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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-v2-052d391097","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Analytique : comment les entreprises modernes utilisent les données pour prendre des décisions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-adc9bf3fce","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EL’analytique va aujourd’hui bien au-delà des tableaux de bord et des rapports, englobant les modèles prédictifs, les charges de travail en temps réel et les interfaces conversationnelles. Les programmes les plus solides associent ces capacités à des données partagées et gouvernées, à des définitions métier cohérentes et aux exigences de traitement liées aux décisions à éclairer.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-e4b60d2d5b","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","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_2045902910","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-acc5189f98",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-886684e477","youtubeVideoId":"560rRpKij6U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":true,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-24f0c944aa","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 border-top","layout":"SIMPLE","id":"container-b48684ea41",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-75482dba14","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003ESujets liés à l’analytique :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-374bcdf50b","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/\"\u003EBusiness Intelligence\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/data-analysis/\"\u003EAnalyse de données\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-f6f64b5732","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-27998ab2f0","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EL’ANALYTIQUE DÉFINIE\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EL’analytique (ou analytique des données, en anglais \u003Ci\u003Edata analytics\u003C/i\u003E) désigne l’exploitation méthodique des données pour suivre la performance, expliquer les évolutions, estimer les résultats futurs et orienter les décisions de l’entreprise.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-00da692ce7","text":"\u003Cp\u003EL’analytique a largement dépassé le stade des tableaux de bord et des rapports périodiques. Aujourd'hui, les équipes disposent de modèles prédictifs, d'outils d'analyse en temps réel et d'interfaces conversationnelles qui permettent d'interroger les données en langage naturel.\u003C/p\u003E\r\n\u003Cp\u003ELes entreprises mettent déjà ces capacités à profit. Dans \u003Ca href=\"https://www.snowflake.com/en/lp/radical-roi-generative-ai-short-form/\"\u003Eune étude Omdia de 2026 sponsorisée par Snowflake\u003C/a\u003E, 59 % des équipes d’analyse de données ont déclaré utiliser \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/generative-ai/\"\u003El’IA générative\u003C/a\u003E. Pourtant, 96 % des répondants déclarent encore rencontrer des difficultés importantes pour mettre l'IA générative en pratique.\u003C/p\u003E\r\n\u003Cp\u003EL’écart entre ces deux chiffres révèle un point essentiel sur l’analytique moderne : les nouvelles façons d'exploiter les données permettent d'obtenir plus rapidement des informations et de les rendre plus accessibles, mais elles ne dispensent pas de disposer d'un socle de données solide. La fiabilité des réponses repose toujours sur des données de confiance, des définitions métier cohérentes, un niveau de fraîcheur adapté et une approche analytique correspondant à la décision à prendre.\u003C/p\u003E\r\n\u003Cp\u003EÀ mesure que l'analytique se développe, l'enjeu est de relier les outils à des données partagées, au contexte métier et à la \u003Ca href=\"https://www.snowflake.com/fr/data-governance/\"\u003Egouvernance des données\u003C/a\u003E afin que les différentes charges de travail analytiques produisent des informations réellement exploitables.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-analytics":{"id":"title-v2-3bcc0f9698","additionalClasses":"anchor-title anchor-title--qu-est-ce-que-analytique","type":"heading2","lines":["Qu’est-ce que l’analytique ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-analytics_0":{"id":"text-65f747898a","text":"\u003Cp\u003EL’analytique utilise les données pour mesurer les performances, étudier les changements, prévoir les résultats et guider les décisions. On parle aussi d’<em>analytics</em>, terme anglais courant dans les entreprises. Elle englobe le travail récurrent de préparation et de modélisation des données, l’application de méthodes analytiques et la présentation des résultats via des rapports, des tableaux de bord, des applications et d’autres interfaces.\u003C/p\u003E\r\n\u003Cp\u003ELa bonne approche analytique dépend de la question posée et de la rapidité avec laquelle la réponse est requise. Les analyses descriptive et diagnostique aident les équipes à comprendre la performance passée et les raisons de son évolution, tandis que les méthodes prédictives et prescriptives permettent d'anticiper les résultats futurs et d'évaluer les actions possibles. La \u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/\"\u003EBusiness Intelligence\u003C/a\u003E (BI) offre aux utilisateurs un moyen récurrent de surveiller et d’explorer des indicateurs définies, tandis que l’analytique en temps réel prend en charge les décisions qui dépendent de données plus récentes.\u003C/p\u003E\r\n\u003Cp\u003EDe plus en plus, les questions métier sont explorées par le biais d’interfaces analytiques assistées par l’IA. Les requêtes en langage naturel, les explications générées et le suivi conversationnel peuvent offrir aux utilisateurs un moyen plus simple et plus flexible de mener à bien une analyse.\u003C/p\u003E\r\n\u003Cp\u003ELes équipes utilisent diverses approches analytiques pour suivre les performances au fil du temps, étudier de nouvelles questions à mesure que les conditions changent et appliquer les résultats aux décisions opérationnelles et stratégiques.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EDécouvrez une démonstration en direct de la manière dont Snowflake Interactive Analytics permet aux API, aux applications et aux agents d'IA d'exploiter l'analytique en temps réel :\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-analytics_0":{"id":"embed-f7bd781df4","youtubeVideoId":"VKnz7vMEBoY","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_v2":{"id":"title-v2-c0340e83f9","additionalClasses":"anchor-title anchor-title--importance-analytique","type":"heading2","lines":["Pourquoi l'analytique est-elle importante ? "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-82d40bc57c","text":"\u003Cp\u003E\u003Cu\u003E\u003C/u\u003EL’analytique relie les données aux décisions. Elle permet de suivre la performance, de comprendre pourquoi un résultat évolue, d’anticiper la suite et de choisir entre plusieurs actions. Sans elle, les équipes décident à partir d’impressions ou de chiffres qui se contredisent.\u003C/p\u003E\r\n\u003Cp\u003ESon intérêt dépasse la production de rapports : c’est une capacité partagée, qui repose sur des données fiables, des définitions communes et une gouvernance claire. Lorsque ces éléments sont réunis, les équipes passent moins de temps à débattre des chiffres et davantage à agir.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_analytics-vs-data-analysis-whats-the-difference":{"id":"title-v2-df47792d93","additionalClasses":"anchor-title anchor-title--analytique-et-analyse-de-donnees","type":"heading2","lines":["Analytique et analyse de données : quelle différence ?"],":type":"snowflake-site/components/title-v2"},"text_analytics-vs-data-analysis-whats-the-difference_0":{"id":"text-89b16b3f82","text":"\u003Cp\u003ELes termes \u003Ci\u003Eanalytique\u003C/i\u003E et \u003Ci\u003Eanalyse de données\u003C/i\u003E se recoupent largement dans l'usage courant, et les professionnels ne tracent pas toujours la même frontière entre les deux. En règle générale, toutefois, \u003Ca href=\"https://www.snowflake.com/fr/analytics/data-analysis/\"\u003El’analyse de données\u003C/a\u003E désigne une activité précise, tandis que l'analytique recouvre la capacité plus large qui permet aux organisations de mener cette activité de manière répétée et cohérente.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analyse de données\u003C/b\u003E consiste à examiner des données pour répondre à une question particulière. Un analyste peut chercher à comprendre pourquoi l'attrition client a augmenté au dernier trimestre, comparer les taux de conversion de différentes campagnes ou déterminer quels produits ont le plus contribué à la baisse de la marge.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analytique\u003C/b\u003E comprend les systèmes, les processus et les pratiques qui rendent ce travail reproductible. Elle englobe les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/modernizing-data-pipelines/\"\u003Epipelines de données\u003C/a\u003E qui fournissent les informations, les définitions d'indicateurs qui garantissent la cohérence des calculs, les méthodes analytiques utilisées pour étudier les questions, les outils par lesquels les utilisateurs accèdent aux résultats et la gouvernance nécessaire pour les maintenir au fil du temps.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELe reporting recoupe l'analytique, notamment dans le domaine de la Business Intelligence, mais se concentre généralement sur la présentation d'indicateurs et de résultats définis. L’analytique va plus loin dans l’investigation, l’explication, la prédiction et l’aide à la décision. Dans la pratique, les tableaux de bord incluent souvent des fonctions analytiques, tandis que les flux de travail d’analytique se terminent fréquemment par un rapport ou une visualisation.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive":{"id":"title-v2-8534d9f565","additionalClasses":"anchor-title anchor-title--types-analyses-donnees","type":"heading2","lines":["Quels sont les 4 types d'analyses de données ?"],":type":"snowflake-site/components/title-v2"},"text_the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive_0":{"id":"text-dec70cd749","text":"\u003Cp\u003EUn cadre courant répartit l’analytique, ou analyse de données, en quatre types selon la question posée : l’analyse descriptive, diagnostique, prédictive et prescriptive.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analyse descriptive\u003C/b\u003E résume ce qui s’est déjà produit. Les rapports sur le chiffre d'affaires, les tendances du trafic web, les niveaux de stock et l'historique de l'activité client relèvent tous de cette catégorie.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analyse diagnostique\u003C/b\u003E étudie les facteurs associés à un résultat observé. Après avoir identifié une baisse des ventes, par exemple, un analyste peut segmenter les données par région, par produit ou par cohorte de clients pour comprendre où le changement s’est produit et ce qui y a contribué.\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/predictive-analytics/\"\u003E\u003Cb\u003EL’analyse prédictive\u003C/b\u003E\u003C/a\u003E utilise des données historiques et des méthodes statistiques ou de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E pour estimer les résultats futurs, tels que la demande, le risque d’attrition ou la défaillance des équipements.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’analyse prescriptive\u003C/b\u003E évalue les actions possibles à la lumière des objectifs, des contraintes et des résultats attendus. Les modèles d’optimisation, les simulations et les règles de décision viennent fréquemment appuyer ce travail.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EPour ces quatre types, des données fiables et des définitions métier stables constituent la base analytique.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-an-analytics-workflow-actually-runs":{"id":"title-v2-5fa5ab4d79","additionalClasses":"anchor-title anchor-title--flux-de-travail-analytique","type":"heading2","lines":["Comment fonctionne un flux de travail analytique ?"],":type":"snowflake-site/components/title-v2"},"text_how-an-analytics-workflow-actually-runs_0":{"id":"text-ef7c86e4c2","text":"\u003Cp\u003EL’analytique commence souvent avant même que quiconque n’ouvre un outil de BI ou n’écrive une requête SQL. La première tâche consiste à décider quelles questions nécessitent des réponses et comment ces réponses influenceront les décisions.\u003C/p\u003E\n\u003Cp\u003EUn flux de travail pratique comprend généralement cinq étapes interconnectées :\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cb\u003EDéfinir l’objectif :\u003C/b\u003E définissez la question, la décision ou le résultat que l’analyse doit étayer. La question « Pourquoi les ventes baissent-elles ? » peut conduire à examiner si une diminution du volume de transactions, des changements tarifaires ou l'évolution de la répartition des produits vendus expliquent une baisse par rapport à l'année précédente sur un marché donné. L’objectif détermine quelles données sont pertinentes, quel doit être le niveau de fraîcheur du résultat et quel niveau d’incertitude la décision tolérera.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ECollecter les données pertinentes :\u003C/b\u003E selon la question posée, les informations nécessaires peuvent comprendre des tables structurées, des enregistrements semi-structurés ou des données non structurées, comme des documents et des transcriptions d'échanges avec le support. La source façonne également les attentes en matière de fraîcheur, d’exhaustivité et d’accès.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPréparer les données :\u003C/b\u003E le traitement des données rend ces sources utilisables par le biais d’activités telles que le nettoyage, la transformation, la jointure et la validation. À ce stade, les équipes doivent également comprendre si les champs contiennent les valeurs que les analystes pensent qu’ils contiennent.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnalyser et interpréter :\u003C/b\u003E les analystes appliquent des requêtes, des méthodes statistiques, des visualisations, des modèles ou d’autres techniques analytiques, puis interprètent le résultat dans son contexte métier. Même un résultat mathématiquement correct peut facilement être mal interprété lorsque les définitions, les populations étudiées ou les hypothèses manquent de clarté.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EConnecter le résultat à une décision ou à une action :\u003C/b\u003E certaines analyses éclairent une décision opérationnelle immédiate ; d'autres orientent la planification, affinent une hypothèse ou permettent d'écarter une option. Dans tous les cas, le résultat doit servir à quelque chose au-delà de l'analyse elle-même.\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003ELe véritable travail analytique a tendance à boucler sur ces étapes. Un résultat inattendu conduit l'analyste à réexaminer les données sources, à revoir une définition ou à préciser le périmètre de sa question. Une nouvelle décision génère à son tour de nouvelles données, qui alimentent ensuite un nouveau cycle d'analyse.\u003C/p\u003E\n\u003Cp\u003E« En fin de compte, la valeur se manifeste dans la rapidité de décision et l’alignement organisationnel », déclare \u003Ca href=\"https://www.snowflake.com/en/blog/authors/josh-klahr/\"\u003EJosh Klahr\u003C/a\u003E, Head of Product Management for Analytics chez Snowflake. « Le critère, c'est de savoir si les équipes transverses passent moins de temps à débattre pour déterminer quels indicateurs sont les bons, et davantage à prendre des décisions fondées sur une logique métier commune. »\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_how-an-analytics-workflow-actually-runs_0":{"id":"quote-item-d735743bd8","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"En fin de compte, la valeur se manifeste dans la rapidité de décision et l’alignement organisationnel. Le critère, c'est de savoir si les équipes transverses passent moins de temps à débattre pour déterminer quels indicateurs sont les bons, et davantage à prendre des décisions fondées sur une logique métier commune.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer":{"id":"title-v2-a9abbc93fa","additionalClasses":"anchor-title anchor-title--qualite-donnees-couche-semantique","type":"heading2","lines":["Qualité des données et couche sémantique partagée"],":type":"snowflake-site/components/title-v2"},"text_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer_0":{"id":"text-7e89fbf341","text":"\u003Cp\u003EUn résultat analytique hérite des caractéristiques des données sous-jacentes. Des enregistrements manquants, des valeurs non actualisées, des identifiants incohérents ou des erreurs de correspondance entre champs influent sur ce que reflète l'analyse, même lorsque la requête ou le modèle fonctionne exactement comme prévu.\u003C/p\u003E\r\n\u003Cp\u003ELes équipes évaluent généralement la \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/a-practical-guide-to-data-quality/\"\u003Equalité des données\u003C/a\u003E selon plusieurs dimensions :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrécision :\u003C/b\u003E les valeurs représentent-elles correctement l’événement ou l’entité sous-jacente ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExhaustivité :\u003C/b\u003E les valeurs et les enregistrements requis sont-ils présents ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECohérence :\u003C/b\u003E les mêmes informations concordent-elles entre les systèmes et les ensembles de données ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFraîcheur :\u003C/b\u003E les données sont-elles suffisamment récentes pour la décision qu’elles appuient ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EValidité :\u003C/b\u003E les valeurs sont-elles conformes aux formats, plages et règles attendus ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUnicité :\u003C/b\u003E les enregistrements en double faussent-ils les comptages ou d’autres calculs ?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EAvant de définir des règles pour ces dimensions, les équipes doivent établir le profil des données existantes et comprendre leurs distributions réelles, leurs taux de valeurs nulles, leurs formats et leurs relations. Un seuil de fraîcheur adapté à la planification financière trimestrielle, par exemple, ne conviendrait pas à la détection des fraudes.\u003C/p\u003E\r\n\u003Cp\u003ECependant, la qualité des données ne garantit pas à elle seule la cohérence analytique. Deux équipes peuvent travailler à partir d’enregistrements parfaitement valides, mais rapporter des chiffres d’affaires différents si elles définissent les revenus récurrents annuels différemment, par exemple. Un tableau de bord peut inclure immédiatement les revenus d’expansion, un autre peut attendre la date de début d’un contrat, et un troisième peut exclure une catégorie de produits particulière. Sans une définition convenue, ces trois calculs pourraient être techniquement corrects tout en produisant des réponses différentes à la même question métier.\u003C/p\u003E\r\n\u003Cp\u003EUne couche sémantique résout ce problème. Rajhans Samdani, Principal Software Engineer chez Snowflake, explique : « Les modèles sémantiques constituent l’interface partagée qui permet aux tableaux de bord de BI et aux agents d’IA de fonctionner sur les mêmes définitions. » Les définitions d'indicateurs, les relations entre les entités et d’autres contextes sémantiques offrent aux analystes, aux tableaux de bord, aux applications et aux \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/\"\u003Esystèmes d’IA\u003C/a\u003E une interprétation commune des données sous-jacentes.\u003C/p\u003E\r\n\u003Cp\u003EUne couche sémantique est particulièrement cruciale pour les interfaces en langage naturel. Lorsque les définitions divergent, le système peut reproduire des réponses inadaptées à plus grande échelle, et il peut être plus difficile d’identifier le problème. La cohérence sémantique joue un rôle opérationnel de plus en plus important à mesure que les utilisateurs interagissent avec les données par le biais de systèmes conversationnels et d’agents.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer_0":{"id":"text-2e87286394","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUne erreur fréquente consiste à supposer qu'un calcul techniquement correct garantit une réponse utile. Deux équipes peuvent appliquer une logique valide aux mêmes données et produire des résultats contradictoires si elles définissent les termes différemment.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_business-intelligence-reporting-and-dashboards-for-the-whole-business":{"id":"title-v2-e163f0f0db","additionalClasses":"anchor-title anchor-title--business-intelligence","type":"heading2","lines":["Business Intelligence : reporting et tableaux de bord"],":type":"snowflake-site/components/title-v2"},"text_business-intelligence-reporting-and-dashboards-for-the-whole-business_0":{"id":"text-8bc50ea959","text":"\u003Cp\u003ELa Business Intelligence s’inscrit dans la discipline plus large de l’analytique, en se concentrant fortement sur le reporting, les tableaux de bord, la visualisation et l’accès aux indicateurs métier récurrentes.\u003C/p\u003E\r\n\u003Cp\u003EUn environnement de BI peut mettre à disposition d'une équipe financière un tableau de bord mensuel du chiffre d'affaires, fournir aux responsables commerciaux des indicateurs sur le pipeline et permettre aux équipes opérationnelles d'explorer l'évolution des stocks. Comme ces questions reviennent régulièrement, les équipes définissent généralement à l'avance les indicateurs et les modèles de données sous-jacents. Les utilisateurs métier disposent ainsi d'un cadre cohérent pour examiner la performance sans devoir reprendre chaque analyse de zéro.\u003C/p\u003E\r\n\u003Cp\u003ELes grands modèles de langage (LLM) et l’IA générative offrent aux utilisateurs de nouvelles façons d’interroger et d’explorer les données analytiques. Outre les filtres et les tableaux de bord, les utilisateurs peuvent interagir avec les données métier par le biais de questions en langage naturel, de visualisations générées et de suivis conversationnels.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_real-time-analytics-acting-on-data-as-it-arrives":{"id":"title-v2-6ec0aee1f1","additionalClasses":"anchor-title anchor-title--analytique-temps-reel","type":"heading2","lines":["Analytique en temps réel : agir dès l'arrivée des données"],":type":"snowflake-site/components/title-v2"},"text_real-time-analytics-acting-on-data-as-it-arrives_0":{"id":"text-655ae30eb1","text":"\u003Cp\u003EL’analyse en temps réel est souvent définie autour de la latence : des millisecondes, des secondes ou des minutes entre l’occurrence d’un événement et son apparition dans un résultat analytique. Toutefois, la nécessité de recourir à l'analyse en temps réel et la latence cible appropriée dépendent de la décision que le résultat doit éclairer. Si une réponse datant de cinq minutes conduit à la même décision qu'une réponse datant de cinq secondes, réduire davantage la latence apporte peu de valeur en pratique.\u003C/p\u003E\r\n\u003Cp\u003EDes exigences de fraîcheur plus strictes nécessitent généralement des données actualisées en continu, un traitement à faible latence et une infrastructure analytique conçue pour traiter les requêtes pendant l'ingestion de nouveaux enregistrements.\u003C/p\u003E\r\n\u003Cp\u003EPour permettre l'analyse en temps réel, le système doit maîtriser à la fois la latence et les accès simultanés : les données doivent parvenir rapidement à la couche analytique, tandis que de nombreux utilisateurs ou applications interrogent des informations en constante évolution sans engendrer de délais inacceptables.\u003C/p\u003E\r\n\u003Cp\u003EPour les charges de travail associées à des décisions qui peuvent s'appuyer sur des données moins récentes, le traitement par lots reste plus simple et souvent moins coûteux.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-analytics-from-predefined-paths-to-interactive-analysis":{"id":"title-v2-1af6a3b8ac","additionalClasses":"anchor-title anchor-title--analytique-ia","type":"heading2","lines":["Analytique et IA : vers l'analyse interactive"],":type":"snowflake-site/components/title-v2"},"text_ai-analytics-from-predefined-paths-to-interactive-analysis_0":{"id":"text-54293c857f","text":"\u003Cp\u003EL'analytique en libre-service a rendu une grande partie du travail d'analyse plus accessible aux utilisateurs métier, en leur proposant des tableaux de bord gouvernés, des fonctions d'exploration par glisser-déposer, des filtres et des requêtes ad hoc. L'IA a considérablement élargi les possibilités du libre-service en permettant aux utilisateurs de formuler leurs questions en langage naturel et de poursuivre l'analyse au fil d'un dialogue.\u003C/p\u003E\r\n\u003Cp\u003EL'IA générative contribue largement à cette expérience grâce à la conversion du langage naturel en SQL, à la génération d'explications et au requêtage conversationnel, tandis que le machine learning sous-tend les méthodes prédictives et prescriptives utilisées notamment pour prévoir les résultats et évaluer les actions possibles.\u003C/p\u003E\r\n\u003Cp\u003EL'IA peut également aider les analystes à repérer des tendances, à générer des explications et à synthétiser les résultats, des fonctionnalités souvent regroupées sous le terme d'analytique augmentée.\u003C/p\u003E\r\n\u003Cp\u003EÀ mesure que le parcours analytique repose moins sur une navigation prédéfinie et davantage sur des requêtes générées et des flux de travail en plusieurs étapes, le contexte sémantique et la gouvernance jouent un rôle croissant pour maintenir la cohérence des résultats avec les définitions métier.\u003C/p\u003E\r\n\u003Cp\u003E« L'introduction de l'IA conversationnelle fait évoluer le travail : il ne s'agit plus de concevoir des tableaux de bord et des rapports, mais de gérer le contexte », explique Josh Klahr. « Le système doit comprendre l'intention de l'utilisateur et appliquer les définitions métier et les règles analytiques adaptées à la question posée. »\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EPour aller plus loin, découvrez les avantages et les cas d’usage de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-for-business-intelligence/\"\u003El’IA dans la Business Intelligence \u003C/a\u003E\u003C/i\u003E\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-for-business-intelligence/\"\u003E>\u003C/a\u003E.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_analytics-across-industries":{"id":"title-v2-9bae8ffb40","additionalClasses":"anchor-title anchor-title--exemples-analytique-par-secteur","type":"heading2","lines":["Exemples d'analytique par secteur d'activité"],":type":"snowflake-site/components/title-v2"},"text_analytics-across-industries_0":{"id":"text-8a27d9d111","text":"\u003Cp\u003ELe secteur modifie davantage le contexte autour de l’analytique que les méthodes analytiques sous-jacentes. Les principales différences tiennent généralement aux données analysées, au niveau de fraîcheur nécessaire à la décision et aux contraintes de gouvernance ou de réglementation qui encadrent leur utilisation.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ESecteur\u003C/th\u003E\r\n\u003Cth\u003EComment l’analytique est appliquée\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERetail et grande distribution\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes détaillants analysent la demande, les stocks, les prix, les promotions, le comportement des clients et les performances de la chaîne d’approvisionnement. Certaines décisions, comme la planification du réapprovisionnement, tolèrent des délais d'analyse plus longs ; la disponibilité des stocks en ligne et d'autres cas d'usage opérationnels exigent des données bien plus récentes.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESanté\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes organismes de santé analysent les parcours patients, l’utilisation des ressources, les données de recherche et les performances financières. Les exigences en matière de confidentialité, de sécurité et de gouvernance déterminent qui peut accéder aux données sous-jacentes et à quelles fins. En France, les données de santé relèvent du RGPD et, pour leur hébergement, de la certification HDS : ces exigences déterminent qui accède aux données et à quelles fins.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EServices financiers\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes institutions financières utilisent l’analytique pour les risques, la fraude, l’activité des clients, les performances des portefeuilles et le reporting réglementaire. Le volume des transactions et les décisions urgentes imposent souvent des exigences importantes en matière de fraîcheur et de simultanéité.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESecteur public\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes administrations arbitrent entre fraîcheur, souveraineté et sécurité : la qualification SecNumCloud de l'ANSSI peut conditionner le choix de la plateforme.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EAssurance\u003C/td\u003E\r\n\u003Ctd\u003ELes assureurs analysent la sinistralité, la tarification, les risques et la relation client. Les modèles prédictifs y occupent une place importante, et la traçabilité des données conditionne la confiance accordée aux résultats. Découvrez des \u003Ca href=\"https://www.snowflake.com/fr/blog/16-ways-insurance-companies-can-use-data-ai/\"\u003Ecas d’usage des données et de l’IA dans l’assurance\u003C/a\u003E.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ESecteur public\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes administrations exploitent l’analytique pour piloter les services, répartir les ressources et évaluer l’effet de leurs actions. Elles arbitrent entre fraîcheur des données, souveraineté et sécurité : la qualification SecNumCloud de l’ANSSI peut conditionner le choix de la plateforme.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EIndustrie\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes fabricants connectent les données de production, d’équipement, de chaîne d’approvisionnement, de qualité et financières pour surveiller les opérations, prévoir la demande et identifier les conditions associées aux temps d’arrêt ou aux défauts.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_analytics-tools-and-platforms":{"id":"title-v2-d863a01c1f","additionalClasses":"anchor-title anchor-title--outils-plateformes-analytique","type":"heading2","lines":["Outils et plateformes d’analytique"],":type":"snowflake-site/components/title-v2"},"text_analytics-tools-and-platforms_0":{"id":"text-af81ae5bb2","text":"\u003Cp\u003EUn environnement analytique nécessite plusieurs capacités tout au long du cheminement, des données brutes au résultat analytique. Les données doivent être ingérées et transformées, stockées et traitées, interprétées au moyen de définitions métier cohérentes et mises à disposition via des interfaces adaptées aux différents utilisateurs et charges de travail.\u003C/p\u003E\r\n\u003Cp\u003EPour l’analytique en particulier, ces capacités comprennent généralement :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EStockage et calcul :\u003C/b\u003E traiter les données structurées, semi-structurées et non structurées utilisées pour l’analyse, y compris à grande échelle avec l’\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/big-data-analytics/\"\u003Eanalyse du Big Data\u003C/a\u003E.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"/fr/fundamentals/data-ingestion/\"\u003EIngestion\u003C/a\u003E et transformation des données :\u003C/b\u003E intégrer les données sources dans l’environnement analytique et les préparer pour leur utilisation.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECapacités sémantiques et métriques :\u003C/b\u003E définir des concepts, des mesures et des relations métier partagés.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"/fr/fundamentals/data-visualization/\"\u003EBI et visualisation\u003C/a\u003E :\u003C/b\u003E prendre en charge les tableaux de bord, le reporting récurrent et l’exploration interactive.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENotebooks et environnements de code :\u003C/b\u003E prendre en charge l’analyse statistique, le machine learning et des flux de travail analytiques plus personnalisés.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EInterfaces conversationnelles :\u003C/b\u003E permettre aux utilisateurs de poser des questions analytiques en langage naturel et de poursuivre une investigation par des questions de suivi.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EIdéalement, ces capacités sont réunies au sein d’une même plateforme de données. Lorsque la plateforme peut prendre en charge plusieurs charges de travail analytiques sur les mêmes données gouvernées, les équipes peuvent limiter les copies, les pipelines et la logique métier qu'elles devraient autrement recréer à mesure que les données circulent entre les systèmes.\u003C/p\u003E\r\n\u003Cp\u003EUne plateforme doit être suffisamment flexible pour répondre aux besoins en ressources de calcul, en latence et en accès simultanés, tout en préservant un socle commun de données, de définitions et de gouvernance pour l'ensemble de ces charges de travail.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the_four_typ":{"id":"card-v2-7a9bd572a1","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/customers/all-customers/case-study/tapestry/"},"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 : Tapestry"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--2719ba48-ef40-41ef-9bed-722192d6c0d5/tapestry%25403x.png?quality=85&preferwebp=true","alt":"Tapestry logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELe groupe de mode Tapestry (Coach, Kate Spade, Stuart Weitzman) a fait de Snowflake la plateforme de données commune à toutes ses marques. En réunissant ses données clients, de navigation web et de trafic dans un même environnement, connecté à Tableau et à ses autres outils, le groupe a créé Apollo, sa plateforme d’analyse client en libre-service : les équipes y identifient par exemple les produits à démarquer lors des temps forts commerciaux. Chaque jour, Tapestry traite environ 4 milliards de lignes et exécute plus de 100 processus de données majeurs (chiffres de 2023).\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_getting-started-with-analytics":{"id":"title-v2-ea93c1ee99","additionalClasses":"anchor-title anchor-title--se-lancer-analytique","type":"heading2","lines":["Comment se lancer dans l’analytique ?"],":type":"snowflake-site/components/title-v2"},"text_getting-started-with-analytics_0":{"id":"text-3a3ff4143c","text":"\u003Cp\u003EDévelopper une capacité analytique consiste moins à suivre un modèle de maturité fixe qu’à prendre quelques décisions fondamentales dans le bon ordre. Les équipes doivent savoir quelles questions elles appuient, comment les indicateurs sous-jacents sont définis, quelle fraîcheur et quel traitement ces décisions nécessitent, et comment les personnes accéderont aux résultats.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ECommencez par la décision et les mesures qui la sous-tendent :\u003C/b\u003E identifiez les questions auxquelles les personnes doivent répondre, les indicateurs dont dépendent ces questions et la rapidité avec laquelle les informations sous-jacentes doivent refléter les changements dans l’entreprise. Un rapport de performance récurrent, une prévision de la demande et une décision opérationnelle imposent chacun des exigences différentes à l’environnement analytique.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDéfinissez les indicateurs importants dès le début :\u003C/b\u003E si le chiffre d'affaires, l'attrition ou la notion de client actif sont définis différemment d'une équipe ou d'un flux de travail analytique à l'autre, l'ajout de tableaux de bord, de modèles ou d'interfaces conversationnelles multiplie le travail de réconciliation à effectuer par la suite. Une appropriation claire donne également aux équipes un moyen de gérer les modifications apportées à ces définitions au fil du temps.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAdaptez les exigences en matière de données et de traitement à la charge de travail :\u003C/b\u003E des actualisations programmées peuvent suffire aux usages récurrents de BI, tandis que l'analytique opérationnelle peut nécessiter un traitement continu des données et une latence plus faible. Les charges de travail prédictives introduisent des exigences supplémentaires concernant la couverture historique, la qualité des caractéristiques et la question de savoir si les données passées représentent toujours les conditions actuelles.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EÉlargissez l’accès analytique en fonction des méthodes de travail :\u003C/b\u003E la BI et le libre-service gouverné répondent aux besoins d'exploration récurrents des utilisateurs métier ; les notebooks et les environnements de programmation permettent de mener des analyses plus spécialisées ; les interfaces conversationnelles offrent aux utilisateurs une autre manière de formuler leurs questions dans le langage métier. Ces modes d'accès fonctionnent au mieux lorsqu'ils s'appuient sur des données gouvernées et un contexte métier partagés, plutôt que de les recréer séparément.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDéveloppez la culture et la gouvernance des données parallèlement à l’accès :\u003C/b\u003E les utilisateurs doivent disposer d'un contexte suffisant pour comprendre ce que représente un indicateur, quelle population il décrit et d'où vient l'incertitude du résultat. La gouvernance apporte les mécanismes de contrôle correspondants en matière d'accès, de responsabilités, de définitions et de traçabilité.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELes organisations mettront en place ces capacités dans des ordres différents. Le fait de conserver les données et le contexte métier partagés entre elles réduit la quantité d’infrastructure et de logique analytique qui doit être reconstruite à mesure que de nouvelles charges de travail sont ajoutées.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_getting-started-with-analytics_0":{"id":"text-4eed1ee0eb","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cb\u003EASTUCE\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ECommencez par la décision que vous devez prendre en charge, puis remontez à l'indicateur, à la fraîcheur des données et à la méthode analytique qu’elle requiert. Cela permet de lier les choix technologiques à une exigence métier réelle.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-modern-analytics":{"id":"title-v2-c48b1fd39e","additionalClasses":"anchor-title anchor-title--snowflake-analytique-moderne","type":"heading2","lines":["Comment Snowflake accompagne l’analytique moderne"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-modern-analytics_0":{"id":"text-3170334d34","text":"\u003Cp\u003ESnowflake prend en charge les charges de travail analytiques sur un socle partagé et gouverné. Les équipes peuvent ainsi combiner BI, analyse en temps réel, machine learning  et requêtage assisté par l'IA sans avoir à gérer un environnement de données distinct pour chaque cas d'usage.\u003C/p\u003E\r\n\u003Cp\u003EPour la BI et l'analyse interactive, Snowflake fournit des ressources de calcul élastiques et permet à de nombreux utilisateurs de travailler simultanément sur les mêmes données sous-jacentes. L'analyse en temps réel et l'analyse opérationnelle peuvent s'appuyer sur des données actualisées en continu, tandis que les flux de travail de data science et de machine learning  utilisent le même socle gouverné pour la préparation des variables, l'expérimentation et le développement de modèles.\u003C/p\u003E\r\n\u003Cp\u003ELes fonctionnalités sémantiques apportent un contexte métier commun à l'ensemble de ces charges de travail. En centralisant la définition des indicateurs, des dimensions et des relations, les organisations peuvent faire en sorte que les tableaux de bord, les applications et les interfaces assistées par l'IA s'appuient sur les mêmes définitions métier, plutôt que de recréer cette logique séparément.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E étend cette base à l’analytique conversationnelle et générative, tandis que \u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003ESnowflake Horizon\u003C/a\u003E applique des contrôles de gouvernance, de sécurité, de découverte et de conformité sur les données, les applications et l’IA. Le résultat est un environnement analytique dans lequel différentes charges de travail peuvent utiliser le traitement et les interfaces dont elles ont besoin tout en restant connectées aux données et à la gouvernance partagées.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_where-analytics-goes-next":{"id":"title-v2-f8eb7fb056","additionalClasses":"anchor-title anchor-title--avenir-analytique","type":"heading2","lines":["L’avenir de l’analytique"],":type":"snowflake-site/components/title-v2"},"text_where-analytics-goes-next_0":{"id":"text-94839ef2e0","text":"\u003Cp\u003ELes utilisateurs métier s’attendront de plus en plus à passer des indicateurs récurrents à l’investigation ad hoc, aux prévisions et aux requêtes conversationnelles sans avoir à comprendre quel pipeline, modèle ou outil se trouve sous chaque résultat. Le socle de données doit donc être plus exigeant : les définitions doivent voyager avec 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d’examiner des données pour répondre à une question particulière. L'analytique désigne à la fois une discipline plus large et une capacité opérationnelle permettant de produire de façon répétée des réponses analytiques, en s'appuyant sur des données, des processus, des modèles, des outils et une gouvernance. Les praticiens utilisent souvent les termes de manière interchangeable.\u003C/p\u003E\r\n"},{"title":"Quelle est la différence entre le reporting et l’analytique ?","richText":"\u003Cp\u003ELe reporting présente généralement des indicateurs connus décrivant ce qui s'est produit, souvent au moyen de rapports récurrents ou de tableaux de bord. L’analytique comprend également l’investigation, l’explication, la prédiction et l’aide à la décision. Les deux se recoupent fréquemment : un tableau de bord peut intégrer des fonctions d'exploration analytique, tandis qu'un flux de travail analytique peut aboutir à un rapport.\u003C/p\u003E\r\n"},{"title":"Avez-vous besoin d’une analyse en temps réel, ou le traitement par lots est-il suffisant ?","richText":"\u003Cp\u003ECommencez par définir le niveau de fraîcheur des données nécessaire à la prise de décision. Lorsqu'un résultat tardif risque de réduire, voire d'annuler, l'utilité de la décision, comme dans certains cas de détection des fraudes ou de pilotage opérationnel, un traitement en temps réel ou quasi réel est adapté. Lorsque la décision peut s'appuyer sur des données moins récentes, le traitement par lots est généralement plus simple et moins coûteux.\u003C/p\u003E\r\n"},{"title":"Par lequel des quatre types d'analytique commencer ?","richText":"\u003Cp\u003EL'analytique descriptive constitue souvent un point de départ utile, car elle établit les indicateurs et la compréhension des évolutions passées sur lesquels s'appuieront les analyses ultérieures. Le point de départ le plus adapté dépend toutefois du cas d'usage : une organisation dont le besoin de prévision est clairement défini peut investir plus tôt dans l'analytique prédictive. Dans tous les cas, la qualité des données et la stabilité des définitions d'indicateurs servent de socle aux travaux qui suivent.\u003C/p\u003E\r\n"},{"title":"L'analytique et la Business Intelligence désignent-elles la même chose ?","richText":"\u003Cp\u003ENon. 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