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Ein praktisches Framework für Entscheidungen, Governance und Messung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-b693c699fe","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EEine starke BI-Strategie sorgt dafür, dass Reporting, Self-Service und neuere KI-gestützte Erfahrungen auf gemeinsame Definitionen, klare Verantwortlichkeiten und messbare Geschäftsergebnisse ausgerichtet bleiben, während der analytische Zugriff im gesamten Unternehmen ausgeweitet wird.\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,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Scorecard\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","isActiveTOC":false,"isBlogPage":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-7b55088fa7","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-c23688175a","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EBI-STRATEGIE – DEFINITION\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EEine BI-Strategie ist ein Framework für die Entscheidung, wie ein Unternehmen Daten in konsistente, vertrauenswürdige Informationen für Geschäftsentscheidungen umwandelt, einschließlich der Prioritäten, Verantwortlichkeiten, Governance und des Bereitstellungsansatzes, die sein BI-Programm leiten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-47d3b9341c","text":"\u003Cp\u003EEine Business-Intelligence-Strategie (BI-Strategie) muss zwei Dinge gleichzeitig unterstützen: die analytische Umgebung, die das Unternehmen bereits betreibt, und die wachsende Bandbreite an Möglichkeiten, wie Teams mit Daten arbeiten möchten. Wiederkehrende Berichte müssen genau und nützlich bleiben, Self-Service-Analytics muss funktionieren, ohne dass jedes Team Metriken unterschiedlich definiert, und Geschäftsanwender:innen möchten Zugriff auf neuere Funktionen wie dialogbasierte Schnittstellen und agentenbasierte Analytics-Workflows.\u003C/p\u003E\n\u003Cp\u003EMit zunehmendem Umfang haben Entscheidungen über Verantwortlichkeiten, gemeinsame Definitionen und Bereitstellung Konsequenzen, die sich auf mehr Bereiche des Unternehmens auswirken. Eine Unstimmigkeit bei Metriken, die früher nur zwei Dashboards betraf, könnte nun in einer Self-Service-Analyse oder einer KI-generierten Antwort auftauchen, während ein Betriebsmodell, das um eine zentrale Anfrage-Warteschlange herum aufgebaut ist, mehr Personen aufnehmen muss, die direkt Fragen stellen.\u003C/p\u003E\n\u003Cp\u003EDiese operativen Themen stehen nach wie vor ganz oben auf den Prioritätenlisten von BI-Führungskräften. In der \u003Ca href=\"https://barc.com/news/barc-publishes-the-data-bi-and-analytics-trend-monitor-2026/\" target=\"_blank\"\u003EUmfrage von BARC aus dem Jahr 2026\u003C/a\u003E unter 1.579 Daten-, BI- und Analytics-Expert:innen belegte das Datenqualitätsmanagement den ersten Platz unter 20 Trends, gefolgt von Datensicherheit und Datenschutz, wobei datengestützte Kultur, Daten- und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/ai-governance/\"\u003EKI-Governance\u003C/a\u003E sowie Daten- und KI-Kompetenz die Top 5 abrundeten.\u003C/p\u003E\n\u003Cp\u003EEine BI-Strategie verbindet operative Entscheidungen mit Geschäftsprioritäten: auf welche Entscheidungen sich \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/\"\u003EBusiness Intelligence\u003C/a\u003E konzentrieren sollte, wo gemeinsame Definitionen angesiedelt sind und wer für sie verantwortlich ist, wie analytische Arbeit organisiert ist und wie der Fortschritt gemessen wird.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-business-intelligence-strategy":{"id":"title-v2-81e020f55c","additionalClasses":"anchor-title anchor-title--what-is-a-business-intelligence-strategy","type":"heading2","lines":["Was ist eine BI-Strategie?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-business-intelligence-strategy_0":{"id":"text-9a047ca2af","text":"\u003Cp\u003EEine Business-Intelligence-Strategie ist ein Betriebsmodell dafür, wie ein Unternehmen Daten mit zentraler Governance, \u003Ca href=\"https://www.snowflake.com/de/analytics/\"\u003EAnalytics\u003C/a\u003E und Reporting nutzt, um Geschäftsentscheidungen zu unterstützen. Sie verbindet Geschäftsprioritäten mit den Personen, Definitionen, Prozessen und der Technologie, die für die Bereitstellung von Informationen bei der Entscheidungsfindung verantwortlich sind.\u003C/p\u003E\n\u003Cp\u003EAuf praktischer Ebene beantwortet eine BI-Strategie vier Fragen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EWelche Geschäftsentscheidungen oder Workflows sollte BI verbessern?\u003C/li\u003E\n\u003Cli\u003EWo werden gemeinsame Metriken definiert und wer ist für ihre Bedeutung verantwortlich?\u003C/li\u003E\n\u003Cli\u003EWie ist die BI-Arbeit organisiert und wie greifen Personen auf freigegebene Antworten zu?\u003C/li\u003E\n\u003Cli\u003EWelche Indikatoren zeigen, ob das Programm funktioniert?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDer Anwendungsbereich ist enger als bei einer Datenstrategie, die sich damit befasst, wie Daten über Workloads hinweg erfasst, organisiert, über Governance gesteuert, geschützt und verfügbar gemacht werden. Eine Analytics-Strategie erstreckt sich im Allgemeinen weiter auf analytische Methoden, einschließlich Advanced Analytics und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E. Eine BI-Strategie ist in diese größere Umgebung eingebettet und konzentriert sich darauf, wie Personen analytische Informationen für Geschäftsentscheidungen erhalten und nutzen.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EErfahren Sie, wie National Tiles eine vertrauenswürdige Datengrundlage für schnellere Entscheidungen und Self-Service-Analytics aufgebaut hat:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-a-business-intelligence-strategy_0":{"id":"embed-63dadceaa6","youtubeVideoId":"KBYk2rgIE1I","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_the-four-decisions-a-bi-strategy-should-make":{"id":"title-v2-dfb56951ea","additionalClasses":"anchor-title anchor-title--the-four-decisions-a-bi-strategy-should-make","type":"heading2","lines":["Die vier Entscheidungen, die eine BI-Strategie treffen sollte"],":type":"snowflake-site/components/title-v2"},"text_the-four-decisions-a-bi-strategy-should-make_0":{"id":"text-1d3e5341c0","text":"\u003Cp\u003EViele BI-Frameworks organisieren die Strategie um vier breite Säulen: Dateninfrastruktur, \u003Ca href=\"https://www.snowflake.com/de/data-governance/\"\u003EData Governance\u003C/a\u003E, Analytics-Technologie sowie Kultur und Akzeptanz. Diese Kategorien sind nützlich für die Bestandsaufnahme der Arbeit, aber ihre Umwandlung in explizite Entscheidungen gibt dem Unternehmen etwas Konkreteres, das es implementieren, zuweisen und überprüfen kann.\u003C/p\u003E\n\u003Ch3\u003EIdentifizieren Sie die Entscheidungen, die BI verbessern sollte\u003C/h3\u003E\n\u003Cp\u003EWenn man mit Dashboard-Anforderungen beginnt, neigt man dazu, die Strategie eher auf Outputs als auf die Geschäftsentscheidungen auszurichten, die diese Outputs unterstützen sollen. Wenn man mit Geschäftsentscheidungen beginnt, wird der Fokus auf ein beobachtbares Ergebnis eingegrenzt.\u003C/p\u003E\n\u003Cp\u003EEin Finanzteam möchte beispielsweise den monatlichen Prognosezyklus verkürzen, während eine Vertriebsorganisation etwa erreichen will, dass regionale Vertriebsleiter:innen Änderungen in der Pipeline-Abdeckung vor dem wöchentlichen Forecast-Call erkennen. Beide Anforderungen führen letztendlich zu Datenprodukten, Metriken und Nutzererlebnissen, obwohl der strategische Teil der Arbeit die Entscheidung selbst ist.\u003C/p\u003E\n\u003Cp\u003EDokumentieren Sie für jede Priorität, wer die Entscheidung trifft, welche Informationen die Person benötigt, wie häufig die Entscheidung getroffen wird und was derzeit zu Verzögerungen oder Unsicherheiten führt. Eine Baseline gibt dem Team etwas, das es später messen kann: aktuelle Entscheidungszeit, aktueller manueller Aufwand, aktuelles Eskalationsvolumen oder ein anderes Signal, das eng mit dem Workflow verbunden ist.\u003C/p\u003E\n\u003Cp\u003EDen anfänglichen Umfang klein zu halten, hilft bei der Implementierung. Zwei oder drei weitreichende Entscheidungen bieten genügend Spezifität, um die Strategie an realem Verhalten zu testen, bevor das Programm ausgeweitet wird.\u003C/p\u003E\n\u003Ch3\u003ELegen Sie fest, wo Metrikdefinitionen angesiedelt sind und wer für sie verantwortlich ist\u003C/h3\u003E\n\u003Cp\u003EEine Metrikdefinition umfasst mehr als nur ein Label. \u003Ci\u003EUmsatz\u003C/i\u003E, \u003Ci\u003Eaktive Kund:innen\u003C/i\u003E oder \u003Ci\u003EBruttomarge\u003C/i\u003E beinhalten in der Regel eine Formel, Granularität, ein Zeitfenster, Quellannahmen, Filter und Aggregationsverhalten. Wenn sich eines dieser Elemente zwischen den Teams unterscheidet, können zwei genaue Berichte unterschiedliche Zahlen aufweisen.\u003C/p\u003E\n\u003Cp\u003EDie Verantwortlichkeit sollte sich über Geschäfts- und Datenteams erstrecken. Geschäfts-Stakeholder genehmigen, was eine Kennzahl im Kontext des Unternehmens bedeutet, während Datenteams diese Logik codieren, pflegen und über Governance steuern, damit die genehmigte Definition nachgelagert konsistent verfügbar ist. Änderungen erfordern ebenfalls eine verantwortliche Person, insbesondere wenn eine neue Quelle, eine Akquisition oder eine organisatorische Änderung die bestehende Logik beeinflusst.\u003C/p\u003E\n\u003Cp\u003EGovernance wirkt sich messbar auf das Vertrauen aus. Die \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.lebow.drexel.edu/sites/default/files/2026-01/lebow-precisely-state-data-integrity-ai-readiness-2026.pdf\"\u003EForschung von Precisely und der Drexel University aus dem Jahr 2026\u003C/a\u003E ergab, dass Unternehmen mit formellen Data-Governance-Programmen ein hohes Vertrauen in ihre Daten von 71 % meldeten, verglichen mit 50 % bei Unternehmen ohne formelle Governance.\u003C/p\u003E\n\u003Cp\u003EFür BI sollte diese Governance die semantische Schicht erreichen. Gemeinsame Metriken, Dimensionen und Beziehungen geben Dashboards, Ad-hoc-Abfragen und KI-gestützten Schnittstellen ein gemeinsames Set von Geschäftsregeln, wodurch die Menge an Logik reduziert wird, die in jedem Nutzungstool separat neu erstellt wird.\u003C/p\u003E\n\u003Ch3\u003EWählen Sie das BI-Betriebsmodell\u003C/h3\u003E\n\u003Cp\u003EDas Betriebsmodell bestimmt, wo die BI-Expertise angesiedelt ist, wer auf die geschäftliche Nachfrage reagiert und wie viel Kontrolle einzelne Domänen über die analytische Arbeit haben.\u003C/p\u003E\n\u003Cp\u003EIn einem zentralisierten Modell ist ein BI- oder Analytics-Team für den Großteil der Entwicklung und Governance verantwortlich. Konsistenz ist einfacher zu koordinieren, obwohl sich bei steigender Nachfrage häufig eine große Anfrage-Warteschlange bildet.\u003C/p\u003E\n\u003Cp\u003EEin föderiertes Modell verlagert mehr analytische Fähigkeiten in die Geschäftsdomänen. Teams erhalten einen engeren Zugang zu Fachwissen und kürzere Feedbackschleifen, während gemeinsame Definitionen und Governance eine bewusste Koordination über Domänen hinweg erfordern.\u003C/p\u003E\n\u003Cp\u003EHub-and-Spoke-Modelle kombinieren zentrale Standards mit verteilter Ausführung. Ein zentrales BI- oder Analytics-Center of Excellence könnte für gemeinsame Metriken, Architektur, Governance und Enablement verantwortlich sein, während Domänenteams Analysen rund um ihre eigenen Geschäftsanforderungen erstellen.\u003C/p\u003E\n\u003Cp\u003EEs gibt kein universelles „bestes“ Modell, das man auswählen könnte. Die geeignete Struktur hängt von der Unternehmensgröße, den regulatorischen Anforderungen, der Domänenkomplexität, den analytischen Fähigkeiten und dem Grad der Unabhängigkeit ab, den die Geschäftsteams benötigen. Was explizit sein sollte, ist die Aufteilung der Verantwortlichkeiten: wer Standards definiert, wer analytische Produkte entwickelt, wer gemeinsame Metriken genehmigt und wer Unstimmigkeiten löst.\u003C/p\u003E\n\u003Ch3\u003EDefinieren Sie, wie das Programm beurteilt wird\u003C/h3\u003E\n\u003Cp\u003EDefinieren Sie Erfolgskriterien, während die Geschäftsergebnisse noch die Arbeit prägen. Sobald die Bereitstellung im Gange ist, neigen Teams dazu, das zu messen, was ihre Systeme aufzeichnen: veröffentlichte Dashboards, geschlossene Tickets, ausgeführte Abfragen oder lizenzierte Nutzer:innen. Diese Zahlen beschreiben die Aktivität. Diese Metriken liefern viel weniger Informationen darüber, ob Mitarbeitende tatsächlich schneller nützliche Antworten erhalten oder ob sich Geschäftsentscheidungen verbessern.\u003C/p\u003E\n\u003Cp\u003EEine BI-Strategie sollte sowohl Früh- als auch Spätindikatoren sowie eine verantwortliche Person, eine Baseline und einen Überprüfungsrhythmus für jeden Indikator festlegen. Frühindikatoren zeigen, ob das Betriebsmodell greift, während Spätindikatoren BI mit Änderungen in Geschäftsprozessen und -ergebnissen verbinden.\u003C/p\u003E\n\u003Cp\u003E„Wahre Wirkung entsteht durch die Verkürzung der Distanz zwischen Exploration und Aktion“, sagt \u003Ca href=\"https://www.snowflake.com/en/blog/authors/josh-klahr/\"\u003EJosh Klahr\u003C/a\u003E, Head of Product Management for Analytics bei Snowflake. „Ein Datenteam ist erfolgreich, wenn Entscheidungsträger:innen von einer breit angelegten, explorativen Frage dazu übergehen können, auf der Grundlage einer zertifizierten Metrik mit Vertrauen in die zugrunde liegende Logik zu handeln.“\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-four-decisions-a-bi-strategy-should-make_0":{"id":"card-v2-2cfe4485a5","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/indeed/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer Story: Indeed"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"/adobe/dynamicmedia/deliver/dm-aid--b6d12684-c637-4d20-a46a-2098c164b52e/indeed-logo.png?preferwebp=true&quality=85","alt":"Indeed logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EIndeed nutzt Snowflake zur Unterstützung von Reporting, Self-Service-Analytics und arbeitgeberorientierter Analytics über einen 52 PB großen Data Lake, der Millionen von Arbeitssuchenden und Arbeitgebern bedient. Die Verlagerung seiner Analytics-APIs zu Snowflake senkte die Bereitstellungskosten um 70 %, während Snowflake Interactive Analytics die durchschnittliche Abfragelatenz um 60 % reduzierte und die Infrastrukturkosten um weitere 45 % senkte [Stand: Juli 2025]. Indeed standardisiert außerdem zentrale Geschäftskennzahlen, sodass dieselben Definitionen Dashboards, APIs und KI-Agenten unterstützen können, wodurch das Risiko inkonsistenter Antworten über analytische Erfahrungen hinweg verringert wird.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-to-build-a-bi-strategy":{"id":"title-v2-25fe5f5182","additionalClasses":"anchor-title anchor-title--how-to-build-a-bi-strategy","type":"heading2","lines":["Wie man eine BI-Strategie aufbaut"],":type":"snowflake-site/components/title-v2"},"text_how-to-build-a-bi-strategy_0":{"id":"text-6a3ef63534","text":"\u003Cp\u003EDie meisten BI-Strategien umfassen vertraute Aufgaben: die aktuelle Umgebung bewerten, Stakeholder abstimmen, Daten vorbereiten, Technologie auswählen, die Implementierung planen und Nutzer:innen schulen.\u003C/p\u003E\n\u003Cp\u003EDie Reihenfolge dieser Schritte beeinflusst, worauf die Strategie aufbaut. Wenn die Auswahl des \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/tools/\"\u003EBI-Tools\u003C/a\u003E erfolgt, bevor sich das Unternehmen auf Entscheidungsprioritäten, Verantwortlichkeiten und Metrikdefinitionen geeinigt hat, prägt die Software die Strategie, anstatt sie zu unterstützen.\u003C/p\u003E\n\u003Ch3\u003EDefinieren Sie Geschäftsergebnisse und den Entscheidungsumfang\u003C/h3\u003E\n\u003Cp\u003EBeginnen Sie mit den Geschäftsentscheidungen, die zur Verbesserung ausgewählt wurden, und beschreiben Sie den aktuellen Prozess rund um jede einzelne. Identifizieren Sie für jede Entscheidung die für die Entscheidung verantwortliche Person, die erforderlichen Informationen, den Rhythmus und die aktuelle Reibungsquelle. Wenn das Problem ein langsamer Zugriff auf Informationen ist, messen Sie die aktuelle Verzögerung. Wenn Teams Stunden damit verbringen, Zahlen vor einer Überprüfung abzugleichen, erfassen Sie diese Arbeit. Wenn eine Metrik routinemäßig umstritten ist, dokumentieren Sie die konkurrierenden Definitionen und die jeweils Verantwortlichen. Spezifität hilft dabei, das BI-Programm an einen Geschäftsprozess gebunden zu halten, sobald die Implementierung beginnt.\u003C/p\u003E\n\u003Ch3\u003EBewerten Sie die aktuelle BI-Umgebung\u003C/h3\u003E\n\u003Cp\u003EDie Bewertung des Ist-Zustands sollte sowohl die Technologie als auch das Verhalten untersuchen. Inventarisieren Sie Berichte, Dashboards, semantische Modelle, Extrakte und wiederkehrende analytische Workflows und sehen Sie sich dann an, wie sie tatsächlich genutzt werden. Stark genutzte Assets verdienen während der Migration Schutz, während Berichte, die sich überschneiden, auf Konsolidierungspotenzial hinweisen. Lange ungenutzte Assets werfen Fragen zu laufenden Aktualisierungs- und Wartungskosten auf.\u003C/p\u003E\n\u003Cp\u003ETeams müssen außerdem Definitionen und die Datenherkunft (Data Lineage) für die Metriken zuordnen, die mit den Prioritätsentscheidungen der Strategie verknüpft sind. Wo mehrere Definitionen existieren, identifizieren Sie, wo sie abweichen – Berechnungslogik, Filter, Quellsysteme, Granularität oder Aktualität.\u003C/p\u003E\n\u003Cp\u003EAuch betriebliche Einschränkungen gehören in die Bewertung. Spitzen-Parallelität, Aktualisierungspläne, Abfragelatenz, Lizenzierung und Rechenressourcen-Kosten beeinflussen alle die darauf folgende Architektur.\u003C/p\u003E\n\u003Ch3\u003EEtablieren Sie Sponsoring und Entscheidungsrechte\u003C/h3\u003E\n\u003Cp\u003EEin BI-Programm überschreitet in der Regel Abteilungsgrenzen, daher benötigt das Executive Sponsorship genügend Autorität, um Prioritäten und Zuständigkeitskonflikte zu lösen. Der:die Sponsor:in ist für die mit der Strategie verbundenen Geschäftsergebnisse verantwortlich. Daten- und Plattformleiter:innen verantworten die technische Bereitstellung und Governance, während Domänenleiter:innen Fachexpertise liefern und Geschäftsdefinitionen in ihren Bereichen genehmigen.\u003C/p\u003E\n\u003Cp\u003EDas schriftliche Festhalten dieser Entscheidungsrechte vermeidet Unklarheiten. Eine einfache Verantwortlichkeitsmatrix reicht oft aus, vorausgesetzt, sie deckt die Genehmigung von Metriken, die Behebung von Datenqualitätsproblemen, Plattformstandards, Zugriffsrichtlinien und Priorisierung ab.\u003C/p\u003E\n\u003Ch3\u003EDie datenbezogene und semantische Grundlage vorbereiten\u003C/h3\u003E\n\u003Cp\u003E„Daten vorbereiten“ deckt mehrere verschiedene Anforderungen ab, die separat bewertet werden sollten.\u003C/p\u003E\n\u003Cp\u003EFür die Prioritätsentscheidungen müssen Teams wissen, ob die erforderlichen Quellen verfügbar, ausreichend vollständig und aktuell genug für den Entscheidungsrhythmus sind. Sie benötigen außerdem Definitionen für die Metriken, die Nutzer:innen konsumieren werden, bekannte Beziehungen zwischen Geschäftsentitäten und die Datenherkunft zurück zu den zugrunde liegenden Daten.\u003C/p\u003E\n\u003Cp\u003EDie Aktualität sollte sich nach den Geschäftsanforderungen richten. Eine monatlich getroffene Personalentscheidung hat ganz andere Latenzanforderungen als operative Entscheidungen, die den ganzen Tag über getroffen werden; beide im schnellsten verfügbaren Rhythmus zu aktualisieren, erhöht die Kosten, ohne zwangsläufig Mehrwert zu schaffen.\u003C/p\u003E\n\u003Cp\u003E„Datenaktualität ist kein Ehrenabzeichen. Sie sollte auf die Geschwindigkeit der zu treffenden Entscheidung abgestimmt sein“, sagt Klahr. „Das Streamen von Echtzeitdaten in eine Umgebung, die für die wöchentliche strategische Planung gedacht ist, erhöht die technische Komplexität, ohne einen entsprechenden Business Value zu liefern.“\u003C/p\u003E\n\u003Cp\u003EDas gleiche Prinzip gilt für Rechenressourcen. Die BI-Architektur sollte erwartete Parallelität und Workload-Muster vor der Bereitstellung berücksichtigen, sodass wiederkehrende Dashboards, explorative Analysen und andere Workloads über angemessene Ressourcen verfügen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_how-to-build-a-bi-strategy_0":{"id":"quote-item-4188c78965","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"Datenaktualität ist kein Ehrenabzeichen … Das Streamen von Echtzeitdaten in eine Umgebung, die für die wöchentliche strategische Planung gedacht ist, erhöht die technische Komplexität, ohne einen entsprechenden Business Value zu liefern.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_how-to-build-a-bi-strategy_1":{"id":"text-11c760bf55","text":"\u003Ch3\u003EWählen Sie Bereitstellungsarchitektur und Tools aus\u003C/h3\u003E\n\u003Cp\u003ESobald das Betriebsmodell, die Definitionen und die Workload-Anforderungen klar sind, unterliegt die Tool-Evaluierung praktischen Vorgaben. Eine BI-Plattform muss nun zu mehreren Nutzungsformen passen: geplantes Reporting, interaktive Exploration, Self-Service mit Governance, Embedded Analytics und zunehmend natürlichsprachliche Schnittstellen. Die Integration in die semantischen und Governance-Schichten des Unternehmens verdient besondere Aufmerksamkeit, da die separate Neuerstellung von Geschäftslogik in jedem Tool die Anzahl der Definitionen erhöht, die Teams pflegen müssen.\u003C/p\u003E\n\u003Cp\u003EBewerten Sie betriebliche Eigenschaften neben den Funktionen für Nutzer:innen. Parallelitätsverhalten, Caching, Workload-Isolierung, Nutzungstelemetrie, Kostenzuordnung und Lifecycle-Management wirken sich alle auf die langfristige Wirtschaftlichkeit einer BI-Umgebung aus.\u003C/p\u003E\n\u003Ch3\u003EPlanen Sie die Akzeptanz als Teil der Bereitstellung\u003C/h3\u003E\n\u003Cp\u003EEine zentral definierte Metrik hat einen begrenzten praktischen Wert, wenn die Personen, die die Entscheidung treffen, sie weder verstehen noch wissen, wo sie zu finden ist. Dieses Problem bleibt eine erhebliche organisatorische Herausforderung. In einer \u003Ca href=\"https://www.datacamp.com/blog/the-data-literacy-skills-gap-in-enterprise-why-table-stakes-still-aren-t-universal\" target=\"_blank\"\u003E2026 gemeinsam mit YouGov durchgeführten DataCamp-Umfrage\u003C/a\u003E gaben 88 % von über 500 Führungskräften in Unternehmen in den USA und Großbritannien an, dass grundlegende Datenkompetenz für die tägliche Arbeit unerlässlich ist, während fast 60 % eine Lücke bei den Datenkompetenzen in ihrem Unternehmen meldeten.\u003C/p\u003E\n\u003Cp\u003EEnablement sollte daher neben der technischen Arbeit in der Roadmap erscheinen. Schulungen sollten die tatsächlichen Entscheidungen, Definitionen und BI-Workflows des Unternehmens nutzen, mit zusätzlicher Unterstützung für Nutzer:innen, die ihre eigenen Analysen erstellen.\u003C/p\u003E\n\u003Cp\u003EZur Akzeptanz gehört auch die Ausmusterung. Das Entfernen doppelter oder ungenutzter Berichte hält die BI-Umgebung verständlich und reduziert den Wartungsaufwand, wenn neue Assets hinzugefügt werden.\u003C/p\u003E\n\u003Ch3\u003ELegen Sie die Reihenfolge der Roadmap und einen Überprüfungsrhythmus fest\u003C/h3\u003E\n\u003Cp\u003EDie Roadmap übersetzt die strategischen Entscheidungen in geordnete Arbeit: Metrikdefinition, Datenaufbereitung, Plattformkonfiguration, Migration, Rollout und Enablement. Abhängigkeiten sollten die Reihenfolge bestimmen. Ein Dashboard, das auf einer ungelösten Metrikdefinition basiert, wird wahrscheinlich nicht von einem früheren Bereitstellungsdatum profitieren. Ebenso neigt ein Self-Service-Rollout, der geplant ist, bevor Governance und Verantwortlichkeiten etabliert sind, dazu, bestehende Meinungsverschiedenheiten über einen größeren Kreis von Nutzer:innen zu verteilen.\u003C/p\u003E\n\u003Cp\u003EGeben Sie der Strategie schließlich ein eigenes Überprüfungsdatum. Geschäftsprioritäten, Organisationsstrukturen und analytische Schnittstellen ändern sich, und das Betriebsmodell sollte überarbeitet werden, wenn diese Änderungen Verantwortlichkeiten, Definitionen, Bereitstellung oder Messung betreffen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-build-a-bi-strategy_0":{"id":"text-a5bc59b6bb","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EHÄUFIGER FALLSTRICK\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDie Auswahl einer BI-Plattform vor der Einigung über die Verantwortung für Metriken und das Betriebsmodell ist ein häufiger Fehler. Teams passen dann letztendlich Governance und Workflows an Annahmen an, die bereits im Tool verankert sind.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_five-stages-of-bi-maturity":{"id":"title-v2-031893d426","additionalClasses":"anchor-title anchor-title--five-stages-of-bi-maturity","type":"heading2","lines":["Fünf Stufen der BI-Reife"],":type":"snowflake-site/components/title-v2"},"text_five-stages-of-bi-maturity_0":{"id":"text-6b87aa2a2e","text":"\u003Cp\u003EEin BI-Reifegradmodell zeigt, wie sich Fähigkeiten im Laufe der Zeit ansammeln. Reife Unternehmen verlassen sich immer noch auf wiederkehrende Berichte und Dashboards, auch wenn sie Self-Service mit Governance, Embedded Analytics, prädiktive Methoden und dialogbasierte oder agentenbasierte Workflows hinzufügen. Jede Stufe erweitert die Möglichkeiten, wie Menschen mit Daten arbeiten können.\u003C/p\u003E\n\u003Cp\u003EDie folgenden Stufen beschreiben, wie ein Unternehmen den Zugang zu freigegebenen analytischen Antworten erweitert.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EStufe\u003C/th\u003E\u003Cth\u003EBetriebsmuster\u003C/th\u003E\u003Cth\u003ETypische Einschränkung\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003E1. Ad-hoc\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EEinzelpersonen erstellen Tabellenkalkulationen, Extrakte und einmalige Analysen zu unmittelbaren Fragen\u003C/td\u003E\u003Ctd\u003EGemeinsame Definitionen und Verantwortlichkeiten sind begrenzt\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003E2. Standardisiert\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EEin zentrales Team erstellt wiederkehrende Berichte und Dashboards\u003C/td\u003E\u003Ctd\u003EDie Nachfrage übersteigt die Kapazität der zentralen Anfragebearbeitung\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003E3. Self-Service mit Governance\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EGemeinsame Definitionen unterstützen Geschäftsanwender:innen bei der Erstellung eigener Analysen\u003C/td\u003E\u003Ctd\u003EGovernance und Enablement müssen mit dem Zugang skalieren\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003E4. Workflow-integriert\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EAnalytische Ergebnisse erscheinen direkt in operativen und geschäftlichen Workflows\u003C/td\u003E\u003Ctd\u003EAktualität, Parallelität und Produktintegration werden immer wichtiger\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003E5. Dialogbasiert und agentenbasiert mit Governance\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ENatürlichsprachliche Schnittstellen und Agenten nutzen gemeinsame semantische Definitionen, um Fragen zu beantworten und Aktionen zu unterstützen\u003C/td\u003E\u003Ctd\u003ESemantische Genauigkeit, Evaluierung, Governance und \u003Ca href=\"https://www.snowflake.com/en/observability/\"\u003EObservability\u003C/a\u003E werden zu zentralen Betriebsanforderungen\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EUnternehmen durchlaufen diese Stufen nicht zwangsläufig als Ganzes. Die Finanzabteilung nutzt möglicherweise Self-Service mit Governance, während sich ein anderer Bereich immer noch auf zentralisiertes Reporting verlässt; Embedded Analytics könnte ausgereift sein, selbst wenn die interne Metrik-Governance fragmentiert bleibt.\u003C/p\u003E\n\u003Cp\u003EFortschritt ist auch umkehrbar. Eine Akquisition führt neue Definitionen und Systeme ein, eine Umstrukturierung verschiebt Zuständigkeitsgrenzen, und eine Plattformmigration erstellt manchmal Geschäftslogik an einem neuen Ort neu. Ein Überprüfungsrhythmus hält die BI-Strategie auf Kurs, wenn sich diese Bedingungen ändern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-measure-whether-a-bi-strategy-is-working":{"id":"title-v2-b2e7faf17d","additionalClasses":"anchor-title anchor-title--how-to-measure-whether-a-bi-strategy-is-working","type":"heading2","lines":["Wie man misst, ob eine BI-Strategie funktioniert"],":type":"snowflake-site/components/title-v2"},"text_how-to-measure-whether-a-bi-strategy-is-working_0":{"id":"text-1d73fa333a","text":"\u003Cp\u003EDie Messung funktioniert am besten, wenn sie frühe Signale der Akzeptanz von späteren Belegen für geschäftliche Auswirkungen trennt.\u003C/p\u003E\r\n\u003Ch3\u003EFrühindikatoren\u003C/h3\u003E\r\n\u003Cp\u003EFrühindikatoren zeigen, ob sich das BI-Betriebsmodell durchsetzt. Sie sind in der Regel früher sichtbar und können häufiger überprüft werden.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EAnteil der aktiv genutzten priorisierten BI-Assets\u003C/li\u003E\r\n\u003Cli\u003EZeit von der Frage bis zur brauchbaren Antwort\u003C/li\u003E\r\n\u003Cli\u003EAnteil der häufigen Fragen, die durch Self-Service mit Governance beantwortet werden\u003C/li\u003E\r\n\u003Cli\u003EAnzahl und Alter ungelöster Streitigkeiten über Metrikdefinitionen\u003C/li\u003E\r\n\u003Cli\u003EAusmusterungsrate für ungenutzte oder doppelte Assets\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ESpätindikatoren\u003C/h3\u003E\r\n\u003Cp\u003ESpätindikatoren betrachten, was sich geändert hat, nachdem BI in eine Entscheidung oder einen Prozess integriert wurde, sodass sie in der Regel ein längeres Überprüfungsfenster benötigen.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EÄnderung der Entscheidungszykluszeit\u003C/li\u003E\r\n\u003Cli\u003EKosten- oder Umsatzentwicklung im Zusammenhang mit einer dokumentierten BI-gestützten Entscheidung\u003C/li\u003E\r\n\u003Cli\u003EReporting-Aufwand, der von wiederkehrender Produktionsarbeit auf höherwertige Analysen verlagert wurde\u003C/li\u003E\r\n\u003Cli\u003EReduzierung der Zeit für Abschlüsse, Audits oder operative Überprüfungen\u003C/li\u003E\r\n\u003Cli\u003EVerbesserung des von der Strategie anvisierten Geschäftsprozesses\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ERichten Sie Kennzahlen an Geschäftszielen aus\u003C/h3\u003E\r\n\u003Cp\u003ESobald die Strategie ihre Indikatoren definiert hat, benötigen Teams eine Möglichkeit, diese wieder mit umfassenderen Geschäftszielen zu verknüpfen. Einige Unternehmen verwenden eine \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/strategy/balanced-scorecard/\"\u003EBalanced Scorecard\u003C/a\u003E, die Kennzahlen aus den Bereichen finanzielle Performance, Kund:innen, interne Prozesse sowie Lernen und Wachstum zusammenfasst. Andere nutzen \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/okr/\"\u003EOKRs\u003C/a\u003E, Executive Scorecards oder Planungs-Frameworks, die bereits im gesamten Unternehmen etabliert sind.\u003C/p\u003E\r\n\u003Cp\u003EDas Framework selbst ist den zugrunde liegenden Definitionen untergeordnet. Jede Kennzahl benötigt weiterhin eine vereinbarte Berechnung, eine verantwortliche Person, eine Quelle und einen Überprüfungsrhythmus, sodass derselbe \u003Ca href=\"https://www.snowflake.com/de/analytics/business-intelligence/kpi/\"\u003EKPI\u003C/a\u003E überall dasselbe bedeutet.\u003C/p\u003E\r\n\u003Cp\u003EStudien zu Data Leadership zeigen, wie wichtig klar definierte Ergebnisse sind. Die \u003Ca href=\"https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/cdo\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EChief Data Officer Study 2025 von IBM\u003C/a\u003E ergab, dass CDOs in Unternehmen, die höhere Renditen aus Daten- und KI-Investitionen melden, mit 25 % höherer Wahrscheinlichkeit klar artikulieren konnten, wie Datenprioritäten wichtige Geschäftsergebnisse unterstützen.\u003C/p\u003E\r\n\u003Cp\u003EMessungen erfordern auch eine Instrumentierung. Abfrage- und Asset-Nutzung, Support-Anfragen, Änderungen an semantischen Modellen, Baselines für Entscheidungszyklen und Geschäftsergebnisse befinden sich oft in unterschiedlichen Systemen. Die Entscheidung, wie diese Signale erfasst werden, gehört in die Strategie, bevor Teams sie für eine vierteljährliche Überprüfung benötigen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-measure-whether-a-bi-strategy-is-working_0":{"id":"text-6f2521936a","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EWählen Sie für jede prioritäre Entscheidung mindestens einen Frühindikator und einen Spätindikator. Zusammen zeigen sie, ob die Mitarbeitenden BI wie vorgesehen nutzen und ob sich der zugrunde liegende Geschäftsprozess verbessert.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_embedded-bi-extends-the-strategy-beyond-internal-users":{"id":"title-v2-58333a8f88","additionalClasses":"anchor-title anchor-title--embedded-bi-extends-the-strategy-beyond-internal-users","type":"heading2","lines":["Embedded BI erweitert die Strategie über interne Nutzer:innen hinaus"],":type":"snowflake-site/components/title-v2"},"text_embedded-bi-extends-the-strategy-beyond-internal-users_0":{"id":"text-94c89cc8d0","text":"\u003Cp\u003EEmbedded BI platziert analytische Informationen in einer Anwendung oder einem digitalen Produkt, das von Kund:innen, Partner:innen oder anderen externen Nutzer:innen verwendet wird. Sobald BI diese Grenze überschreitet, fließen zusätzliche Plattformanforderungen in die Strategie ein.\u003C/p\u003E\n\u003Cp\u003EMandantenisolierung und Zugriffskontrolle müssen über Kundengruppen hinweg funktionieren. Parallelität ist weniger vorhersehbar, da Produktteams nicht mehr kontrollieren, wann Nutzer:innen eintreffen, und die Erwartungen an die Aktualität oft dem Betriebsrhythmus der Anwendung folgen. Auch die Kostenzuordnung ändert sich: Das Verständnis der Rechenressourcen- und Analysekosten pro Kund:in oder Produktstufe wird für die Preis- und Margenanalyse nützlich.\u003C/p\u003E\n\u003Cp\u003EAuch die Verantwortlichkeiten weiten sich aus. Das Produktmanagement beteiligt sich an Entscheidungen über analytische Erlebnisse, Service-Level und Release-Reihenfolgen, während Daten- und Plattformteams weiterhin Definitionen, Zugriff und die zugrunde liegende Architektur verwalten.\u003C/p\u003E\n\u003Cp\u003EFür Unternehmen, die Embedded BI planen, sollten diese Anforderungen frühzeitig in Kapazitätsplanungs- und Governance-Diskussionen auftauchen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_where-bi-is-heading-and-what-ai-means-for-bi-strategy":{"id":"title-v2-029d747a84","additionalClasses":"anchor-title anchor-title--where-bi-is-heading-and-what-ai-means-for-bi-strategy","type":"heading2","lines":["Wohin sich BI entwickelt und was KI für die BI-Strategie bedeutet"],":type":"snowflake-site/components/title-v2"},"text_where-bi-is-heading-and-what-ai-means-for-bi-strategy_0":{"id":"text-489b33ff01","text":"\u003Cp\u003EBI geht seit Jahren über geplante Berichte und Dashboards hinaus, durch Self-Service-Analytics, Embedded Analytics und direkteren Zugriff auf Daten. \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/\"\u003EGenerative KI\u003C/a\u003E erweitert diese Entwicklung, indem sie Nutzer:innen neue Möglichkeiten bietet, Fragen zu stellen, Ergebnisse zu interpretieren und analytische Arbeit in mehrstufige Workflows zu integrieren.\u003C/p\u003E\n\u003Cp\u003EFür die BI-Strategie reichen die Auswirkungen bis in das Fundament unter diesen Anwendungen. Natürlichsprachliche und agentenbasierte Analytics hängen stark von gemeinsamen Geschäftsdefinitionen und einem Kontext mit zentraler Governance ab, während operative Anwendungsfälle unterschiedliche Anforderungen an Aktualität und Workload mit sich bringen. Sie werfen auch eine neue Messfrage auf: Wie gut interpretiert und beantwortet das System Geschäftsfragen, wenn Nutzer:innen nicht mehr viel von der Abfragelogik hinter dem Ergebnis sehen?\u003C/p\u003E\n\u003Ch3\u003ESteuern Sie die Semantik hinter KI-gestützter BI\u003C/h3\u003E\n\u003Cp\u003EBei einem Dashboard oder einer explorativen Analyse stoßen Nutzer:innen bei der Überprüfung eines Ergebnisses häufig auf Felder, Filter und Diagrammstrukturen. Eine dialogbasierte Schnittstelle verbirgt einen Großteil dieser Maschinerie und überträgt der semantischen Schicht mehr Verantwortung für die Bereitstellung der entsprechenden Metrikdefinition, Beziehung, Granularität, des Filterverhaltens und der Fachterminologie, bevor die Abfrage ausgeführt wird.\u003C/p\u003E\n\u003Cp\u003EGemeinsame semantische Definitionen tragen auch dazu bei, verschiedene BI-Anwendungen aufeinander abzustimmen. Dashboards, SQL-Workflows, dialogbasierte Schnittstellen und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EAgenten\u003C/a\u003E präsentieren Informationen möglicherweise unterschiedlich, beziehen sich aber dennoch auf dieselbe freigegebene Geschäftslogik.\u003C/p\u003E\n\u003Ch3\u003EPassen Sie Aktualität und Workload-Design an die Entscheidung an\u003C/h3\u003E\n\u003Cp\u003EDa BI immer weiter in operative Workflows vordringt, werden die Anforderungen an die Aktualisierung spezifischer. Ein monatlicher Planungsprozess, ein Executive Dashboard und eine Anwendung, die Entscheidungen im Laufe des Tages unterstützt, haben unterschiedliche Anforderungen an den Datenrhythmus.\u003C/p\u003E\n\u003Cp\u003EDie Strategie sollte die Aktualität entsprechend der zu unterstützenden Entscheidung definieren und dann die Rechenressourcen, Parallelität und Workload-Isolierung berücksichtigen, die für die Bereitstellung erforderlich sind. Schnellere Aktualisierungen erhöhen den Verarbeitungsaufwand und die Kosten. Die nützliche Frage ist also, wie aktuell die Daten für eine bestimmte Entscheidung sein müssen, und nicht, wie nah die Plattform an Echtzeit herankommen kann.\u003C/p\u003E\n\u003Cp\u003EDialogbasierte und agentenbasierte Workloads fügen eine weitere Variable hinzu: Die Nachfrage ist oft weniger vorhersehbar als geplante Dashboard-Aktualisierungen. Die Kapazitätsplanung muss daher sowohl etablierte Reporting-Muster als auch interaktive Workloads berücksichtigen, deren Timing von den Nutzer:innen bestimmt wird.\u003C/p\u003E\n\u003Ch3\u003EBewerten Sie Antworten und Geschäftsergebnisse\u003C/h3\u003E\n\u003Cp\u003EDer natürlichsprachliche Zugriff ändert auch, was BI-Teams testen müssen. Wenn Nutzer:innen direkt Fragen stellen, sollte die semantische Genauigkeit anhand repräsentativer Geschäftsfragen bewertet werden, wobei gegebenenfalls erwartete Antworten oder SQL definiert werden. Änderungen an Metriken, Beziehungen oder anderer semantischer Logik sollten eine weitere Bewertungsrunde auslösen.\u003C/p\u003E\n\u003Cp\u003EAuf Programmebene sollte die Messung über die Genauigkeit der Antworten hinausgehen. Decision Intelligence lenkt die Aufmerksamkeit darauf, was nach der Bereitstellung einer Erkenntnis geschah: ob eine Entscheidung schneller getroffen wurde, ob ein Workflow weniger manuelle Analysen erforderte und ob sich der von der Strategie anvisierte Geschäftsprozess verbesserte.\u003C/p\u003E\n\u003Cp\u003EDiese Kennzahlen bauen auf den Früh- und Spätindikatoren auf, die zuvor in der BI-Strategie festgelegt wurden. Während sich die Schnittstellen ändern, bleibt das zugrunde liegende Ziel bestehen: Menschen freigegebene analytische Informationen in einer Form und in einem Rhythmus bereitzustellen, die für die Entscheidung, die sie treffen müssen, geeignet sind.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-snowflake-supports-a-bi-strategy":{"id":"title-v2-8ce2c6eacf","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-a-bi-strategy","type":"heading2","lines":["Wie Snowflake eine BI-Strategie unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-a-bi-strategy_0":{"id":"text-947066c4ff","text":"\u003Cp\u003ESnowflake unterstützt mehrere der operativen Entscheidungen im Zentrum einer BI-Strategie, einschließlich der Frage, wo gemeinsame Definitionen angesiedelt sind, wie Workloads organisiert sind und wie sich Analytics mit zentraler Governance auf natürlichsprachliche Anwendungen erstrecken.\u003C/p\u003E\n\u003Ch3\u003EHalten Sie Geschäftsdefinitionen konsistent\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/engineering/native-semantic-views-ai-bi/\"\u003ESnowflake Semantic Views\u003C/a\u003E speichern Metriken, Dimensionen, Beziehungen und andere Geschäftslogik direkt in Snowflake und geben Dashboards, SQL-Workflows und KI-gestützten Schnittstellen Zugriff auf dieselben zentral festgelegten Definitionen. \u003Ca href=\"https://www.snowflake.com/de/product/features/horizon-context/\"\u003ESnowflake Horizon Context\u003C/a\u003E fügt Geschäftskontext, Metadaten und Datenherkunft hinzu und hilft Teams dabei, diese Definitionen zu entdecken und konsistent anzuwenden.*\u003C/p\u003E\n\u003Cp\u003EDieser Ansatz unterstützt auch dialogbasierte Analytics. \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-analyst/\"\u003ECortex Analyst\u003C/a\u003E verwendet Semantic Views, um Geschäftsfragen zu interpretieren und SQL zu generieren, sodass der natürlichsprachliche Zugriff auf derselben Geschäftslogik basiert, die auch an anderer Stelle in der Analytics-Umgebung verwendet wird.\u003C/p\u003E\n\u003Ch3\u003ERichten Sie Rechenressourcen am Betriebsmodell aus\u003C/h3\u003E\n\u003Cp\u003ESeparate virtuelle Warehouses von Snowflake bieten Teams unabhängige Rechenressourcen, während sie weiterhin mit gemeinsamen Daten mit zentraler Governance arbeiten. Dies unterstützt zentralisierte, föderierte und Hub-and-Spoke-BI-Modelle, ohne dass jedes Team oder jeder Workload um dieselbe Rechenressourcenkapazität konkurrieren muss.\u003C/p\u003E\n\u003Cp\u003ENutzungs-, Abfrage- und Kostentelemetrie liefert BI-Teams auch Belege für die zuvor beschriebenen Messpraktiken. Teams können Abfrageaktivität, Latenz, Workload-Verbrauch und Objektnutzung untersuchen und diese Signale dann mit Geschäftskennzahlen wie Entscheidungszeit oder Prozess-Performance kombinieren.\u003C/p\u003E\n\u003Ch3\u003EErweitern Sie BI mit zentraler Governance auf neue Schnittstellen\u003C/h3\u003E\n\u003Cp\u003EWährend BI auf dialogbasierte und agentenbasierte Workflows ausgeweitet wird, hält Snowflake Governance und semantischen Kontext nah an den zugrunde liegenden Daten. \u003Ca href=\"https://www.snowflake.com/de/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E bietet eine dialogbasierte Schnittstelle für Unternehmensdaten, die Funktionen wie Cortex Analyst und Semantic Views nutzt und gleichzeitig bestehende Snowflake-Zugriffskontrollen und Maskierungsrichtlinien anwendet.\u003C/p\u003E\n\u003Cp\u003EDashboards, Self-Service-Analytics und KI-Schnittstellen arbeiten konsistenter, wenn sie zentral festgelegte Definitionen, Zugriffskontrollen und eine beobachtbare Datengrundlage gemeinsam nutzen, anstatt diese Logik in jeder Anwendung separat neu zu erstellen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_keep-the-strategy-close-to-the-decisions":{"id":"title-v2-cc54f5dedf","additionalClasses":"anchor-title anchor-title--keep-the-strategy-close-to-the-decisions","type":"heading2","lines":["Halten Sie die Strategie nah an den Entscheidungen"],":type":"snowflake-site/components/title-v2"},"text_keep-the-strategy-close-to-the-decisions_0":{"id":"text-2b6f28c0fe","text":"\u003Cp\u003EBI wird weiterhin neue Schnittstellen aufnehmen. Dashboards koexistieren jetzt mit Embedded Analytics, natürlichsprachlichen Fragen und Agenten, und die nächste Schnittstelle wird viele der gleichen Anforderungen erben.\u003C/p\u003E\n\u003Cp\u003EWenn ein Unternehmen weiß, welche Entscheidungen es verbessern möchte, wer für die Definitionen verantwortlich ist, wie freigegebene Antworten die Nutzer:innen erreichen und welche Belege den Fortschritt zeigen, haben Technologie-Entscheidungen klare Anforderungen zu erfüllen. Die Strategie hat dann etwas Konkretes, das sie überprüfen kann, wenn sich diese Technologien – und das Unternehmen selbst – ändern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_keep-the-strategy-close-to-the-decisions_0":{"id":"text-4375e5ea2d","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EBeginnen Sie mit den Entscheidungen, die das Unternehmen verbessern möchte, und arbeiten Sie sich dann nach außen zu Definitionen, Verantwortlichkeiten, Bereitstellungsmodell und Messung vor. 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