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style=\"padding-top: 8.0px;\"\u003EGrundlagen\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-af7da6bf08","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Business Intelligence: Zuverlässige Insights in großem Maßstab"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-c93460730d","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EBusiness Intelligence umfasst heute weit mehr als nur Dashboards und Berichte. 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Das Meeting gerät ins Stocken. Analyst:innen beginnen, Filter und SQL zu überprüfen, während die Abteilungsleiter:innen im Raum darauf warten, welcher Zahl sie vertrauen können.\u003C/p\u003E\r\n\u003Cp\u003EBeide Dashboards wurden erfolgreich aktualisiert. Beide verwenden genehmigte Daten. Die Diskrepanz liegt tiefer im Data Stack: Möglicherweise wenden die Dashboards unterschiedliche Definitionen von Umsatz an, verwenden unterschiedliche Transaktionsdaten oder stützen sich auf Joins, die die zugrunde liegenden Datensätze unterschiedlich aggregieren.\u003C/p\u003E\r\n\u003Cp\u003EDa sich Business Intelligence auf Self-Service-Tools, eingebettete Anwendungen, natürlichsprachliche Schnittstellen und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003Eagentische KI\u003C/a\u003E ausweitet, haben Diskrepanzen wie diese mehr Möglichkeiten, sich zu verbreiten. Zuverlässige Ergebnisse hängen von den Schichten unterhalb der Schnittstelle ab: den Datenmodellen, Metrikdefinitionen, Beziehungen und der analytischen Infrastruktur, die bestimmen, wie eine Frage interpretiert und beantwortet wird.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-business-intelligence":{"id":"title-v2-4a6b9dc176","additionalClasses":"anchor-title anchor-title--what-is-business-intelligence","type":"heading2","lines":["Was ist Business Intelligence?"],":type":"snowflake-site/components/title-v2"},"text_what-is-business-intelligence_0":{"id":"text-16ab5f4bf6","text":"\u003Cp\u003EBusiness Intelligence ist die Gesamtheit der Praktiken und Technologien, die Unternehmen nutzen, um Geschäftsdaten in Kennzahlen, Analysen und Entscheidungshilfen umzuwandeln. BI umfasst die Datenmodelle und Analyseprozesse hinter der Arbeit sowie die Software, die zur Abfrage, Visualisierung und Verteilung der Ergebnisse verwendet wird.\u003C/p\u003E\r\n\u003Cp\u003EBetrachten Sie das Umsatz-Dashboard aus dem vierteljährlichen Review. Transaktions- und Finanzsysteme liefern die zugrunde liegenden Datensätze. \u003Ca href=\"https://www.snowflake.com/de/fundamentals/modernizing-data-pipelines/\"\u003EDatenpipelines\u003C/a\u003E führen diese Datensätze zusammen und bereiten sie für die Analyse auf, während ein Datenmodell festlegt, wie Bestellungen, Kund:innen, Produkte und Zeiträume zusammenhängen. Die Umsatzkennzahl wendet anschließend die im Unternehmen vereinbarte Berechnungsmethodik auf diese Datenstruktur an. Ein BI-Tool fragt dann die Daten ab und präsentiert das Ergebnis im Dashboard, wo Finanzführungskräfte es zur Bewertung der Quartalsleistung nutzen.\u003C/p\u003E\r\n\u003Cp\u003EJeder Schritt ist Teil von BI, auch wenn das Dashboard in der Regel der einzige Teil ist, den die Nutzer:innen sehen. Jede Änderung im Vorfeld – ein Quellsystem, eine Metrikdefinition oder eine Beziehung zwischen Tabellen – kann die am Ende des Prozesses präsentierte Zahl verändern. BI umfasst daher sowohl die analytische Erfahrung als auch die Daten, die Logik und die Prozesse, die sie hervorbringen.\u003C/p\u003E\r\n\u003Cp\u003EBusiness Intelligence ist Teil des breiteren Feldes der Analytics. Ein Großteil von BI konzentriert sich auf wiederholbare Messungen, Berichte und Explorationen, während moderne BI auch Funktionen wie statistische Analysen, Prognosen, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003EMachine Learning\u003C/a\u003E und KI-gestützte Analysen umfasst. In der Praxis sind die Grenzen zunehmend fließend: Ein Dashboard kann eine Prognose oder eine Anomaliewarnung enthalten, eine konversationelle Schnittstelle kann Folgefragen in natürlicher Sprache beantworten, und eine operative Anwendung kann Analyseergebnisse direkt in einem Geschäftsprozess anzeigen. Der rote Faden ist die strukturierte geschäftliche Entscheidungsfindung auf Basis von Daten.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EErfahren Sie, wie NASCAR fortschrittliche Datenanalysen und digitale Dienste nutzt, um das Fan-Engagement und die Sicherheit zu verbessern:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-business-intelligence_0":{"id":"embed-c03fbf4b2f","youtubeVideoId":"Is9NhAvVhTs","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_how-business-intelligence-works":{"id":"title-v2-12f4550a57","additionalClasses":"anchor-title anchor-title--how-business-intelligence-works","type":"heading2","lines":["Wie Business Intelligence funktioniert"],":type":"snowflake-site/components/title-v2"},"text_how-business-intelligence-works_0":{"id":"text-f07dd93d12","text":"\u003Cp\u003EEin BI-Ergebnis steht am Ende eines Datenpfads, der bei operativen Systemen beginnt und mit einer Entscheidung endet. Zwischen diesen Punkten müssen Daten gesammelt, über Quellen hinweg abgeglichen, für die Analyse modelliert, abgefragt und in einer für Menschen nutzbaren Form bereitgestellt werden. Die Implementierungen unterscheiden sich, aber dieser Workflow folgt im Allgemeinen fünf Phasen.\u003C/p\u003E\r\n\u003Ch3\u003E1. Daten sammeln\u003C/h3\u003E\r\n\u003Cp\u003EBI beginnt mit Daten, die im gesamten Unternehmen und seinem Ökosystem generiert werden: Transaktions- und Point-of-Sale-Systeme, Customer-Relationship-Management- (CRM) und Enterprise-Resource-Planning-Plattformen (ERP), Finanzanwendungen, E-Commerce-Systeme, Marketing- und Werbeplattformen, Websites und mobile Apps, Kundensupportsysteme, Supply-Chain- und Bestandsanwendungen, vernetzte Geräte sowie relevante externe oder Partnerdaten.\u003C/p\u003E\r\n\u003Cp\u003EDie Sammlung stellt fest, welche Informationen verfügbar sind und wie gut sie die aktuellen Geschäftsaktivitäten widerspiegeln. Die Anforderungen variieren je nach Workload. Beispielsweise benötigen ein monatlicher Verkaufsbericht und ein Operations-Dashboard, das den ganzen Tag über aktualisiert wird, unterschiedliche Ingestionsmuster, Aufbewahrungshistorien und Latenzzeiten.\u003C/p\u003E\r\n\u003Ch3\u003E2. Daten integrieren\u003C/h3\u003E\r\n\u003Cp\u003EWenn sich die Analyse über mehrere Systeme erstreckt, müssen Datensätze abgeglichen werden. Die Integration führt Daten zusammen und standardisiert gleichzeitig Schemata, Formate, Identifikatoren und Zeitdarstellungen; je nach Quelle kann sie auch Deduplizierung, Validierung, Identitätsauflösung und Regeln für den Umgang mit fehlenden oder widersprüchlichen Werten umfassen.\u003C/p\u003E\r\n\u003Cp\u003EDer schwierige Teil besteht oft darin, die Art und Weise anzugleichen, wie verschiedene Systeme das Geschäft abbilden. Beispielsweise könnte ein CRM eine:n Kund:in als Konto darstellen, während ein E-Commerce-System einzelne Käufer:innen erfasst. Die Integration legt fest, wie diese Datensätze zusammenhängen, welche Quelle für ein bestimmtes Attribut maßgeblich ist und, sofern historische Analysen dies erfordern, wie Änderungen an Geschäftsentitäten erhalten bleiben, anstatt überschrieben zu werden.\u003C/p\u003E\r\n\u003Ch3\u003E3. Daten modellieren\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-modeling/\"\u003EDatenmodellierung\u003C/a\u003E organisiert integrierte Datensätze rund um die Entitäten und Beziehungen, die analysiert werden. Eine Faktentabelle könnte Bestellungen mit einer Zeile pro Transaktion erfassen, während Dimensionen Attribute wie Kund:in, Produkt, Geografie und Zeit beschreiben.\u003C/p\u003E\r\n\u003Cp\u003EDie Granularität ist hier von grundlegender Bedeutung – wenn eine Tabelle eine Zeile pro Bestellung und eine andere eine Zeile pro Bestellposition enthält, wird das Zusammenführen und Aggregieren ohne Berücksichtigung dieses Unterschieds die Berechnung verändern. Auch Kennzahlen benötigen ein definiertes Aggregationsverhalten. Beispielsweise erfordert ein Durchschnitt oder ein Verhältnis oft mehr Sorgfalt, wenn Nutzer:innen es auf verschiedenen Detailebenen analysieren.\u003C/p\u003E\r\n\u003Cp\u003EEin physisches Datenmodell legt diese analytische Struktur fest. Semantische Modelle fügen geschäftsorientierte Konzepte hinzu – benannte Metriken, Dimensionen, Beziehungen und anderen Kontext –, die nachgelagerte Systeme bei der Interpretation verwenden.\u003C/p\u003E\r\n\u003Ch3\u003E4. Analysieren der Daten\u003C/h3\u003E\r\n\u003Cp\u003EMit einem Analysemodell können Nutzer:innen es über wiederkehrende Berichte, Dashboards, SQL, Notebooks und explorative BI-Tools abfragen.\u003C/p\u003E\r\n\u003Cp\u003EDie Analysemethode richtet sich nach der Frage. Die deskriptive Analyse fasst zusammen, was passiert ist. Die diagnostische Analyse untersucht Beziehungen und Einflussfaktoren. Prognosen und andere prädiktive Methoden schätzen ab, was als Nächstes wahrscheinlich passieren wird. BI-Workflows können auch statistische Analysen, Machine Learning und KI integrieren – zum Beispiel, um anomale Änderungen des Transaktionsvolumens zu erkennen, die Bestandsnachfrage zu prognostizieren oder eine narrative Erklärung für eine Leistungsverschiebung zu generieren.\u003C/p\u003E\r\n\u003Cp\u003EEinige Analysen sind stark standardisiert. Ein Finanzteam könnte jeden Monat dieselben Umsatz-, Margen- und Ausgabenkennzahlen anhand zertifizierter Berichte mit fester Berechnungslogik überprüfen. Andere Arbeiten sind explorativ: Ein:e Analyst:in bemerkt einen unerwarteten Umsatzrückgang, segmentiert das Ergebnis nach Regionen, verfolgt den Rückgang bis zu einer bestimmten Produktkategorie und untersucht dann Kunden- oder Vertriebsaktivitäten auf mögliche Erklärungen. Moderne BI-Umgebungen unterstützen zunehmend beide Arten der Analyse und ermöglichen es den Nutzer:innen, zwischen wiederkehrenden Messungen und Untersuchungen zu wechseln, ohne die zugrunde liegende Analysestruktur für jede Frage neu aufbauen zu müssen.\u003C/p\u003E\r\n\u003Ch3\u003E5. Insights liefern und handeln\u003C/h3\u003E\r\n\u003Cp\u003EDie Ergebnisse erreichen die Nutzer:innen über Dashboards, Berichte, Warnmeldungen, eingebettete Analysen und konversationelle Schnittstellen. An diesem Punkt verbindet sich BI wieder mit dem Geschäftsprozess, der die Analyse ausgelöst hat.\u003C/p\u003E\r\n\u003Cp\u003EDie Bereitstellung umfasst auch die Kontrollen rund um das Ergebnis. Teams müssen festlegen, wer Zugriff haben soll, welche Berichte maßgeblich sind, wie Nutzer:innen relevante Analysen entdecken und ob die Informationen für den beabsichtigten Zweck aktuell genug sind. Die Vertriebskanäle variieren entsprechend: Dashboards und geplante Berichte eignen sich gut für wiederkehrende Überprüfungen, Warnmeldungen machen auf Bedingungen aufmerksam, die einen Schwellenwert überschreiten, und konversationelle Schnittstellen unterstützen Folgefragen, wenn Nutzer:innen weitere Untersuchungen anstellen müssen.\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-v2-2ac32f7cf0","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/matillion/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EDie Cloud-Datenintegrationsplattform Matillion nutzt Snowflake Intelligence (jetzt CoWork), um Mitarbeiter:innen über natürlichsprachliche Abfragen in Slack Self-Service-Zugriff auf Daten zu geben, was die Arbeitsbelastung von Analyst:innen um 50 % reduziert. Zusammen haben diese Funktionen Matillion geholfen, die Infrastrukturkosten um etwa 40 % zu senken und gleichzeitig einen durchgängigen KI-Workflow vom Data Engineering bis zur Analyse zu unterstützen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer Story: Matillion"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3313f7ea-d8cd-4364-b9a4-071d951ab7dc/matillion%25403x.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Matillion logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_the-data-and-semantic-foundation-beneath-bi":{"id":"title-v2-98f03e3917","additionalClasses":"anchor-title anchor-title--the-data-and-semantic-foundation-beneath-bi","type":"heading2","lines":["Die datenbezogene und semantische Grundlage von BI"],":type":"snowflake-site/components/title-v2"},"text_the-data-and-semantic-foundation-beneath-bi_0":{"id":"text-39938a3788","text":"\u003Cp\u003EBI-Abfragen werden letztendlich für Tabellen, Spalten und Beziehungen ausgeführt, während Geschäftsanwender:innen Fragen in geschäftlichen Begriffen wie Umsatz, Kundenbindung, Produktleistung und Konversion stellen. Eine zuverlässige Übersetzung erfordert sowohl ein solides analytisches Datenmodell als auch eine explizite Darstellung der geschäftlichen Bedeutung.\u003C/p\u003E\n\u003Cp\u003EDas Datenmodell legt die Struktur der Analyse fest: was eine Zeile darstellt, wie Tabellen zusammenhängen und wie Datensätze kombiniert werden sollten. Granularität und Kardinalität sind besonders wichtig, da ein gültiger Join eine Aggregation dennoch verzerren kann, wenn Tabellen unterschiedliche Detailebenen darstellen.\u003C/p\u003E\n\u003Cp\u003EIn einer Fan-Trap beispielsweise dupliziert ein Join Werte vor der Aggregation. Will Pugh, Principal Software Engineer bei Snowflake, veranschaulicht den Effekt an \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/why-we-need-semantic-views/\"\u003Eeinem Beispiel\u003C/a\u003E: Wenn ein Verkauf über 100 USD mit zwei zugehörigen Produktdatensätzen verknüpft wird, meldet eine gültige SQL-Abfrage einen Umsatz von 200 USD, da der Verkauf zweimal auftritt, bevor er summiert wird.\u003C/p\u003E\n\u003Ch3\u003EGeschäftliche Bedeutung explizit darstellen\u003C/h3\u003E\n\u003Cp\u003EEine semantische Schicht fügt die geschäftsorientierte Struktur hinzu, die Tabellen und Beziehungen allein nicht bieten. Kennzahlen, Dimensionen, Berechnungsregeln, Terminologie und gültige analytische Beziehungen geben nachgelagerten Systemen ein explizites Modell zur Interpretation der Daten.\u003C/p\u003E\n\u003Cp\u003E„Ein physisches Datenbankschema erklärt, wie Daten strukturiert sind, erfasst aber nicht, wie das Unternehmen arbeitet”, sagt Josh Klahr, Head of Product Management for Analytics bei Snowflake. „Regeln wie Umsatzrealisierung, saisonale Anpassungen oder veraltete Randfälle müssen explizit angegeben werden.”\u003C/p\u003E\n\u003Cp\u003EEine Bruttomargen-Kennzahl könnte beispielsweise die genehmigten Umsatz- und Kostenberechnungen identifizieren, ihr Aggregationsverhalten spezifizieren und die Dimensionen festlegen, über die die Metrik gültig ist. Ein BI-Tool, Notebook oder eine natürlichsprachliche Schnittstelle, die dieses Modell abfragt, arbeitet mit diesen Definitionen, anstatt sie aus dem physischen Schema zu rekonstruieren.\u003C/p\u003E\n\u003Ch3\u003ESemantische Definitionen toolübergreifend portierbar machen\u003C/h3\u003E\n\u003Cp\u003EEin gemeinsames semantisches Modell löst jedoch nur einen Teil des Problems, wenn seine Definitionen an ein einziges Tool gebunden sind. In der Vergangenheit wurden semantische Metadaten oft in anbieterspezifischen Modellen codiert, was Teams dazu zwang, Kennzahlen und Beziehungen neu zu erstellen, wenn neue BI-Tools oder Schnittstellen in die Umgebung eingeführt wurden.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/open-semantic-interchanges-specs-finalized/\"\u003EOpen Semantic Interchange\u003C/a\u003E (OSI) löst dieses Portabilitätsproblem. Die Initiative veröffentlichte im Januar 2026 die erste Version ihrer offenen Spezifikation und schuf damit einen Standard für den Austausch semantischer Metadaten zwischen teilnehmenden Plattformen und Tools.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-data-and-semantic-foundation-beneath-bi_0":{"id":"text-a8eb517474","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EHÄUFIGER FALLSTRICK\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EGehen Sie nicht davon aus, dass eine Abfrage korrekt ist, nur weil sie erfolgreich ausgeführt wird. Unterschiede in Granularität, Join-Beziehungen oder Aggregationslogik können plausibel aussehende Ergebnisse liefern, die nicht die beabsichtigte geschäftliche Berechnung widerspiegeln.\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-v2-44a8747ee9","additionalClasses":"anchor-title anchor-title--bi-tools-and-how-they-work-together","type":"heading2","lines":["BI-Tools und wie sie zusammenarbeiten"],":type":"snowflake-site/components/title-v2"},"text_bi-tools-and-how-they-work-together_0":{"id":"text-a92f07b93f","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/tools/\"\u003EBI-Tools\u003C/a\u003E unterscheiden sich darin, wie Nutzer:innen Analysen erstellen, Daten untersuchen, zusammenarbeiten und Ergebnisse präsentieren. Ihre Verbindung zur Datenplattform variiert ebenfalls, und diese architektonischen Entscheidungen wirken sich auf Aktualität, Latenz, Kosten und Governance aus.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ELive-Abfrage:\u003C/b\u003E Bei Live-Abfrage-BI sendet das Tool Abfragen an die zugrunde liegende Analyse-Engine, wenn Nutzer:innen Dashboards öffnen, Filter ändern oder Daten untersuchen. Nutzer:innen arbeiten mit aktuellen Daten entsprechend dem Aktualisierungsrhythmus der Quelle, während die Plattform die daraus resultierende Nebenläufigkeit absorbiert.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIn-Memory:\u003C/b\u003E Ein Extrakt oder In-Memory-Modell erstellt eine separate analytische Darstellung, die das BI-Tool regelmäßig aktualisiert. Dieser Ansatz reduziert die wiederholte Belastung der Quelle und verbessert manchmal die interaktive Leistung, obwohl die Aktualität nun vom Extraktionszeitplan abhängt und eine weitere Kopie verwaltet werden muss.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESelf-Service:\u003C/b\u003E Self-Service-BI gibt Geschäftsanwender:innen mehr Kontrolle über die Untersuchung und Abfrageerstellung. Ein gutes Self-Service-Design verringert das erforderliche technische Wissen: Nutzer:innen wählen verständliche Kennzahlen und Dimensionen aus, während die darunter modellierten Beziehungen einschränken, wie diese Felder kombiniert werden.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEingebettet:\u003C/b\u003E Bei eingebetteter BI oder Embedded Analytics erscheinen Berichte und interaktive Analysen innerhalb einer anderen Anwendung oder eines anderen Workflows. Hier ähneln Nutzungsmuster oft eher dem Anwendungsdatenverkehr als dem internen Reporting, insbesondere wenn kundenorientierte Software viele gleichzeitige Nutzer:innen bedient.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EKI-gesteuerte BI-Schnittstellen\u003C/b\u003E: Natürlichsprachliche Schnittstellen ermöglichen es Nutzer:innen, Fragen in einfacher Sprache zu stellen, Analysen zu generieren und Ergebnisse im Dialog zu untersuchen. Sie stützen sich weiterhin auf dieselben zugrunde liegenden Daten, dieselbe Abfrageinfrastruktur und denselben analytischen Kontext wie andere BI-Erfahrungen.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EDie meisten Unternehmensumgebungen mischen diese Funktionen. Ihre Bewertung erfordert jedoch mehr als einen Funktionsvergleich zwischen BI-Produkten. Teams müssen auch das Abfrageverhalten, die Datenaktualität, das Caching, die Integration in bestehende Modelle und den Workload verstehen, den jede Schnittstelle nachgelagert sendet.\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-f33a29c254","additionalClasses":"anchor-title anchor-title--scaling-bi-and-managing-dashboard-sprawl","type":"heading2","lines":["BI skalieren und Dashboard-Wildwuchs verwalten"],":type":"snowflake-site/components/title-v2"},"text_scaling-bi-and-managing-dashboard-sprawl_0":{"id":"text-1bdc49af1c","text":"\u003Cp\u003EDie BI-Nachfrage ist selten einheitlich. Montags um 9 a.m. Uhr öffnen möglicherweise Hunderte von Personen wiederkehrende Dashboards, während Analyst:innen Ad-hoc-Abfragen starten und eine eingebettete Anwendung weiterhin Kundenanfragen bedient.\u003C/p\u003E\n\u003Cp\u003EDiese Workloads stellen unterschiedliche Anforderungen an die Rechenleistung. Geplante Aktualisierungen sind vorhersehbar und oft ressourcenintensiv. Interaktive Filter bevorzugen eine geringe Latenz. Exploratives SQL variiert stark in der Komplexität. Workload-Isolierung, Skalierung für Nebenläufigkeit, Caching und Aktualisierungsplanung helfen Teams, die interaktive Leistung mit den Rechenkosten in Einklang zu bringen, wenn sich die Nachfrage ändert.\u003C/p\u003E\n\u003Cp\u003EDer Dashboard-Bestand verursacht weitere Kosten. Berichte neigen dazu, sich anzusammeln, wenn Teams vorhandene Assets für neue Zielgruppen oder Anforderungen kopieren und es versäumen, alte, nicht mehr verwendete Assets auszumustern. Letztendlich sehen sich Nutzer:innen mit mehreren ähnlichen Berichten konfrontiert, und Plattformteams pflegen oder aktualisieren weiterhin Assets, die wenig Traffic erhalten.\u003C/p\u003E\n\u003Cp\u003ENutzungstelemetrie kann verwendet werden, um wenig genutzte Dashboards zu identifizieren, zu bestätigen, ob sie noch einem Geschäftsprozess dienen, Eigentümer zuzuweisen und veraltete Versionen auszumustern oder zu archivieren. Die Zertifizierung hilft auch dabei, maßgebliche Berichte von explorativer Arbeit zu unterscheiden.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_defining-and-governing-kpis":{"id":"title-v2-3aab1006ce","additionalClasses":"anchor-title anchor-title--defining-and-governing-kpis","type":"heading2","lines":["KPIs definieren und verwalten"],":type":"snowflake-site/components/title-v2"},"text_defining-and-governing-kpis_0":{"id":"text-ab97ecd3c3","text":"\u003Cp\u003EEin \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/kpi/\"\u003EKey Performance Indicator\u003C/a\u003E (KPI) verwandelt ein Geschäftsziel in einen messbaren Wert. Damit jemand anderes diesen Wert reproduzieren kann, benötigt die Definition mehr als einen Namen und eine Formel.\u003C/p\u003E\r\n\u003Cp\u003EEin Produktions-KPI spezifiziert typischerweise:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EGeschäftliche Bedeutung und Verwendungszweck\u003C/li\u003E\r\n\u003Cli\u003EBerechnungs- und Aggregationslogik\u003C/li\u003E\r\n\u003Cli\u003EGranularität\u003C/li\u003E\r\n\u003Cli\u003EZeitfenster oder Zeitverhalten\u003C/li\u003E\r\n\u003Cli\u003EAnwendbare Dimensionen und Filter\u003C/li\u003E\r\n\u003Cli\u003EMaßgebliche Quelle\u003C/li\u003E\r\n\u003Cli\u003EGeschäftliche und technische Eigentümer\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EBetrachten Sie die Kundenbindung. Die Definition muss die Ausgangspopulation identifizieren, was als gebunden gilt und den Zeitraum, über den die Bindung gemessen wird. Diese Elemente sollten konsistent bleiben, wenn die Metrik an anderer Stelle wiederverwendet wird.\u003C/p\u003E\r\n\u003Cp\u003EÄnderungen müssen verfolgt und verwaltet werden. Wenn die Finanzabteilung beispielsweise die Behandlung von Rückerstattungen im realisierten Umsatz ändert, müssen Teams entscheiden, wann die Überarbeitung in Kraft tritt, ob historische Ergebnisse neu berechnet werden und welche nachgelagerten Berichte von der alten Definition abhängen. Versionierung, Lineage und ein Genehmigungsprozess machen diese Änderungen nachvollziehbar.\u003C/p\u003E\r\n\u003Cp\u003EBusiness-Stakeholder sind in der Regel dafür verantwortlich, was ein wichtiger KPI bedeutet, während Daten- und Analyseteams die genehmigte Logik implementieren und pflegen. Diese Verantwortlichkeiten explizit zu halten, hilft der Metrik-Governance, Änderungen bei Personen, Tools und Organisationsstrukturen zu überstehen.\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-22caf0bd1d","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDokumentieren Sie mehr als nur die Formel eines KPIs. Erfassen Sie Granularität, Filter, Zeitlogik, maßgebliche Quelle und Eigentümer, damit ein anderes Team dasselbe Ergebnis reproduzieren kann – und versteht, was sich ändert, wenn die Definition aktualisiert wird.\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-v2-92d460d393","additionalClasses":"anchor-title anchor-title--bi-strategy-and-benefits","type":"heading2","lines":["BI-Strategie und Vorteile"],":type":"snowflake-site/components/title-v2"},"text_bi-strategy-and-benefits_0":{"id":"text-e6f540de48","text":"\u003Cp\u003EEine \u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/strategy/\"\u003EBI-Strategie\u003C/a\u003E definiert, wie ein Unternehmen Daten nutzen wird, um wiederkehrende Entscheidungen zu unterstützen, welche analytischen Fähigkeiten diese Entscheidungen erfordern und wer für die dahinter stehenden Assets verantwortlich ist. Die Tool-Auswahl passt in diese Strategie, aber erst, nachdem die Teams die Fragen, Nutzer:innen und Workload-Anforderungen verstanden haben, die die BI-Umgebung unterstützen muss.\u003C/p\u003E\n\u003Ch3\u003EBeginnen Sie mit den Entscheidungen und Anforderungen\u003C/h3\u003E\n\u003Cp\u003EEine gute BI-Strategie arbeitet rückwärts von den Entscheidungen, die Menschen treffen müssen. Welche Fragen wiederholen sich oft genug, um sie zu standardisieren? Wer braucht die Antwort und wie schnell? Welche Kennzahlen erfordern unternehmensweite Definitionen, während andere spezifisch für ein Team oder einen Workflow bleiben können?\u003C/p\u003E\n\u003Cp\u003EDiese Entscheidungen prägen das technische Design. Das Executive Reporting priorisiert möglicherweise historische Konsistenz und zertifizierte Metriken, während operative BI aktuellere Daten und Abfragen mit geringerer Latenz erfordert. Self-Service-Nutzer:innen benötigen genügend Flexibilität, um Daten zu untersuchen, ohne die Governance zu umgehen, und Embedded Analytics führt Leistungs- und Zugriffsanforderungen im Anwendungsmaßstab ein.\u003C/p\u003E\n\u003Cp\u003EVon dort aus können Teams festlegen, wie Daten integriert und modelliert werden, wo die Geschäftslogik gepflegt wird, welche Workloads Live-Abfragen oder Extrakte verwenden, wie der Zugriff geregelt wird und wie Leistung und Kosten bei zunehmender Akzeptanz verwaltet werden.\u003C/p\u003E\n\u003Ch3\u003EMessen Sie, ob BI funktioniert\u003C/h3\u003E\n\u003Cp\u003ETeams müssen wissen, ob BI die Entscheidungsfindung verbessert. „Wenn der Erfolg an Dashboard-Aufrufen, Abfragevolumen oder aktiven Plattformnutzer:innen gemessen wird, messen Sie Aktivität statt Wert”, sagt Klahr.\u003C/p\u003E\n\u003Cp\u003EZu den aussagekräftigen Kennzahlen gehören die Akzeptanz bei den vorgesehenen Nutzer:innen, die Zeit, die zur Beantwortung wiederkehrender Fragen benötigt wird, die Abfrageleistung, die Duplizierung von Dashboards oder analytischen Modellen und der Aufwand, den Teams für den Abgleich widersprüchlicher Ergebnisse betreiben. Bei Self-Service-Programmen können Teams auch verfolgen, wie oft Nutzer:innen Fragen unabhängig beantworten, anstatt sich für Routineanfragen an Analyst:innen zu wenden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_bi-strategy-and-benefits_0":{"id":"quote-item-fd3a7e5eb3","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"If success is measured by dashboard views, query volume or active platform users, you are measuring activity rather than value.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_bi-strategy-and-benefits_1":{"id":"text-881b95081f","text":"\u003Cp\u003EVertrauen hat einen direkten Einfluss auf die Akzeptanz. In einer \u003Ca href=\"https://technologyleadersclub.com/wp-content/uploads/2024/10/report_precisely-lebow-report-2024.pdf\" target=\"_blank\"\u003EStudie von Precisely und dem LeBow College of Business der Drexel University\u003C/a\u003E gaben 76 % der Unternehmen an, dass datengesteuerte Entscheidungsfindung ein Hauptziel ihrer Datenprogramme sei, während 67 % berichteten, dass sie ihren Daten nicht vollständig vertrauen. Diese Lücke erklärt, warum die BI-Strategie zunehmend Datenqualität, Governance und Ownership umfasst.\u003C/p\u003E\n\u003Ch3\u003EDie Vorteile von Business Intelligence\u003C/h3\u003E\n\u003Cp\u003EDie Vorteile von BI ergeben sich aus der Strategie. Wiederverwendbare Analysemodelle reduzieren den Aufwand, der mit der wiederholten Implementierung derselben Logik verbunden ist. Gemeinsam genutzte Metriken verkürzen die Abstimmungszyklen. Self-Service-Zugriff bietet einigen Nutzer:innen einen schnelleren Weg zu Routineantworten, während zertifizierte Berichte eine stabile Referenz für wiederkehrende Entscheidungen darstellen.\u003C/p\u003E\n\u003Cp\u003EAuf Plattformebene helfen skalierbare Rechenleistung und Workload-Management den BI-Umgebungen, eine breitere Nutzung aufzufangen, ohne dass Teams jeden Workload für Spitzenlasten bereitstellen müssen. Ein besseres Lifecycle-Management begrenzt zudem die Kosten und die Verwirrung, die durch veraltete Berichte, doppelte Dashboards und Analyse-Assets entstehen, die keinen aktiven Geschäftsprozess mehr unterstützen.\u003C/p\u003E\n\u003Cp\u003EDie stärksten BI-Strategien verknüpfen Verbesserungen mit spezifischen Entscheidungen und Workflows, anstatt die Dashboard-Akzeptanz allein als Wertnachweis zu betrachten.\u003C/p\u003E","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-d11e018bcd","additionalClasses":"anchor-title anchor-title--modern-bi-augmented-analytics-conversational-bi-and-agentic-bi","type":"heading2","lines":["Moderne BI: Augmented Analytics, Conversational BI und Agentic BI"],":type":"snowflake-site/components/title-v2"},"text_modern-bi-augmented-analytics-conversational-bi-and-agentic-bi_0":{"id":"text-1ccae993cb","text":"\u003Cp\u003EKI verlagert einen Teil der BI von der expliziten Abfrageerstellung zur Interpretation. Der Grad der Interpretation variiert bei verschiedenen Ansätzen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EAugmented Analytics\u003C/strong\u003E wendet KI und maschinelles Lernen innerhalb eines Analyse-Workflows an, einschließlich Anomalieerkennung, Prognoseunterstützung, Visualisierungsvorschlägen und Insight-Narration.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConversational BI\u003C/strong\u003E akzeptiert Fragen in natürlicher Sprache, übersetzt sie in analytische Operationen und unterstützt Folgefragen, wenn Nutzer:innen ein Ergebnis untersuchen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAgentic BI\u003C/strong\u003E erweitert KI-gesteuerte Business Analytics über mehrere Schritte hinweg, wie z. B. das Lokalisieren relevanter Daten, das Ausführen von Analysen, das Untersuchen einer Anomalie und das Zusammenstellen von Ergebnissen.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EInterpretation der Geschäftsfrage\u003C/h3\u003E\n\u003Cp\u003EBevor ein System SQL generiert, muss es ermitteln, was der:die Nutzer:in fragt. Angenommen, jemand fragt: „Welche unserer besten Kund:innen geben in diesem Quartal weniger aus?“ Das System muss die entsprechende Kundenentität identifizieren, bestimmen, was „am besten“ bedeutet, die genehmigte Ausgabenkennzahl auswählen, das relevante Quartal auflösen, gültige Beziehungen zwischen den erforderlichen Daten identifizieren und entscheiden, wie aktuelle und vorherige Aktivitäten verglichen werden sollen. Syntaktisch korrektes SQL adressiert nur die finale Übersetzung dieser Entscheidungen in eine Abfrage.\u003C/p\u003E\n\u003Cp\u003EProduktionssysteme begegnen dieser Mehrdeutigkeit, indem sie mehr Kontext über die Daten und das Unternehmen bereitstellen. Ein semantisches Modell liefert dem System genehmigte Kennzahlen, Dimensionen und Beziehungen, anstatt es diese allein aus Tabellen- und Spaltennamen ableiten zu lassen. Geschäftsbeschreibungen und Synonyme verbinden Begriffe, die Nutzer:innen tatsächlich verwenden – wie \u003Cem\u003EBuchungen\u003C/em\u003E, \u003Cem\u003Eaktives Konto\u003C/em\u003E oder \u003Cem\u003EUnternehmenskunde\u003C/em\u003E – mit den entsprechenden Objekten im Modell, während Beispielwerte helfen zu klären, wie Kategorien und Entitäten in den zugrunde liegenden Daten erscheinen.\u003C/p\u003E\n\u003Ch3\u003EFundierung und Evaluierung KI-generierter Analysen\u003C/h3\u003E\n\u003Cp\u003EFür wiederkehrende oder besonders wichtige Fragen verknüpfen verifizierte Abfragen eine natürlichsprachliche Anfrage mit genehmigtem SQL und zeigen dem System ein als gut bekanntes Analysemuster. Benutzerdefinierte Anweisungen können Regeln hinzufügen, die durch das Schema selbst schwer auszudrücken sind, z. B. welches Datumsfeld für eine bestimmte Analyse verwendet werden soll oder wie ein Unternehmen einen Berichtszeitraum definiert.\u003C/p\u003E\n\u003Cp\u003EDiese Kontrollen schränken den Bereich der dem System zur Verfügung stehenden Interpretationen ein, beseitigen jedoch nicht die Notwendigkeit einer Evaluierung. Teams sollten repräsentative Geschäftsfragen anhand bekanntermaßen guter Ergebnisse testen, einschließlich mehrdeutiger Formulierungen und Anfragen, die mehrere Metriken, Filter oder Beziehungen erfordern. Wenn ein Fehler auftritt, müssen Teams in der Lage sein zu identifizieren, wo die Interpretation abwich – Metrikauswahl, Entitätsauflösung, Join-Pfad, Filter, Zeitlogik oder eine andere analytische Entscheidung –, damit der relevante Kontext oder das Modell verbessert werden kann.\u003C/p\u003E\n\u003Cp\u003EBei agentischen Workflows erstreckt sich die Evaluierung auch auf Zwischenschritte, da eine falsche Entscheidung früh in einer mehrstufigen Analyse alles Folgende beeinflussen kann.\u003C/p\u003E\n\u003Ch3\u003ENutzung der richtigen Schnittstelle für die analytische Aufgabe\u003C/h3\u003E\n\u003Cp\u003EKI-gesteuerte Schnittstellen erweitern das Spektrum der Fragen, die Nutzer:innen untersuchen können, ohne dass ein vordefinierter Bericht erforderlich ist. Dashboards funktionieren weiterhin gut für wiederkehrende Kennzahlen, die Teams in einem konsistenten Format überprüfen, während Conversational BI Fragen und Folgeuntersuchungen außerhalb dieser vordefinierten Pfade unterstützt.\u003C/p\u003E\n\u003Cp\u003EUnternehmen benötigen zunehmend beide Erfahrungen. Ein Führungsteam könnte jede Woche ein zertifiziertes Umsatz-Dashboard überwachen und dann eine konversationelle Schnittstelle nutzen, um einen unerwarteten regionalen Rückgang zu untersuchen. Die Interaktion ändert sich zwischen diesen Aufgaben, aber die verwalteten Daten und der analytische Kontext darunter sorgen für Konsistenz und Kontinuität.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bi-roles-and-teams":{"id":"title-v2-ad0fecc18f","additionalClasses":"anchor-title anchor-title--bi-roles-and-teams","type":"heading2","lines":["BI-Rollen und -Teams"],":type":"snowflake-site/components/title-v2"},"text_bi-roles-and-teams_0":{"id":"text-5f44b1de42","text":"\u003Cp\u003EBI-Verantwortlichkeiten erstrecken sich in der Regel über mehrere Disziplinen, obwohl die Titel stark variieren.\u003C/p\u003E\n\u003Cp\u003EEin:e \u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/roles/bi-analyst/\"\u003EBI-Analyst:in\u003C/a\u003E arbeitet am engsten an Geschäftsfragen und nutzt Daten, um die Leistung zu untersuchen und wiederkehrende oder explorative Analysen zu erstellen. Ein:e Data oder Analytics Engineer bereitet die Strukturen unter dieser Arbeit vor, einschließlich Transformationen, Datenqualitätsprüfungen und Analysemodellen. Ein:e BI-Entwickler:in konzentriert sich auf semantische Modelle, Dashboards, Berichte und eingebettete Bereitstellungserfahrungen.\u003C/p\u003E\n\u003Cp\u003EBusiness-Stakeholder behalten eine wichtige Rolle als Eigentümer:innen der Domänenbedeutung. Ein:e Finanzleiter:in bestimmt beispielsweise, wie ein finanzieller KPI interpretiert werden soll; das Datenteam übersetzt diese genehmigte Definition in eine analytische Implementierung.\u003C/p\u003E\n\u003Cp\u003EGrößere Unternehmen verteilen diese Verantwortlichkeiten oft auf Plattform- und Domänenteams, während kleinere Unternehmen mehrere in einer Rolle kombinieren. Unabhängig von der Struktur hilft eine explizite Eigentümerschaft von Pipelines, Modellen, Metriken und Produktionsberichten zu verhindern, dass Analyse-Assets die Personen überdauern, die sie verstehen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/roles/\"\u003EWeitere Informationen zu Analytics- und BI-Rollen \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bi-by-industry-and-business-function":{"id":"title-v2-1caf6102cf","additionalClasses":"anchor-title anchor-title--bi-by-industry-and-business-function","type":"heading2","lines":["BI nach Branche und Geschäftsfunktion"],":type":"snowflake-site/components/title-v2"},"text_bi-by-industry-and-business-function_0":{"id":"text-57c80dbf70","text":"\u003Cp\u003EDie grundlegende BI-Architektur erstreckt sich über Branchen und Funktionen hinweg, während sich die Anforderungen mit der Entscheidung ändern.\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EBereich\u003C/th\u003E\n\u003Cth\u003EBeispielhafte BI-Frage\u003C/th\u003E\n\u003Cth\u003EWichtige Anforderung\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFinanzen\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWie verändern sich Umsatz, Marge oder Ausgaben?\u003C/td\u003E\n\u003Ctd\u003EAbstimmung und konsistentes historisches Reporting\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMarketing\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWelche Aktivitäten tragen zu Akquise, Pipeline oder Umsatz bei?\u003C/td\u003E\n\u003Ctd\u003EQuellübergreifende Identitäts- und Attributionsregeln\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EVertrieb (Sales)\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWie haben sich Pipeline oder Konversion im Laufe der Zeit verändert?\u003C/td\u003E\n\u003Ctd\u003EHistorische Snapshots des sich ändernden CRM-Status\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EOperations\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWo verschlechtern sich Bestand, Fulfillment oder Service-Level?\u003C/td\u003E\n\u003Ctd\u003EAngemessene Aktualität und operative Details\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EHandel\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWie verändert sich die Leistung über Kanäle, Produkte und Standorte hinweg?\u003C/td\u003E\n\u003Ctd\u003EIntegration von digitalen und physischen Transaktionsdaten\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERegulierte Branchen\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EWie wurde dieses Ergebnis erzielt und wer hatte Zugriff auf die Daten?\u003C/td\u003E\n\u003Ctd\u003ELineage, Auditierbarkeit, Zugriffskontrollen und Richtliniendurchsetzung\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EEine einzelne Geschäftsfrage überschreitet oft diese Grenzen. Die Untersuchung eines Umsatzrückgangs könnte beispielsweise Finanzkennzahlen, die Historie der Vertriebspipeline und Marketing-Akquisedaten erfordern. Für eine genaue Antwort benötigen Teams genügend gemeinsame Struktur, um diese Analysen zu verbinden und gleichzeitig die Regeln beizubehalten, die jede Domäne erfordert.\u003C/p\u003E\n\u003Cp\u003EWeitere Informationen: \u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/use-cases/\"\u003EBI-Beispiele und Anwendungsfälle \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_business-intelligence-on-snowflake":{"id":"title-v2-7f14127a1c","additionalClasses":"anchor-title anchor-title--business-intelligence-on-snowflake","type":"heading2","lines":["Business Intelligence auf Snowflake"],":type":"snowflake-site/components/title-v2"},"text_business-intelligence-on-snowflake_0":{"id":"text-60a6a1c0bd","text":"\u003Cp\u003ESnowflake bringt die Daten, den semantischen Kontext, die Governance und die Rechenleistung, die BI unterstützen, in eine gemeinsame Grundlage ein und ermöglicht es Unternehmen gleichzeitig, die BI-Tools und -Schnittstellen zu nutzen, die zu ihren Workflows passen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/horizon-context/\"\u003ESnowflake Horizon Context\u003C/a\u003E bietet einen verwalteten Geschäftskontext für BI und KI. Semantic Views repräsentieren Metriken, Dimensionen, Beziehungen und Geschäftslogik als Snowflake-Objekte, während Semantic View Autopilot vorhandenes SQL und unterstützte BI-Modelle nutzt, um diese Ansichten aus etablierten Analysemustern zu generieren. Open Semantic Interchange (OSI) hilft dabei, semantische Metadaten über teilnehmende Tools hinweg zu erweitern, sodass Unternehmen verwaltete Definitionen in verschiedenen BI-Umgebungen wiederverwenden können.\u003C/p\u003E\r\n\u003Cp\u003EAuf der Serving-Ebene unterstützt die elastische Compute-Architektur von Snowflake BI-Workloads mit unterschiedlichen Anforderungen an Parallelität und Leistung. Separate virtuelle Warehouses bieten Workload-Isolierung für wiederkehrendes Reporting, Ad-hoc-Analysen und andere Workloads, während \u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E Zugriffskontrollen, Discovery, Lineage und Datenqualität für die zugrunde liegenden Daten bereitstellt.\u003C/p\u003E\r\n\u003Cp\u003EFür KI-gestützte BI übersetzt \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E natürlichsprachliche Fragen in SQL, wobei die in Semantic Views dargestellten Geschäftsdefinitionen und Beziehungen verwendet werden. Diese Ansichten sind auch über Standard-SQL und semantisches SQL zugänglich, was BI-Tools, Analyst:innen und KI-Systemen mehrere Möglichkeiten bietet, mit denselben modellierten Daten zu arbeiten.\u003C/p\u003E\r\n\u003Cp\u003EZusammen stellen diese Funktionen verwaltete Daten, analytische Logik, semantischen Kontext und skalierbare Abfrageverarbeitung unter die Schnittstelle, über die ein:e Nutzer:in eine Frage stellt.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-bi-for-changing-analytical-experiences":{"id":"title-v2-81426dd6a0","additionalClasses":"anchor-title anchor-title--build-bi-for-changing-analytical-experiences","type":"heading2","lines":["BI für sich wandelnde Analyseerlebnisse entwickeln"],":type":"snowflake-site/components/title-v2"},"text_build-bi-for-changing-analytical-experiences_0":{"id":"text-9eac56a5ef","text":"\u003Cp\u003EDie Art und Weise, wie Menschen mit Geschäftsdaten interagieren, wird sich weiter ändern. Dashboards bleiben nützlich, während Self-Service-Tools, Embedded Analytics, konversationelle Schnittstellen und Agenten den Nutzer:innen zusätzliche Möglichkeiten bieten, dieselben zugrunde liegenden Informationen zu erkunden. Diese Erfahrungen benötigen eine stabile Grundlage, auf der sie aufbauen können: verwaltete Daten, explizite analytische Beziehungen, gemeinsame Geschäftsdefinitionen und eine Infrastruktur, die die erforderliche Aktualität, Parallelität und Leistung unterstützt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-bi-for-changing-analytical-experiences_0":{"id":"text-7bbf30ff4b","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDa sich BI über Dashboards, Self-Service-Tools, eingebettete Anwendungen und KI-Schnittstellen hinweg ausdehnt, hängt die Konsistenz zunehmend von der darunter liegenden Grundlage ab: verwaltete Daten, gemeinsamer analytischer Kontext und eine für den Workload geeignete Infrastruktur.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},"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",":itemsOrder":["container"],":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-eacb7fe315",":type":"snowflake-site/components/container",":itemsOrder":["text_943981956_copy_","text_copy"],":items":{"text_943981956_copy_":{"id":"text-514d9a478f","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn diesem Leitfaden\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-b261a1e564","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-business-intelligence\"\u003EWas ist Business Intelligence?\u003C/li\u003E\u003Cli data-anchor=\"how-business-intelligence-works\"\u003EWie Business Intelligence funktioniert\u003C/li\u003E\u003Cli data-anchor=\"the-data-and-semantic-foundation-beneath-bi\"\u003EDie datenbezogene und semantische Grundlage von BI\u003C/li\u003E\u003Cli data-anchor=\"bi-tools-and-how-they-work-together\"\u003EBI-Tools und wie sie zusammenarbeiten\u003C/li\u003E\u003Cli data-anchor=\"scaling-bi-and-managing-dashboard-sprawl\"\u003EBI skalieren und Dashboard-Wildwuchs verwalten\u003C/li\u003E\u003Cli data-anchor=\"defining-and-governing-kpis\"\u003EKPIs definieren und verwalten\u003C/li\u003E\u003Cli data-anchor=\"bi-strategy-and-benefits\"\u003EBI-Strategie und Vorteile\u003C/li\u003E\u003Cli data-anchor=\"modern-bi-augmented-analytics-conversational-bi-and-agentic-bi\"\u003EModerne BI: Augmented Analytics, Conversational BI und Agentic BI\u003C/li\u003E\u003Cli data-anchor=\"bi-roles-and-teams\"\u003EBI-Rollen und -Teams\u003C/li\u003E\u003Cli data-anchor=\"bi-by-industry-and-business-function\"\u003EBI nach Branche und Geschäftsfunktion\u003C/li\u003E\u003Cli data-anchor=\"business-intelligence-on-snowflake\"\u003EBusiness Intelligence auf Snowflake\u003C/li\u003E\u003Cli data-anchor=\"build-bi-for-changing-analytical-experiences\"\u003EBI für sich wandelnde Analyseerlebnisse entwickeln\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"}},"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false},"flexible_column_cont_1786318617":{"id":"flexible-column-container-36e05b6b7a","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",":itemsOrder":["title_v2_copy","text_copy"],":items":{"title_v2_copy":{"id":"title-v2-fd233c8ae5","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Häufig gestellte 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