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Erfahren Sie, wie semantische Modelle, Abfragearchitektur, Governance und Portabilität das Zusammenspiel von BI-Tools in einer integrierten Analytics-Umgebung prägen.\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 aem-GridColumn--default--12"},"id":"container-8c12a8b5f8",":type":"snowflake-site/components/container",":itemsOrder":["content_chip","content_chip_copy"],":items":{"content_chip":{"id":"content-chip-baf961104d","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie 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border-top","layout":"SIMPLE","id":"container-acb1974346",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["text_894059747","text"],":items":{"text_894059747":{"id":"text-059d1be85d","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EBusiness-Intelligence-Themen:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-c2f362bf43","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/roles/\"\u003ERollen in Analytics und BI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/use-cases/\"\u003EBI-Beispiele und Anwendungsfälle\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/strategy/\"\u003EBI-Strategie\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/kpi/\"\u003EKPIs\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/okr/\"\u003EOKR\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}}},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-583e62130c","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",":itemsOrder":["callout__0","text__0","title_what-are-business-intelligence-tools","text_what-are-business-intelligence-tools_0","yt_what-are-business-intelligence-tools_0","title_six-categories-of-business-intelligence-tools","text_six-categories-of-business-intelligence-tools_0","quote_six-categories-of-business-intelligence-tools_0","text_six-categories-of-business-intelligence-tools_1","card_v2_six-categories-of-business-intelligence-tools_0","title_what-actually-differentiates-bi-tools-now-that-features-have-converged","text_what-actually-differentiates-bi-tools-now-that-features-have-converged_0","title_what-happens-to-metric-definitions-when-bi-tools-change","text_what-happens-to-metric-definitions-when-bi-tools-change_0","callout_what-happens-to-metric-definitions-when-bi-tools-change_0","title_how-to-evaluate-a-business-intelligence-tool","text_how-to-evaluate-a-business-intelligence-tool_0","callout_how-to-evaluate-a-business-intelligence-tool_0","title_how-business-intelligence-tools-work-with-snowflake","text_how-business-intelligence-tools-work-with-snowflake_0","callout_how-business-intelligence-tools-work-with-snowflake_0"],":items":{"callout__0":{"id":"text-748d9384b5","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EBI-TOOLS DEFINIERT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EEin Business-Intelligence-Tool (BI-Tool) ist die anwenderorientierte Softwareschicht, über die Nutzer:innen die Geschäftsleistung überwachen, Fragen untersuchen und analytische Ergebnisse aus Unternehmensdaten konsumieren.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-c47735589f","text":"\u003Cp\u003EWenn Sie heute einen Vergleich von BI-Tools öffnen, sieht die Liste überraschend einheitlich aus. Dashboards, geplantes Reporting, mobiler Zugriff, Self-Service-Analysen und Abfragen in natürlicher Sprache sind heute in weiten Teilen des Marktes Standard, sodass Teams bei der Evaluierung von Tools oft nur eine Funktionsmatrix voller Häkchen vorfinden.\u003C/p\u003E\r\n\u003Cp\u003EDie Unterschiede zeigen sich oft weiter unten im Stack – Unterschiede, die sich darauf auswirken, wie sich ein Tool verhält, sobald es mit Ihren Daten verbunden ist. Ein Tool speichert beispielsweise Metriken und Geschäftslogik in seinem eigenen semantischen Modell, während ein anderes Definitionen liest, die in einer gemeinsam genutzten Schicht gepflegt werden. Einige Tools fragen verwaltete Daten direkt ab, andere arbeiten mit Extrakten, die über eigene Aktualisierungspläne und Berechtigungen verfügen. Diese grundlegenden Unterschiede wirken sich auf die Konsistenz aus und letztlich darauf, wie viel Aufwand entsteht, wenn das Unternehmen eine weitere BI-Schnittstelle hinzufügt oder das Tool wechselt.\u003C/p\u003E\r\n\u003Cp\u003ENeben der Benutzeroberfläche und der User Experience müssen Teams prüfen, wo die Metriklogik angesiedelt ist, wie Governance-Richtlinien durchgesetzt werden, welche Abhängigkeiten das Tool schafft und wie einfach Definitionen an anderer Stelle wiederverwendet werden können. Diese Faktoren sagen viel mehr darüber aus, wie die \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/\"\u003EBusiness-Intelligence\u003C/a\u003E-Umgebung im Laufe der Zeit funktionieren wird, als eine Reihe von Häkchen bei den Funktionen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-business-intelligence-tools":{"id":"title-v2-71a23c63ec","additionalClasses":"anchor-title anchor-title--what-are-business-intelligence-tools","type":"heading2","lines":["Was sind Business-Intelligence-Tools?"],":type":"snowflake-site/components/title-v2"},"text_what-are-business-intelligence-tools_0":{"id":"text-86d4f84e91","text":"\u003Cp\u003EBusiness-Intelligence-Tools sind Softwareanwendungen, mit denen Nutzer:innen Geschäftsdaten abfragen, visualisieren, analysieren und teilen. Je nach Produkt und Zielgruppe können sie Dashboards, Ad-hoc-Analysen, Self-Service-Analytics, eingebettetes Reporting und natürlichsprachliche Schnittstellen umfassen. Die meisten BI-Tools befinden sich zwischen den Geschäftsanwender:innen und einer zugrunde liegenden Datenumgebung, wie einem Data Warehouse, Data Lake oder einer Datenplattform.\u003C/p\u003E\n\u003Cp\u003EBI-Tools unterscheiden sich zudem grundlegend darin, wie sie sich an die Datenbasis, semantische Modelle und die darunterliegenden Governance-Kontrollen anbinden. Diese architektonischen Entscheidungen beeinflussen, wie gut mehrere analytische Schnittstellen zusammenarbeiten.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EErfahren Sie, wie das Unternehmen für saubere Wohnenergie Sunrun seine Analytics auf Snowflake zentralisiert und Self-Service-BI im gesamten Unternehmen skaliert hat:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-are-business-intelligence-tools_0":{"id":"embed-df1bc8ffa1","youtubeVideoId":"7gVRmTkb6b8","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_six-categories-of-business-intelligence-tools":{"id":"title-v2-d57dd2bc47","additionalClasses":"anchor-title anchor-title--six-categories-of-business-intelligence-tools","type":"heading2","lines":["Sechs Kategorien von Business-Intelligence-Tools"],":type":"snowflake-site/components/title-v2"},"text_six-categories-of-business-intelligence-tools_0":{"id":"text-0eb1b005a5","text":"\u003Cp\u003EBusiness-Intelligence-Tools lassen sich in der Regel in sechs breite Kategorien einteilen, basierend auf der Art der analytischen Arbeit, die sie unterstützen, und den Nutzer:innen, die sie primär bedienen. Manche Softwareprodukte decken mehrere Kategorien gleichzeitig ab, und Großunternehmen setzen häufig verschiedene Tool-Typen kombiniert ein. Die isolierte Betrachtung der Kategorien ist jedoch nützlich, um die Rolle zu verstehen, die jedes Tool in einer integrierten BI-Umgebung spielt.\u003C/p\u003E\r\n\u003Ch3\u003EVisualisierungszentrierte BI-Tools\u003C/h3\u003E\r\n\u003Cp\u003EVisualisierungszentrierte Tools legen ihren Schwerpunkt auf die interaktive Datenexploration, die Erstellung von Dashboards und das visuelle Diagrammdesign. Sie eignen sich für Analyst:innen und Geschäftsteams, deren Fragen sich häufig ändern, insbesondere wenn Nutzer:innen Daten über Dimensionen hinweg aufschlüsseln, Anomalien untersuchen und visuelle Erklärungen zusammenstellen müssen, ohne jede Abfrage von Grund auf neu zu schreiben.\u003C/p\u003E\r\n\u003Ch3\u003EEnterprise-Reporting-Plattformen\u003C/h3\u003E\r\n\u003Cp\u003EEnterprise-Reporting-Plattformen priorisieren modellierte, wiederholbare Ausgaben: geplante Berichte, kontrollierte Verteilung und standardisiertes Reporting für große Nutzergruppen. In diesen Umgebungen haben Konsistenz und betriebliche Zuverlässigkeit in der Regel mehr Gewicht als explorative Flexibilität.\u003C/p\u003E\r\n\u003Ch3\u003EEingebettete Analytics-Tools\u003C/h3\u003E\r\n\u003Cp\u003EEingebettete Analytics integriert Dashboards, Metriken und andere analytische Funktionen direkt in eine Anwendung oder ein Portal, sodass Nutzer:innen relevante Daten analysieren können, ohne in eine separate BI-Umgebung wechseln zu müssen. Häufige Beispiele sind kundenorientierte Nutzungs-Dashboards, Partner-Reporting und operative Analytics, die in eine Geschäftsanwendung integriert sind.\u003C/p\u003E\r\n\u003Ch3\u003ENotebook- und Code-First-Analytics-Tools\u003C/h3\u003E\r\n\u003Cp\u003ENotebook- und Code-First-Umgebungen dienen Analyst:innen, Data Scientists und Engineers, die direkt in SQL, Python oder anderen Programmiersprachen arbeiten, um Daten zu erkunden, Analysen zu erstellen und wiederholbare Workflows zu schaffen. Sie sind besonders nützlich, wenn Teams mehr Kontrolle über die analytische Logik, Reproduzierbarkeit und Integration in codebasierte Workflows benötigen. Der Kompromiss ist die Zugänglichkeit: Eine hochflexible Code-First-Umgebung nützt Geschäftsanwender:innen wenig, die einen wiederkehrenden \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/kpi/\"\u003EKPI\u003C/a\u003E benötigen, ohne einen Programmier-Workflow erlernen zu müssen.\u003C/p\u003E\r\n\u003Ch3\u003ETabellenkalkulationsnative BI-Tools\u003C/h3\u003E\r\n\u003Cp\u003ETabellenkalkulationsnative Tools bringen verwaltete oder zentralisierte Daten in eine Benutzeroberfläche, die vielen Finanz-, Betriebs- und Geschäftsteams bereits vertraut ist. Diese Vertrautheit hat einen praktischen Wert. Das technisch stärkste analytische System hängt immer noch davon ab, dass Menschen es nutzen, und die Akzeptanz steigt oft, wenn ein Tool in einen etablierten Workflow passt. Tabellenkalkulationsnative Analytics benötigen weiterhin Governance und Definitionsmanagement, insbesondere wenn lokale Formeln Logik reproduzieren, die bereits an anderer Stelle existiert.\u003C/p\u003E\r\n\u003Ch3\u003EKonversationelle und agentenbasierte BI-Tools\u003C/h3\u003E\r\n\u003Cp\u003EKonversationelle Analytics akzeptiert Fragen in natürlicher Sprache und liefert Antworten, Visualisierungen oder generierte Abfragen. KI-gestützte Analytics und Text-to-SQL haben diese Kategorie schnell erweitert, während \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EAgenten\u003C/a\u003E zunehmend analytische Fragen mit zusätzlichen Aktionen oder Workflows kombinieren. Es ist wichtig zu beachten, dass eine generierte Antwort die Qualität der Definitionen, Beziehungen und des Kontexts erbt, die dem Modell zur Verfügung gestellt werden.\u003C/p\u003E\r\n\u003Cp\u003EDiese hohe Messlatte für Genauigkeit verändert die Anforderungen der Teams an das zugrunde liegende Datenmodell. Josh Klahr, Head of Product Management for Analytics bei Snowflake, erklärt es so: „‚Weitgehend genaue‘ Datenmodelle zerstören das Vertrauen schnell. Ein:e Nutzer:in kann vielleicht eine verwirrende Dashboard-Spalte umgehen, aber wenn ein KI-System definitiv eine falsche Antwort liefert, sinkt das Vertrauen in die analytische Erfahrung schnell.“\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_six-categories-of-business-intelligence-tools_0":{"id":"quote-item-c0e65a40f1","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"Weitgehend genaue‘ Datenmodelle zerstören das Vertrauen schnell. Ein:e Nutzer:in kann vielleicht eine verwirrende Dashboard-Spalte umgehen, aber wenn ein KI-System definitiv eine falsche Antwort liefert, sinkt das Vertrauen in die analytische Erfahrung schnell.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_six-categories-of-business-intelligence-tools_1":{"id":"text-3539b432b5","text":"\u003Cp\u003EGroße Unternehmen nutzen oft Tools aus mehreren dieser BI-Kategorien, da Analyst:innen, Finanzteams, Anwendungsnutzer:innen und Führungskräfte selten dieselbe Benutzeroberfläche benötigen. Das macht die Architektur unter diesen Tools umso wichtiger. Ein Ansatz, der manchmal als Headless BI bezeichnet wird, trennt die gemeinsame Geschäftslogik von der Präsentationsschicht, sodass Metriken, Dimensionen und Beziehungen über mehrere BI-, Analytics- und KI-Erfahrungen hinweg referenziert werden können.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_six-categories-of-business-intelligence-tools_0":{"id":"card-v2-7ef238575c","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/forte-insurance/"},"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\u003EForte Insurance integriert Power BI mit Snowflake, um Geschäftsanwender:innen aktuellere analytische Einblicke zu geben und die Aktualisierungszeiten von Power BI-Berichten von 12 Minuten auf nahezu Echtzeit zu reduzieren. Das Result Caching von Snowflake reduzierte zudem die durchschnittlichen Abfrageantwortzeiten um bis zu 60 Prozent [Stand März 2025], was veranschaulicht, wie sich die Architektur unter der BI-Schnittstelle auf Leistung und Skalierbarkeit auswirkt.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer Story: Forte Insurance"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dddbd2d4-643d-4952-8141-695bf7f10554/forte-logo.jpg?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Forte Insurance logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_what-actually-differentiates-bi-tools-now-that-features-have-converged":{"id":"title-v2-e1ce0db31c","additionalClasses":"anchor-title anchor-title--what-actually-differentiates-bi-tools-now-that-features-have-converged","type":"heading2","lines":["Was unterscheidet BI-Tools eigentlich noch, nachdem sich die Funktionen angeglichen haben?"],":type":"snowflake-site/components/title-v2"},"text_what-actually-differentiates-bi-tools-now-that-features-have-converged_0":{"id":"text-e098702553","text":"\u003Cp\u003EIn einer Multi-Tool-BI-Umgebung haben vier architektonische Entscheidungen besonders weitreichende Auswirkungen: wo sich das semantische Modell befindet, wie Abfragen die Daten erreichen, wo Governance durchgesetzt wird und wie Geschäftsdefinitionen über Schnittstellen hinweg bereitgestellt werden.\u003C/p\u003E\r\n\u003Ch3\u003EWo sich das semantische Modell befindet\u003C/h3\u003E\r\n\u003Cp\u003EEin semantisches Modell übersetzt physische Datenstrukturen in Geschäftskonzepte und definiert Metriken, Dimensionen, Beziehungen, Berechnungen und andere Regeln, die beschreiben, wie das Unternehmen seine Daten interpretiert. Die semantische Schicht ist die architektonische Schicht, in der diese Modelle verwaltet und dann analytischen Anwendungen zur Verfügung gestellt werden.\u003C/p\u003E\r\n\u003Cp\u003EEinige BI-Tools pflegen ihre eigenen semantischen Modelle innerhalb der Reporting-Anwendung. In einer Multi-Tool-Umgebung schafft dieser Ansatz mehrere Orte, an denen dieselbe Umsatzmetrik, Kundendefinition oder Produkthierarchie abgebildet, gepflegt und schließlich abgeglichen werden muss.\u003C/p\u003E\r\n\u003Cp\u003EEine gemeinsam genutzte semantische Schicht platziert diese Geschäftslogik außerhalb einer einzelnen BI-Schnittstelle. Mehrere Dashboards, analytische Tools und KI-Erfahrungen können dann auf die dort gepflegten Modelle verweisen, was die Notwendigkeit verringert, dieselben Definitionen jedes Mal neu zu erstellen, wenn eine weitere Schnittstelle hinzugefügt wird.\u003C/p\u003E\r\n\u003Ch3\u003EWie Abfragen die Daten erreichen\u003C/h3\u003E\r\n\u003Cp\u003EBI-Tools unterscheiden sich darin, wie sie Daten abrufen und verarbeiten. Bei einer direkten Abfrage oder Live-Verbindung sendet das Tool Abfragen an die zugrunde liegende Datenplattform, wo sie gegen die aktuellen Daten ausgeführt werden. Andere Konfigurationen importieren oder extrahieren Daten in die eigene analytische Engine des BI-Tools und erstellen so eine weitere Kopie mit eigenem Aktualisierungsplan und eigenen Speicheranforderungen.\u003C/p\u003E\r\n\u003Cp\u003EDirekte Abfragen halten die Analyse näher an den Quelldaten und vermeiden einen separaten Aktualisierungszyklus, sind aber nicht für jeden Workload die richtige Wahl. Extrakte können nützlich sein, wenn das Leistungsmodell, die Konnektivitätsanforderungen oder die analytischen Funktionen einer BI-Anwendung davon abhängen. Teams sollten verstehen, wo die Verarbeitung stattfindet, ob eine weitere Kopie erstellt wird und welche Kompromisse dies hinsichtlich Aktualität, Leistung und Kosten mit sich bringt.\u003C/p\u003E\r\n\u003Ch3\u003EDurchsetzung von Governance\u003C/h3\u003E\r\n\u003Cp\u003EGovernance ist einfacher zu verwalten, wenn Zugriffsrichtlinien nah an den Daten bleiben, anstatt in jedem BI-Tool neu erstellt zu werden. Bei einer direkten Verbindung kann die Datenplattform beispielsweise beim Ausführen der Abfrage Zugriff auf Zeilenebene, Maskierung und rollenbasierte Kontrollen anwenden und dann nur die Daten zurückgeben, für die der:die Nutzer:in autorisiert ist.\u003C/p\u003E\r\n\u003Cp\u003EWo immer möglich, sollte die Identität der einzelnen Nutzer:innen durch das BI-Tool bis zur zugrunde liegenden Plattform erhalten bleiben. Dadurch können bestehende Rollen und Richtlinien den Zugriff konsistent steuern. Gemeinsam genutzte Dienstkonten sind manchmal notwendig, erschweren jedoch die Unterscheidung zwischen einzelnen Nutzer:innen und können mehr Logik für die Zugriffssteuerung in die BI-Anwendung verlagern. Extrakte bringen eine weitere Überlegung mit sich: Sobald Daten in die Engine des BI-Tools kopiert werden, benötigt diese Kopie eigene Berechtigungen und Governance-Kontrollen.\u003C/p\u003E\r\n\u003Cp\u003EUm zu bewerten, wie dies in einem BI-Tool funktioniert, verfolgen Sie eine:n repräsentative:n Nutzer:in durch den gesamten Prozess: Welche Identität erreicht die Datenplattform, welche Rolle wird angewendet, wie verhalten sich der Zugriff auf Zeilenebene und die Maskierung, und was ändert sich, wenn Daten die Plattform verlassen? Dies liefert ein viel klareres Bild des Governance-Modells als eine Funktionsliste.\u003C/p\u003E\r\n\u003Ch3\u003EWie Geschäftsdefinitionen über Tools hinweg bereitgestellt werden\u003C/h3\u003E\r\n\u003Cp\u003EDie Zentralisierung von Geschäftsdefinitionen bietet nur einen begrenzten Mehrwert, wenn andere analytische Schnittstellen nicht darauf zugreifen können. In einer Umgebung, die Dashboards, Notebooks, Anwendungen und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/\"\u003EKI-Systeme\u003C/a\u003E umfasst, sollten Teams prüfen, wie Metriken, Dimensionen, Beziehungen und der unterstützende Kontext bereitgestellt werden – über SQL, semantische Schnittstellen oder andere programmatische Zugriffspfade.\u003C/p\u003E\r\n\u003Cp\u003EDies ist besonders wichtig für KI-gestützte Analysen. Wenn ein Modell explizite Definitionen und Beziehungen erhält, muss es die Bedeutung nicht aus Tabellen- und Spaltennamen ableiten, während es eine natürlichsprachliche Anfrage in eine Abfrage übersetzt. Verifizierte Abfragen können Orientierung für wiederkehrende oder komplexe Fragen bieten, indem sie zeigen, wie genehmigte analytische Logik ausgedrückt werden sollte.\u003C/p\u003E\r\n\u003Cp\u003E„Ein physisches Datenbankschema erklärt, wie Daten strukturiert sind, erfasst aber nicht, wie das Unternehmen arbeitet“, sagt Klahr. „Ein LLM kann Regeln wie Umsatzrealisierung, saisonale Anpassungen oder veraltete Randfälle nicht allein aus Tabellenschemata ableiten – diese Logik muss explizit bereitgestellt werden.“\u003C/p\u003E\r\n\u003Cp\u003EEin guter Test ist, ob eine einmal erstellte Definition in den verschiedenen analytischen Erfahrungen, die das Unternehmen betreibt, genutzt werden kann, ohne dass jede die Logik unabhängig neu erstellen muss.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-happens-to-metric-definitions-when-bi-tools-change":{"id":"title-v2-81f7a812d6","additionalClasses":"anchor-title anchor-title--what-happens-to-metric-definitions-when-bi-tools-change","type":"heading2","lines":["Was mit Metrikdefinitionen passiert, wenn sich BI-Tools ändern"],":type":"snowflake-site/components/title-v2"},"text_what-happens-to-metric-definitions-when-bi-tools-change_0":{"id":"text-a7b7b13f2b","text":"\u003Cp\u003EEine ausgereifte BI-Umgebung umfasst weit mehr als nur Dashboards. Im Laufe der Zeit kodieren Teams geschäftliche Bedeutungen in Metriken, Dimensionen, Joins, Filter, Hierarchien und Berechnungen und fügen oft Ausnahmen und Varianten hinzu, wenn sich die Berichtsanforderungen weiterentwickeln. Wenn diese Logik in einem bestimmten BI-Tool angesiedelt ist, bedeutet ein Wechsel der Schnittstelle auch herauszufinden, welche Definitionen übertragen werden, welche übersetzt werden müssen und welche neu erstellt und validiert werden müssen.\u003C/p\u003E\n\u003Ch3\u003ESemantischer Lock-in erhöht den Migrationsaufwand\u003C/h3\u003E\n\u003Cp\u003EEin semantischer Lock-in tritt auf, wenn Geschäftsdefinitionen hauptsächlich in einem proprietären Modell ausgedrückt werden, das andere analytische Systeme nicht ohne Weiteres nutzen können. Je mehr Metriken und Geschäftsregeln Teams in dieses Modell einbauen, desto mehr wächst die Abhängigkeit zusammen mit dem Wert der BI-Umgebung.\u003C/p\u003E\n\u003Cp\u003EDer Neuaufbau und die Validierung dieser Definitionen können erheblichen Aufwand erfordern, selbst wenn die zugrunde liegenden Daten auf derselben Plattform verbleiben. Ein Ersatz-BI-Tool muss weiterhin dieselben Metrikdefinitionen, Beziehungen und Berechnungsregeln verstehen, und die Teams müssen bestätigen, dass die neue Implementierung die beabsichtigten Ergebnisse liefert. Während eines Übergangs können auch alte und neue Berichte nebeneinander existieren, was die Wahrscheinlichkeit sichtbarer Diskrepanzen erhöht, während die Definitionen abgeglichen werden.\u003C/p\u003E\n\u003Ch3\u003EPortabilität hängt davon ab, was das Tool tatsächlich verlässt\u003C/h3\u003E\n\u003Cp\u003EEine Exportoption macht ein semantisches Modell nicht zwangsläufig portabel. Generiertes SQL erfasst beispielsweise die aus einer Definition erstellte Abfrage, lässt jedoch einen Teil des Kontexts weg, der sie hervorgebracht hat – Beziehungen, Metrik-Granularität, wiederverwendbare Dimensionen, Geschäftsterminologie und andere Modellierungsinformationen.\u003C/p\u003E\n\u003Cp\u003ETeams sollten prüfen, was die Plattform auf semantischer Ebene exportieren kann. Metriken, Dimensionen, Beziehungen, Berechnungen und unterstützende Metadaten sollten getrennt von Dashboard-Dateien oder generierten Abfragen betrachtet werden.\u003C/p\u003E\n\u003Cp\u003EDer Praxistest ist einfach: Wenn das Unternehmen zu einer anderen BI-Schnittstelle wechseln würde, wie viel der Geschäftslogik könnte die neue Umgebung direkt nutzen und wie viel müsste neu erstellt werden?\u003C/p\u003E\n\u003Ch3\u003EGemeinsame Definitionen benötigen weiterhin kontrollierte Varianten\u003C/h3\u003E\n\u003Cp\u003EEine gemeinsame semantische Schicht bedeutet nicht, jede Geschäftsfrage durch eine universelle Definition einer Metrik zu zwingen. Umsatz, Abwanderung oder der Status aktiver Kund:innen können für die Finanzberichterstattung, die Vertriebsplanung oder die Produktanalyse unterschiedlich berechnet werden, und diese Unterschiede können legitim sein. Das Ziel ist es, diese Varianten explizit zu machen, anstatt zuzulassen, dass sie sich unabhängig voneinander in separaten BI-Tools entwickeln.\u003C/p\u003E\n\u003Cp\u003EEin gemeinsames semantisches Modell kann eine Standardmetrik neben benannten Varianten mit klaren Berechnungsregeln, Verwendungszwecken und Verantwortlichkeiten definieren, sodass Nutzer:innen wissen, welche Definition in einem bestimmten Kontext gilt. Dies ist besonders während einer Migration wichtig. Wenn Metrikvarianten in der semantischen Schicht dokumentiert sind, können Teams die Definitionen und ihren Kontext gemeinsam verschieben. Wenn diese Unterschiede nur als lokale Berechnungen in Dashboards, Arbeitsblättern oder proprietären Modellen existieren, müssen sie während des Umzugs neu entdeckt und abgeglichen werden.\u003C/p\u003E\n\u003Ch3\u003EOffene semantische Standards könnten die Portabilität verbessern\u003C/h3\u003E\n\u003Cp\u003EOffene semantische Spezifikationen beginnen, das Problem der Portabilität anzugehen. Im Juli 2026 wurde das Projekt, das heute als Apache Ossie (incubating) bekannt ist – ehemals \u003Ca href=\"https://www.snowflake.com/en/blog/open-semantic-interchanges-specs-finalized/\"\u003EOpen Semantic Interchange\u003C/a\u003E –, in den Apache Incubator aufgenommen. Die YAML-basierte Spezifikation bietet eine anbieterneutrale Möglichkeit, Metriken, Dimensionen, Beziehungen und einen breiteren semantischen Kontext darzustellen. Die teilnehmende Koalition ist auf mehr als 50 Unternehmen angewachsen, wobei sich Referenzkonverter bereits in der Entwicklung befinden. Beachten Sie, dass sich das Projekt in der Inkubationsphase befindet und daher als aufkommende Interoperabilitätsinitiative betrachtet werden sollte.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-happens-to-metric-definitions-when-bi-tools-change_0":{"id":"text-da5a967096","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EHÄUFIGER FALLSTRICK\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EVerwechseln Sie den Dashboard-Export nicht mit semantischer Portabilität. Das Verschieben von Visualisierungen oder generiertem SQL bewahrt nicht zwangsläufig die Metriken, Beziehungen, Granularität, Berechnungen und den geschäftlichen Kontext dahinter.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-to-evaluate-a-business-intelligence-tool":{"id":"title-v2-881a4ee50d","additionalClasses":"anchor-title anchor-title--how-to-evaluate-a-business-intelligence-tool","type":"heading2","lines":["So bewerten Sie ein Business-Intelligence-Tool"],":type":"snowflake-site/components/title-v2"},"text_how-to-evaluate-a-business-intelligence-tool_0":{"id":"text-68c6ce0c6a","text":"\u003Cp\u003EEine ausgefeilte Anbieterdemo zeigt in der Regel den kürzesten Weg zu einem attraktiven Ergebnis. Eine nützliche Bewertung folgt dem Weg, den Daten, Definitionen und Richtlinien in der Produktion nehmen werden.\u003C/p\u003E\n\u003Cp\u003EArbeiten Sie diese Prüfungen nacheinander ab:\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cstrong\u003EZeigen Sie, wo eine Geschäftsmetrik definiert ist:\u003C/strong\u003E Wählen Sie eine reale Kennzahl, wie z. B. die Bruttomarge oder aktive Kund:innen, und verfolgen Sie deren Berechnung. Identifizieren Sie, wo sich Joins, Filter, Berechnungen und Varianten befinden, wer sie bearbeitet und welche anderen Schnittstellen Zugriff auf dieselbe Definition haben.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EVerfolgen Sie eine Abfrage von der Schnittstelle zu den Daten:\u003C/strong\u003E Stellen Sie fest, ob das Tool die Quelle live abfragt, Query Pushdown verwendet oder einen Extrakt in seiner eigenen Engine erstellt. Dokumentieren Sie für Extrakte die Aktualisierungsfrequenz, den Speicher, die Berechtigungen und den operativen Prozess, um die Kopie mit der Quelle synchron zu halten.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ETesten Sie eine bestehende Governance-Richtlinie:\u003C/strong\u003E Verwenden Sie eine repräsentative Einschränkung auf Zeilenebene, eine Maskierungsrichtlinie oder eine eingeschränkte Spalte und überprüfen Sie deren Verhalten in der BI-Umgebung. Wenn die Richtlinie neu erstellt werden muss, beziehen Sie diese Arbeit in das Administrations- und Audit-Modell ein.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ETesten Sie die Spitzennebenläufigkeit mit realistischen Workloads:\u003C/strong\u003E Die durchschnittliche Abfrageleistung sagt wenig über den Montagmorgen aus, wenn geplante Dashboards aktualisiert werden, während Analyst:innen und Geschäftsanwender:innen aktiv sind. Verwenden Sie während des Proof of Concept eine repräsentative Berichtskomplexität, Datenvolumina und Nebenläufigkeit.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrüfen Sie, wie gemeinsame Geschäftsdefinitionen bereitgestellt werden:\u003C/strong\u003E Wenn Conversational Analytics oder Agenten Teil der Roadmap sind, ermitteln Sie, wie die Software verwaltete Metriken und den semantischen Kontext abruft.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFragen Sie nach dem Exit-Pfad:\u003C/strong\u003E Fordern Sie das Format an, das zum Exportieren des semantischen Modells verwendet wird, einschließlich Metriken, Dimensionen, Beziehungen und unterstützender Metadaten. Fragen Sie dann nach einem Beispiel für eine Kundenmigration, bei der dieser Export verwendet wurde.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EModellieren Sie die Gesamtkosten anhand der erwarteten Architektur:\u003C/strong\u003E Berücksichtigen Sie das BI-Abonnement, die Rechenleistung der Datenplattform, gegebenenfalls den Extrakt-Speicher, die Administration und die Zeit der Analyst:innen, die mit duplizierter oder abgeglichener Logik verbunden ist. Die Ausbreitung von BI-Tools ist oft mit Kosten verbunden, die im Lizenzangebot nie auftauchen.\u003C/li\u003E\n\u003C/ol\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-evaluate-a-business-intelligence-tool_0":{"id":"text-fddba958fe","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EVerwenden Sie während des Proof of Concept eine reale Metrik und eine:n repräsentative:n Nutzer:in und verfolgen Sie dann die Definition, den Abfragepfad, die Durchsetzung von Richtlinien und die Portabilität durchgängig. Sie werden weitaus mehr lernen als aus einer generischen Demo.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-business-intelligence-tools-work-with-snowflake":{"id":"title-v2-f1ceeafcb1","additionalClasses":"anchor-title anchor-title--how-business-intelligence-tools-work-with-snowflake","type":"heading2","lines":["Wie Business-Intelligence-Tools mit Snowflake funktionieren"],":type":"snowflake-site/components/title-v2"},"text_how-business-intelligence-tools-work-with-snowflake_0":{"id":"text-1c20063f63","text":"\u003Cp\u003ESnowflake befindet sich unterhalb der BI-Schnittstelle und stellt die Daten, die Rechenleistung, die Governance und den semantischen Kontext bereit, die diese Tools verwenden. Mit Semantic Views definieren Teams Geschäftskonzepte wie Metriken, Fakten, Dimensionen, Beziehungen und Berechnungen in Snowflake, anstatt sie in jeder Berichtsanwendung unabhängig neu zu erstellen.\u003C/p\u003E\n\u003Cp\u003EDiese Semantic Views unterstützen verschiedene analytische Erfahrungen. Sie können direkt mit SQL abgefragt, von \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-analyst/\"\u003ECortex Analyst\u003C/a\u003E zur Beantwortung natürlichsprachlicher Analysen verwendet und über die Weboberfläche von Snowflake mithilfe des Semantic View Editor und des Semantic View Autopilot erstellt oder verwaltet werden.\u003C/p\u003E\n\u003Cp\u003EDa die semantischen Definitionen und Governance-Kontrollen näher an den zugrunde liegenden Daten bleiben, können BI-, Analyse- und KI-Schnittstellen mit einer gemeinsamen Geschäftslogik arbeiten und gleichzeitig unterschiedliche Nutzer:innen und Workflows bedienen. 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