationnels et s'achève par une décision. Entre ces deux points, les données doivent être collectées, réconciliées entre les sources, modélisées à des fins d'analyse, interrogées et restituées sous une forme exploitable. Les mises en œuvre varient, mais ce flux de travail suit généralement cinq étapes.\u003C/p\u003E\r\n\u003Ch3\u003E1. Collecter les données\u003C/h3\u003E\r\n\u003Cp\u003ELa BI commence par les données générées à l’échelle de l’organisation et de son écosystème, à savoir les systèmes transactionnels et de point de vente, les plateformes de gestion de la relation client (CRM) et de progiciel de gestion intégré (ERP), les applications financières, les systèmes e-commerce, les plateformes de marketing et de publicité, les sites Web et les applications mobiles, les systèmes d’assistance client, les applications de chaîne d’approvisionnement et de gestion des stocks, les appareils connectés et les données externes ou partenaires pertinentes.\u003C/p\u003E\r\n\u003Cp\u003ELa collecte permet de déterminer quelles informations sont disponibles et dans quelle mesure elles reflètent l’activité actuelle de l’entreprise. Les exigences varient selon la charge de travail. Par exemple, un rapport de ventes de fin de mois et un tableau de bord des opérations actualisé tout au long de la journée nécessitent des cadences d'ingestion, des politiques de rétention et des niveaux de latence distincts.\u003C/p\u003E\r\n\u003Ch3\u003E2. Intégrer les données\u003C/h3\u003E\r\n\u003Cp\u003ELorsque l’analyse porte sur plusieurs systèmes, les enregistrements doivent être réconciliés. L'intégration rassemble les données tout en harmonisant schémas, formats, identifiants et représentations temporelles ; selon les sources, elle peut également impliquer une déduplication, une validation, une résolution d'identité et des règles de traitement des valeurs manquantes ou contradictoires.\u003C/p\u003E\r\n\u003Cp\u003ELa difficulté consiste souvent à aligner la manière dont les différents systèmes représentent l’entreprise. Par exemple, un CRM peut représenter un client sous la forme d’un compte, tandis qu’un système e-commerce enregistre des acheteurs individuels. L'intégration établit les relations entre ces enregistrements, détermine quelle source fait autorité pour un attribut donné et, lorsque l'analyse historique l'exige, définit la manière dont les modifications apportées aux entités métier sont conservées plutôt qu'écrasées.\u003C/p\u003E\r\n\u003Ch3\u003E3. Modéliser les données\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-modeling/\"\u003Emodélisation des données\u003C/a\u003E organise les enregistrements intégrés autour des entités et des relations sur lesquelles portent les analyses. Une table de faits peut enregistrer les commandes à raison d'une ligne par transaction, tandis que les dimensions décrivent des attributs tels que le client, le produit, la zone géographique et la période.\u003C/p\u003E\r\n\u003Cp\u003ELa granularité est ici fondamentale : si une table contient une ligne par commande et une autre une ligne par article de commande, les joindre et les agréger sans tenir compte de cette différence faussera le calcul. Les mesures doivent également avoir un comportement d'agrégation défini. Par exemple, une moyenne ou un ratio exige souvent un traitement plus attentif lorsque les utilisateurs l'analysent à différents niveaux de détail.\u003C/p\u003E\r\n\u003Cp\u003EUn modèle de données physique établit cette structure analytique. Les modèles sémantiques ajoutent des concepts orientés métier (indicateurs nommés, dimensions, relations et autres éléments de contexte) que les systèmes en aval exploitent pour interpréter les données.\u003C/p\u003E\r\n\u003Ch3\u003E4. Analyser les données\u003C/h3\u003E\r\n\u003Cp\u003EUne fois le modèle analytique en place, les utilisateurs l'interrogent via des rapports récurrents, des tableaux de bord, des requêtes SQL, des notebooks et des outils de BI exploratoire.\u003C/p\u003E\r\n\u003Cp\u003ELa méthode analytique découle de la question posée. L'analyse descriptive résume ce qui s'est passé. L'analyse diagnostique examine les relations et les facteurs contributifs. La prévision et les autres méthodes prédictives estiment ce qui est susceptible de se produire ensuite. Les flux de travail BI peuvent également intégrer de l'analyse statistique, du machine learning et de l'IA, par exemple pour détecter des variations anormales du volume de transactions, prévoir la demande de stocks ou générer une explication en langage naturel d'une évolution de la performance.\u003C/p\u003E\r\n\u003Cp\u003ECertaines analyses sont hautement standardisées. Une équipe financière peut examiner chaque mois les mêmes mesures (chiffre d'affaires, marge, charges) à l'aide de rapports certifiés dotés d'une logique de calcul figée. D'autres analyses sont exploratoires : un analyste remarque une baisse inattendue du chiffre d'affaires, segmente le résultat par région, remonte la piste jusqu'à une catégorie de produits précise, puis examine l'activité des clients ou des équipes commerciales à la recherche d'explications. Les environnements de BI modernes prennent de plus en plus en charge les deux types d'analyse, ce qui permet aux utilisateurs d'alterner entre mesure récurrente et exploration sans avoir à reconstruire la structure analytique sous-jacente à chaque nouvelle question.\u003C/p\u003E\r\n\u003Ch3\u003E5. Fournir des informations et agir\u003C/h3\u003E\r\n\u003Cp\u003ELes résultats parviennent aux utilisateurs par le biais de tableaux de bord, de rapports, d’alertes, d’analytique intégrée et d’interfaces conversationnelles. À ce stade, la BI se reconnecte au processus métier qui a déclenché l’analyse.\u003C/p\u003E\r\n\u003Cp\u003ELa restitution comprend également les contrôles qui encadrent le résultat. Les équipes doivent déterminer qui doit y avoir accès, quels rapports font autorité, comment les utilisateurs découvrent les analyses pertinentes et si les informations sont suffisamment récentes pour l’usage prévu. Les canaux de diffusion varient en conséquence : tableaux de bord et rapports programmés conviennent bien au suivi récurrent, les alertes attirent l'attention sur des conditions franchissant un seuil, et les interfaces conversationnelles permettent de poser des questions de suivi lorsque les utilisateurs ont besoin d'approfondir.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-business-intelligence-works_0":{"id":"card-v2-531044abae","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/customers/all-customers/case-study/accor/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire l’étude de cas complète"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["TÉMOIGNAGE CLIENT : Accor"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b9f6480b-c9ce-46d1-a047-5f2ff0c49f75/accor%25403x.png?preferwebp=true&quality=85","alt":"accor logo","lazyEnabled":true,"isLcpImage":false,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELe groupe hôtelier Accor s’appuie sur Snowflake, son socle data depuis 2019, pour donner à ses équipes marketing un accès direct aux données clients. Avec une Customer Data Platform construite sur Snowflake et Hightouch et lancée en deux mois, les équipes isolent désormais un segment complexe en quelques minutes, contre trois semaines auparavant. L’extraction des données de segmentation est 85 % plus rapide, et plus de 200 collaborateurs ont été formés à l’outil.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_the-data-and-semantic-foundation-beneath-bi":{"id":"title-v2-0dafa268d0","additionalClasses":"anchor-title anchor-title--donnees-couche-semantique-bi","type":"heading2","lines":["Données et couche sémantique : le socle de la BI"],":type":"snowflake-site/components/title-v2"},"text_the-data-and-semantic-foundation-beneath-bi_0":{"id":"text-bee8d29a1b","text":"\u003Cp\u003ELes requêtes BI s'exécutent in fine sur des tables, des colonnes et des relations, tandis que les utilisateurs métier formulent leurs questions en termes métier : chiffre d'affaires, fidélisation client, performance produit, conversion. Une traduction fiable nécessite à la fois un modèle de données analytique solide et une représentation explicite de la signification métier.\u003C/p\u003E\r\n\u003Cp\u003ELe modèle de données établit la structure de l’analyse : ce qu’une ligne représente, comment les tables sont liées et comment les enregistrements doivent se combiner. La granularité et la cardinalité sont particulièrement importantes, car une jointure valide peut malgré tout fausser une agrégation lorsque les tables représentent des niveaux de détail différents.\u003C/p\u003E\r\n\u003Cp\u003EDans le cas d'un piège en éventail (fan trap), par exemple, une jointure duplique des valeurs avant l'agrégation. Will Pugh, Principal Software Engineer chez Snowflake, illustre cet effet dans \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/why-we-need-semantic-views/\"\u003Eun exemple\u003C/a\u003E : la jointure d’une vente de 100 € à deux enregistrements de produits associés amène une requête SQL valide à signaler 200 € de ventes, car la vente apparaît deux fois avant d’être additionnée.\u003C/p\u003E\r\n\u003Ch3\u003EReprésenter explicitement la signification métier\u003C/h3\u003E\r\n\u003Cp\u003EUne couche sémantique ajoute la structure orientée métier que les tables et les relations seules ne fournissent pas. Les mesures, les dimensions, les règles de calcul, la terminologie et les relations analytiques valides fournissent aux systèmes en aval un modèle explicite pour interpréter les données.\u003C/p\u003E\r\n\u003Cp\u003E« Un schéma physique de base de données décrit la manière dont les données sont structurées, mais il ne rend pas compte du fonctionnement de l'entreprise », explique Josh Klahr, Head of Product Management for Analytics chez Snowflake. « Les règles telles que la comptabilisation des revenus, les ajustements saisonniers ou les cas particuliers hérités doivent être fournies explicitement. »\u003C/p\u003E\r\n\u003Cp\u003EUne mesure de marge brute, par exemple, peut identifier les calculs de revenus et de coûts approuvés, spécifier leur comportement d’agrégation et établir les dimensions sur lesquelles l'indicateur est valide. Un outil de BI, un notebook ou une interface en langage naturel qui interroge ce modèle s'appuie sur ces définitions, au lieu de devoir les reconstituer à partir du schéma physique.\u003C/p\u003E\r\n\u003Ch3\u003ERendre les définitions sémantiques portables entre les outils\u003C/h3\u003E\r\n\u003Cp\u003ECependant, un modèle sémantique partagé ne résout qu’une partie du problème si ses définitions sont liées à un seul outil. Historiquement, les métadonnées sémantiques ont souvent été encodées dans des modèles propres à chaque éditeur, obligeant les équipes à recréer mesures et relations chaque fois qu'un nouvel outil de BI ou une nouvelle interface faisait son entrée dans l'environnement.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/blog/open-semantic-interchanges-specs-finalized/\"\u003EOpen Semantic Interchange\u003C/a\u003E (OSI) résout ce problème de portabilité. L’initiative a publié la première version de sa spécification ouverte en janvier 2026, créant ainsi une norme pour l’échange de métadonnées sémantiques entre les plateformes et les outils participants.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-data-and-semantic-foundation-beneath-bi_0":{"id":"text-816795684b","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ENe supposez pas qu’une requête est correcte parce qu’elle s’exécute avec succès. Les différences de granularité, de relations de jointure ou de logique d’agrégation peuvent produire des résultats d’apparence plausible qui ne reflètent pas le calcul métier prévu.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_bi-tools-and-how-they-work-together":{"id":"title-v2-a08738d9df","additionalClasses":"anchor-title anchor-title--types-outils-bi","type":"heading2","lines":["Quels sont les différents types d’outils de BI ?"],":type":"snowflake-site/components/title-v2"},"text_bi-tools-and-how-they-work-together_0":{"id":"text-e57a0e915c","text":"\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/tools/\"\u003Eoutils de BI\u003C/a\u003E diffèrent par la manière dont les utilisateurs créent des analyses, explorent les données, collaborent et présentent les résultats. Leur connexion à la plateforme de données varie également, et ces choix architecturaux affectent la fraîcheur, la latence, les coûts et la gouvernance.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ERequêtâge direct :\u003C/b\u003E avec une BI en requêtage direct (live query), l'outil envoie des requêtes au moteur analytique sous-jacent chaque fois que les utilisateurs ouvrent un tableau de bord, modifient un filtre ou explorent les données. Les utilisateurs travaillent avec des données actuelles selon la cadence d’actualisation de la source, tandis que la plateforme absorbe la simultanéité qui en résulte.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEn mémoire :\u003C/b\u003E un extrait ou un modèle en mémoire crée une représentation analytique distincte que l’outil de BI actualise périodiquement. Cette approche réduit les sollicitations répétées sur la source et améliore parfois la performance interactive, mais la fraîcheur des données dépend alors de la fréquence d'extraction, et une copie supplémentaire doit être gérée.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELibre-service :\u003C/b\u003E la BI en libre-service donne aux utilisateurs métier davantage de contrôle sur l'exploration et la construction des requêtes. Une approche en libre-service bien conçue limite les connaissances techniques requises : les utilisateurs sélectionnent des mesures et des dimensions compréhensibles, tandis que les relations modélisées en arrière-plan encadrent la manière dont ces champs peuvent se combiner.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIntégré :\u003C/b\u003E avec la BI embarquée, ou analytique embarquée, les rapports et les analyses interactives s'affichent directement au sein d'une autre application ou d'un autre flux de travail. Dans ce cas, les profils d'utilisation s'apparentent souvent davantage au trafic applicatif qu'au reporting interne, en particulier lorsque des logiciels destinés aux clients sont utilisés simultanément par de nombreux utilisateurs.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EInterfaces de BI basées sur l’IA\u003C/b\u003E : les interfaces en langage naturel permettent aux utilisateurs de poser des questions avec leurs propres mots, de générer des analyses et d'approfondir les résultats au fil d'un dialogue. Elles n'en reposent pas moins sur les mêmes données sous-jacentes, la même infrastructure de requêtage et le même contexte analytique que les autres expériences de BI.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELa plupart des environnements d’entreprise mélangent ces capacités. Cependant, leur évaluation nécessite plus qu’une comparaison des fonctionnalités entre les produits de BI. Les équipes doivent également comprendre le comportement des requêtes, la fraîcheur des données, la mise en cache, l'intégration avec les modèles existants et la charge de travail que chaque interface transmet aux systèmes en aval.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_scaling-bi-and-managing-dashboard-sprawl":{"id":"title-v2-e6d2b5fccb","additionalClasses":"anchor-title anchor-title--passer-bi-echelle","type":"heading2","lines":["Passer la BI à l'échelle et maîtriser la prolifération des tableaux de bord"],":type":"snowflake-site/components/title-v2"},"text_scaling-bi-and-managing-dashboard-sprawl_0":{"id":"text-38a4bddff8","text":"\u003Cp\u003ELa demande en matière de BI est rarement uniforme. Le lundi à 9 h, des centaines de personnes peuvent ouvrir des tableaux de bord récurrents tandis que les analystes lancent des requêtes ad hoc et qu’une application intégrée continue de répondre aux demandes des clients.\u003C/p\u003E\n\u003Cp\u003ECes charges de travail imposent des exigences différentes en matière de calcul. Les actualisations planifiées sont prévisibles et souvent gourmandes en ressources. Les filtres interactifs favorisent une faible latence. La complexité du SQL exploratoire varie considérablement. L'isolation des charges de travail, la mise à l'échelle en fonction de la concurrence, la mise en cache et la planification des actualisations aident les équipes à concilier performance interactive et coûts de calcul au gré de l'évolution de la demande.\u003C/p\u003E\n\u003Cp\u003ELe parc de tableaux de bord engendre lui aussi un coût. Les rapports ont tendance à s'accumuler : les équipes dupliquent les ressources existantes pour de nouveaux publics ou de nouveaux besoins, mais négligent de retirer les anciennes, devenues inutilisées. À terme, les utilisateurs se retrouvent face à plusieurs rapports similaires, tandis que les équipes plateforme continuent de maintenir ou d'actualiser des ressources peu consultées.\u003C/p\u003E\n\u003Cp\u003ELa télémétrie d'utilisation permet de repérer les tableaux de bord peu consultés, de vérifier s'ils servent encore un processus métier, d'en attribuer la responsabilité et de retirer ou d'archiver les versions obsolètes. La certification permet en outre de distinguer les rapports de référence des travaux exploratoires.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_defining-and-governing-kpis":{"id":"title-v2-cbc1967630","additionalClasses":"anchor-title anchor-title--definir-gouverner-kpi","type":"heading2","lines":["Définir et gouverner les KPI"],":type":"snowflake-site/components/title-v2"},"text_defining-and-governing-kpis_0":{"id":"text-d78cfe5eb6","text":"\u003Cp\u003EUn \u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/kpi/\"\u003Eindicateur clé de performance\u003C/a\u003E (KPI) transforme un objectif métier en une valeur mesurable. Pour que quiconque puisse reproduire cette valeur, la définition nécessite plus qu’un nom et une formule.\u003C/p\u003E\r\n\u003Cp\u003EUn KPI utilisé en production spécifie généralement :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003ESignification métier et utilisation prévue\u003C/li\u003E\r\n\u003Cli\u003ELogique de calcul et d’agrégation\u003C/li\u003E\r\n\u003Cli\u003EGranularité\u003C/li\u003E\r\n\u003Cli\u003EFenêtre de temps ou comportement temporel\u003C/li\u003E\r\n\u003Cli\u003EDimensions et filtres applicables\u003C/li\u003E\r\n\u003Cli\u003ESource faisant autorité\u003C/li\u003E\r\n\u003Cli\u003EResponsables métier et techniques\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EPrenons la fidélisation des clients. La définition doit préciser la population de départ, les critères permettant de considérer un client comme fidélisé et la période sur laquelle la fidélisation est mesurée. Ces éléments doivent rester identiques lorsque l'indicateur est réutilisé ailleurs.\u003C/p\u003E\r\n\u003Cp\u003ELes modifications doivent être suivies et gérées. Si, par exemple, la direction financière modifie le traitement des remboursements dans le chiffre d'affaires comptabilisé, les équipes doivent déterminer à quelle date la modification prend effet, si les résultats historiques doivent être recalculés et quels rapports en aval reposent sur l'ancienne définition. Le contrôle des versions, la \u003Ca href=\"https://www.snowflake.com/fr/data-governance/data-lineage/\"\u003Etraçabilité des données\u003C/a\u003E et un processus d’approbation rendent ces modifications traçables.\u003C/p\u003E\r\n\u003Cp\u003EEn règle générale, la définition d'un KPI important relève de la responsabilité des parties prenantes métier, tandis que les équipes data et analytics mettent en œuvre et maintiennent la logique validée. En formalisant clairement ces responsabilités, la gouvernance des métriques résiste mieux aux changements de personnes, d'outils et de structure organisationnelle.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_defining-and-governing-kpis_0":{"id":"text-0d0fb77c74","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDocumentez plus que la formule d’un KPI. Documentez sa granularité, ses filtres, sa logique temporelle, sa source de référence et son responsable, afin qu'une autre équipe puisse reproduire le même résultat et comprendre ce qui change lorsque la définition est mise à jour.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_bi-strategy-and-benefits":{"id":"title-v2-890dd28de3","additionalClasses":"anchor-title anchor-title--strategie-avantages-bi","type":"heading2","lines":["Stratégie BI et avantages de la Business Intelligence"],":type":"snowflake-site/components/title-v2"},"text_bi-strategy-and-benefits_0":{"id":"text-26730eac78","text":"\u003Cp\u003EUne stratégie BI définit la manière dont une organisation utilisera les données pour éclairer ses décisions récurrentes, les capacités analytiques que ces décisions exigent et les responsables des ressources sur lesquelles elles reposent. Le choix des outils s'inscrit dans cette stratégie, mais seulement une fois que les équipes ont cerné les questions, les utilisateurs et les besoins en charges de travail auxquels l'environnement BI doit répondre.\u003C/p\u003E\r\n\u003Ch3\u003E\u003Cb\u003EPartir\u003C/b\u003E des décisions et des exigences\u003C/h3\u003E\r\n\u003Cp\u003EUne bonne stratégie de BI part des décisions que les gens doivent prendre. Quelles questions reviennent assez souvent pour être standardisées ? Qui a besoin de la réponse, et à quelle vitesse ? Quelles mesures nécessitent une définition commune à l'ensemble de l'organisation, et lesquelles peuvent rester propres à une équipe ou à un flux de travail ?\u003C/p\u003E\r\n\u003Cp\u003ECes décisions façonnent la conception technique. Le reporting de direction peut privilégier la cohérence historique et les indicateurs certifiés, tandis que la BI opérationnelle nécessite des données plus fraîches et des requêtes à plus faible latence. Les utilisateurs du libre-service ont besoin d'une marge de manœuvre suffisante pour explorer les données sans contourner la gouvernance, tandis que l'analytique embarquée impose des exigences de performance et de contrôle d'accès à l'échelle applicative.\u003C/p\u003E\r\n\u003Cp\u003ESur cette base, les équipes peuvent définir la manière dont les données sont intégrées et modélisées, l'endroit où la logique métier est maintenue, les charges de travail qui reposent sur le requêtage direct ou sur des extraits, les modalités de gouvernance des accès, ainsi que la façon dont la performance et les coûts seront maîtrisés à mesure que l'adoption progresse.\u003C/p\u003E\r\n\u003Ch3\u003EMesurer \u003Cb\u003El'efficacité de la BI\u003C/b\u003E\u003C/h3\u003E\r\n\u003Cp\u003ELes équipes doivent savoir si la BI améliore la prise de décision. « Si le succès est mesuré par les vues de tableaux de bord, le volume de requêtes ou les utilisateurs actifs de la plateforme, vous mesurez l’activité plutôt que la valeur », explique Josh Klahr.\u003C/p\u003E\r\n\u003Cp\u003EParmi les indicateurs pertinents figurent le taux d'adoption auprès des utilisateurs visés, le temps nécessaire pour répondre aux questions récurrentes, la performance des requêtes, le nombre de doublons parmi les tableaux de bord ou les modèles analytiques, ainsi que l'effort consacré par les équipes à réconcilier des résultats contradictoires. Pour les initiatives de libre-service, les équipes peuvent également mesurer la fréquence à laquelle les utilisateurs trouvent les réponses par eux-mêmes plutôt que de se tourner vers les analystes pour des demandes courantes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_bi-strategy-and-benefits_0":{"id":"quote-item-a194e1bc61","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"Si le succès est mesuré par les vues de tableaux de bord, le volume de requêtes ou les utilisateurs actifs de la plateforme, vous mesurez l’activité plutôt que la valeur.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_bi-strategy-and-benefits_1":{"id":"text-2e31bfb287","text":"\u003Cp\u003ELa confiance a un impact direct sur l’adoption. Dans \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://technologyleadersclub.com/wp-content/uploads/2024/10/report_precisely-lebow-report-2024.pdf\"\u003Eune étude de Precisely et du LeBow College of Business de l’Université Drexel\u003C/a\u003E, 76 % des organisations ont identifié la prise de décision basée sur les données comme un objectif principal de leurs programmes de données, tandis que 67 % ont déclaré ne pas faire totalement confiance à leurs données. Cet écart permet d’expliquer pourquoi la stratégie de BI englobe de plus en plus la qualité, la gouvernance et la propriété des données.\u003C/p\u003E\r\n\u003Ch3\u003ELes avantages de la Business Intelligence\u003C/h3\u003E\r\n\u003Cp\u003ELes avantages de la BI découlent de la stratégie. Les modèles analytiques réutilisables réduisent le travail nécessaire pour implémenter la même logique à plusieurs reprises. Les indicateurs partagés raccourcissent les cycles de réconciliation. L’accès en libre-service offre à certains utilisateurs un chemin plus rapide vers des réponses de routine, tandis que les rapports certifiés fournissent une référence stable pour les décisions récurrentes.\u003C/p\u003E\r\n\u003Cp\u003EAu niveau de la plateforme, le calcul évolutif et la gestion des charges de travail aident les environnements de BI à absorber une utilisation plus large sans obliger les équipes à provisionner chaque charge de travail en fonction de la demande de pointe. Une meilleure gestion du cycle de vie limite aussi les coûts et la confusion qu'engendrent les rapports obsolètes, les tableaux de bord en double et les ressources analytiques qui ne servent plus aucun processus métier actif.\u003C/p\u003E\r\n\u003Cp\u003ELes stratégies BI les plus efficaces relient les améliorations à des décisions et à des flux de travail précis, au lieu de considérer la seule adoption des tableaux de bord comme une preuve de valeur.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_modern-bi-augmented-analytics-conversational-bi-and-agentic-bi":{"id":"title-v2-1549a83d19","additionalClasses":"anchor-title anchor-title--bi-moderne-ia","type":"heading2","lines":["La BI moderne : analytique augmentée, BI conversationnelle et BI agentique"],":type":"snowflake-site/components/title-v2"},"text_modern-bi-augmented-analytics-conversational-bi-and-agentic-bi_0":{"id":"text-5b5b00f929","text":"\u003Cp\u003EAvec l'IA, une partie de la BI ne repose plus sur la construction explicite de requêtes, mais sur l'interprétation. Le degré d’interprétation varie selon plusieurs approches :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analytique augmentée\u003C/b\u003E applique l'IA et le machine learning au sein d'un flux de travail analytique, qu'il s'agisse de détection d'anomalies, d'aide à la prévision, de suggestions de visualisations ou de restitution narrative des informations clés.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELa BI conversationnelle\u003C/b\u003E accepte les questions en langage naturel, les traduit en opérations analytiques et prend en charge les questions de suivi à mesure que les utilisateurs examinent un résultat.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELa BI agentique\u003C/b\u003E étend l’analytique métier basée sur l’IA à travers plusieurs étapes, telles que la localisation de données pertinentes, l’exécution d’analyses, l’investigation d’une anomalie et l’assemblage des résultats.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EInterpréter la question métier\u003C/h3\u003E\r\n\u003Cp\u003EAvant qu’un système ne génère du SQL, il doit déterminer ce que l’utilisateur demande. Supposons que quelqu’un demande : « Lesquels de nos meilleurs clients dépensent moins ce trimestre ? » Le système doit identifier l'entité client appropriée, déterminer ce que signifie « meilleur », sélectionner l'indicateur de dépenses validé, établir le trimestre concerné, repérer les relations valides entre les données nécessaires et choisir la manière de comparer l'activité actuelle à l'activité précédente. Un SQL syntaxiquement correct ne traite que la traduction finale de ces choix en une requête.\u003C/p\u003E\r\n\u003Cp\u003ELes systèmes de production traitent cette ambiguïté en fournissant plus de contexte sur les données et l’entreprise. Un modèle sémantique fournit au système des mesures, des dimensions et des relations validées, au lieu de le laisser les déduire des seuls noms de tables et de colonnes. Les descriptions métier et les synonymes établissent le lien entre les termes réellement employés par les utilisateurs, comme prises de commandes, compte actif ou client grand compte, et les objets correspondants du modèle. Les valeurs d'exemple, quant à elles, aident à comprendre sous quelle forme les catégories et les entités apparaissent dans les données sous-jacentes.\u003C/p\u003E\r\n\u003Ch3\u003E\u003Cb\u003EAncrer\u003C/b\u003E et évaluer l’analyse générée par l’IA\u003C/h3\u003E\r\n\u003Cp\u003EPour les questions récurrentes ou particulièrement importantes, les requêtes vérifiées associent une demande en langage naturel à un SQL approuvé et montrent au système un modèle analytique reconnu comme valide. Des instructions personnalisées permettent d'ajouter des règles difficiles à exprimer par le seul schéma, par exemple le champ de date à utiliser pour une analyse donnée ou la façon dont l'organisation définit une période de reporting.\u003C/p\u003E\r\n\u003Cp\u003ECes dispositifs réduisent l'éventail des interprétations possibles pour le système, mais ils ne dispensent pas de l'évaluer. Les équipes doivent tester des questions métier représentatives en comparant les réponses obtenues à des résultats de référence, y compris pour des formulations ambiguës et des demandes qui combinent plusieurs indicateurs, filtres ou relations. Lorsqu'une erreur survient, les équipes doivent pouvoir déterminer à quel moment l'interprétation a dévié (choix de l'indicateur, résolution des entités, chemin de jointure, filtre, logique temporelle ou autre choix analytique), afin d'améliorer le contexte ou le modèle concerné.\u003C/p\u003E\r\n\u003Cp\u003EDans le cas des flux de travail agentiques, l'évaluation porte également sur les étapes intermédiaires, car un mauvais choix effectué tôt dans une analyse en plusieurs étapes peut se répercuter sur toute la suite.\u003C/p\u003E\r\n\u003Ch3\u003EChoisir l'interface adaptée à chaque tâche analytique\u003C/h3\u003E\r\n\u003Cp\u003ELes interfaces pilotées par l'IA élargissent l'éventail des questions que les utilisateurs peuvent explorer sans qu'un rapport prédéfini soit nécessaire. Les tableaux de bord restent bien adaptés aux mesures récurrentes que les équipes consultent dans un format constant, tandis que la BI conversationnelle permet de poser des questions et d'approfondir l'analyse en dehors de ces parcours prédéfinis.\u003C/p\u003E\r\n\u003Cp\u003ELes organisations ont de plus en plus besoin de ces deux expériences. Une équipe de direction peut suivre chaque semaine un tableau de bord certifié du chiffre d'affaires, puis recourir à une interface conversationnelle pour comprendre une baisse inattendue dans une région. Le mode d'interaction change d'une tâche à l'autre, mais les données gouvernées et le contexte analytique sur lesquels elles reposent assurent la cohérence et la continuité.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bi-roles-and-teams":{"id":"title-v2-6673c87277","additionalClasses":"anchor-title anchor-title--roles-equipes-bi","type":"heading2","lines":["Rôles et équipes de BI"],":type":"snowflake-site/components/title-v2"},"text_bi-roles-and-teams_0":{"id":"text-1ba3023b73","text":"\u003Cp\u003ELes responsabilités liées à la BI couvrent généralement plusieurs disciplines, même si les intitulés de poste varient considérablement.\u003C/p\u003E\r\n\u003Cp\u003EUn analyste BI travaille au plus près des questions métier, utilisant les données pour examiner les performances et créer des analyses récurrentes ou exploratoires. Un data engineer ou un analytics engineer prépare les structures qui sous-tendent ce travail, notamment les transformations, les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/a-practical-guide-to-data-quality/\"\u003Econtrôles de qualité des données\u003C/a\u003E et les modèles analytiques. Un développeur BI se consacre aux modèles sémantiques, aux tableaux de bord, aux rapports et aux expériences analytiques embarquées.\u003C/p\u003E\r\n\u003Cp\u003ELes parties prenantes métier conservent un rôle essentiel en tant que garantes du sens métier dans leur domaine. Un responsable financier, par exemple, détermine la manière dont un KPI financier doit être interprété ; l'équipe data traduit ensuite cette définition validée en une mise en œuvre analytique.\u003C/p\u003E\r\n\u003Cp\u003ELes grandes organisations répartissent souvent ces responsabilités entre équipes plateforme et équipes métier, tandis que les plus petites en regroupent plusieurs au sein d'un même poste. Quelle que soit l'organisation retenue, attribuer clairement la responsabilité des pipelines, des modèles, des indicateurs et des rapports de production permet d'éviter que des ressources analytiques ne survivent aux personnes qui les comprennent.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bi-by-industry-and-business-function":{"id":"title-v2-9870b39146","additionalClasses":"anchor-title anchor-title--bi-par-secteur","type":"heading2","lines":["La BI par secteur d’activité et par fonction"],":type":"snowflake-site/components/title-v2"},"text_bi-by-industry-and-business-function_0":{"id":"text-9e228c7d1c","text":"\u003Cp\u003EL’architecture de base de la BI s’applique à tous les secteurs et fonctions, tandis que les exigences changent avec la décision.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EDomaine\u003C/th\u003E\r\n\u003Cth\u003EExemple de question de BI\u003C/th\u003E\r\n\u003Cth\u003ECondition essentielle\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFinance\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EComment évoluent les revenus, la marge ou les dépenses ?\u003C/td\u003E\r\n\u003Ctd\u003ERéconciliation et cohérence du reporting historique\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMarketing\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EQuelles activités contribuent à l’acquisition, au pipeline ou aux revenus ?\u003C/td\u003E\r\n\u003Ctd\u003ERègles d'identification et d'attribution entre les sources\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EVentes\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EComment le pipeline ou la conversion ont-ils évolué au fil du temps ?\u003C/td\u003E\r\n\u003Ctd\u003EHistorisation des états successifs du CRM\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EOpérations\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EOù les stocks, la préparation des commandes ou les niveaux de service se dégradent-ils ?\u003C/td\u003E\r\n\u003Ctd\u003EFraîcheur adaptée et niveau de détail opérationnel\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERetail et grande distribution\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EComment la performance évolue-t-elle selon les canaux, les produits et les sites ?\u003C/td\u003E\r\n\u003Ctd\u003EIntégration des données transactionnelles numériques et physiques\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESecteurs réglementés\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EComment ce résultat a-t-il été obtenu et qui a eu accès aux données ?\u003C/td\u003E\r\n\u003Ctd\u003ETraçabilité, auditabilité, contrôles d'accès et application des règles\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EUne seule question métier franchit souvent ces limites. Analyser une baisse du chiffre d'affaires, par exemple, peut nécessiter des indicateurs financiers, l'historique du pipeline commercial et des données d'acquisition marketing. Pour obtenir une réponse fiable, les équipes ont besoin d'une structure commune suffisante pour relier ces analyses, tout en préservant les règles propres à chaque domaine.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_business-intelligence-on-snowflake":{"id":"title-v2-41b3192ca8","additionalClasses":"anchor-title anchor-title--business-intelligence-snowflake","type":"heading2","lines":["La Business Intelligence sur Snowflake"],":type":"snowflake-site/components/title-v2"},"text_business-intelligence-on-snowflake_0":{"id":"text-cff3e2e2b9","text":"\u003Cp\u003ESnowflake rassemble les données, le contexte sémantique, la gouvernance et le calcul qui prennent en charge la BI dans une base partagée, tout en permettant aux organisations d’utiliser les outils et interfaces de BI qui correspondent à leurs flux de travail.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon-context/\"\u003ESnowflake Horizon Context\u003C/a\u003E fournit un contexte métier gouverné pour la BI et l’IA. Les Semantic Views représentent indicateurs, dimensions, relations et logique métier sous forme d'objets Snowflake, tandis que Semantic View Autopilot s'appuie sur les requêtes SQL existantes et les modèles BI pris en charge pour aider à générer ces vues à partir de schémas analytiques établis. Open Semantic Interchange (OSI) contribue à propager les métadonnées sémantiques entre les outils participants, afin que les organisations puissent réutiliser des définitions gouvernées dans l'ensemble de leurs environnements BI.\u003C/p\u003E\n\u003Cp\u003EAu niveau de la couche de restitution, l'architecture de calcul élastique de Snowflake prend en charge les charges de travail BI aux exigences variées en matière de concurrence et de performance. Des entrepôts virtuels distincts offrent une isolation des charges de travail pour le reporting récurrent, l’analyse ad hoc et d’autres charges de travail, tandis que \u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E fournit les contrôles d'accès, la découverte, la traçabilité et la qualité des données sur l'ensemble des données sous-jacentes.\u003C/p\u003E\n\u003Cp\u003EPour la BI optimisée par l’IA, \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E traduit les questions en langage naturel en SQL à l’aide des définitions métier et des relations représentées dans les vues sémantiques. Ces vues sont également accessibles via le SQL standard et le SQL sémantique, offrant aux outils de BI, aux analystes et aux systèmes d’IA de multiples façons de travailler avec les mêmes données modélisées.\u003C/p\u003E\n\u003Cp\u003EEnsemble, ces capacités placent les données gouvernées, la logique analytique, le contexte sémantique et le traitement évolutif des requêtes sous l’interface par laquelle un utilisateur pose une question.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-bi-for-changing-analytical-experiences":{"id":"title-v2-6d9cd08d26","additionalClasses":"anchor-title anchor-title--nouveaux-usages-bi","type":"heading2","lines":["Préparer la BI aux nouveaux usages analytiques"],":type":"snowflake-site/components/title-v2"},"text_build-bi-for-changing-analytical-experiences_0":{"id":"text-d5519c5317","text":"\u003Cp\u003ELa façon dont les gens interagissent avec les données métier continuera de changer. Les tableaux de bord restent utiles, tandis que les outils en libre-service, l’analytique embarquée, les interfaces conversationnelles et les agents offrent aux utilisateurs des moyens supplémentaires d’explorer les mêmes informations sous-jacentes. Ces expériences nécessitent un socle stable sur lequel s'appuyer : des données gouvernées, des relations analytiques explicites, des définitions métier partagées et une infrastructure capable de garantir la fraîcheur, la concurrence et la performance requises.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-bi-for-changing-analytical-experiences_0":{"id":"text-556c23de28","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EÀ RETENIR\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EÀ mesure que la BI se déploie à travers tableaux de bord, outils en libre-service, applications embarquées et interfaces d'IA, la cohérence repose de plus en plus sur les fondations sous-jacentes : des données gouvernées, un contexte analytique partagé et une infrastructure adaptée aux charges de travail.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-business-intelligence","text_what-is-business-intelligence_0","wistia_video","title_how-business-intelligence-works","text_how-business-intelligence-works_0","card_v2_how-business-intelligence-works_0","title_the-data-and-semantic-foundation-beneath-bi","text_the-data-and-semantic-foundation-beneath-bi_0","callout_the-data-and-semantic-foundation-beneath-bi_0","title_bi-tools-and-how-they-work-together","text_bi-tools-and-how-they-work-together_0","title_scaling-bi-and-managing-dashboard-sprawl","text_scaling-bi-and-managing-dashboard-sprawl_0","title_defining-and-governing-kpis","text_defining-and-governing-kpis_0","callout_defining-and-governing-kpis_0","title_bi-strategy-and-benefits","text_bi-strategy-and-benefits_0","quote_bi-strategy-and-benefits_0","text_bi-strategy-and-benefits_1","title_modern-bi-augmented-analytics-conversational-bi-and-agentic-bi","text_modern-bi-augmented-analytics-conversational-bi-and-agentic-bi_0","title_bi-roles-and-teams","text_bi-roles-and-teams_0","title_bi-by-industry-and-business-function","text_bi-by-industry-and-business-function_0","title_business-intelligence-on-snowflake","text_business-intelligence-on-snowflake_0","title_build-bi-for-changing-analytical-experiences","text_build-bi-for-changing-analytical-experiences_0","callout_build-bi-for-changing-analytical-experiences_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"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",":items":{"container":{"additionalClasses":"sticky-sidebar","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"container-c26bb02dd2",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-6e56a6274d","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-77edde8627","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"qu-est-ce-que-business-intelligence\"\u003EQu’est-ce que la Business Intelligence ?\u003C/li\u003E\r\n\u003Cli data-anchor=\"fonctionnement-business-intelligence\"\u003EComment fonctionne la Business Intelligence ?\u003C/li\u003E\r\n\u003Cli data-anchor=\"donnees-couche-semantique-bi\"\u003EDonnées et couche sémantique : le socle de la BI\u003C/li\u003E\r\n\u003Cli data-anchor=\"types-outils-bi\"\u003EQuels sont les différents types d’outils de BI ?\u003C/li\u003E\r\n\u003Cli data-anchor=\"passer-bi-echelle\"\u003EPasser la BI à l’échelle et maîtriser la prolifération des tableaux de bord\u003C/li\u003E\r\n\u003Cli data-anchor=\"definir-gouverner-kpi\"\u003EDéfinir et gouverner les KPI\u003C/li\u003E\r\n\u003Cli data-anchor=\"strategie-avantages-bi\"\u003EStratégie BI et avantages de la Business Intelligence\u003C/li\u003E\r\n\u003Cli data-anchor=\"bi-moderne-ia\"\u003ELa BI moderne : analytique augmentée, BI conversationnelle et BI agentique\u003C/li\u003E\r\n\u003Cli data-anchor=\"roles-equipes-bi\"\u003ERôles et équipes de BI\u003C/li\u003E\r\n\u003Cli data-anchor=\"bi-par-secteur\"\u003ELa BI par secteur d’activité et par fonction\u003C/li\u003E\r\n\u003Cli data-anchor=\"business-intelligence-snowflake\"\u003ELa Business Intelligence sur Snowflake\u003C/li\u003E\r\n\u003Cli data-anchor=\"nouveaux-usages-bi\"\u003EPréparer la BI aux nouveaux usages analytiques\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1786318617":{"id":"flexible-column-container-4582b4386a","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy":{"id":"title-v2-db7ae96a4e","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Foire aux questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-3887eb287e","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes réponses de nos experts Snowflake à vos questions les plus fréquentes sur la Business Intelligence.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"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-52bbbfc324","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"À quel moment une architecture BI a-t-elle besoin d'une couche sémantique ?","richText":"\u003Cp\u003EUne 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