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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-v2-3e5640ee90","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Data Governance: Was es ist und warum Sie es brauchen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-6b833c6dbc","additionalClasses":"hub-hero__subheadline","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EDieser Leitfaden erläutert die Frameworks, Prinzipien und praktischen Schritte, die erforderlich sind, um Daten vertrauenswürdig, überprüfbar und skalierbar zu machen – auch in KI-gesteuerten Umgebungen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","gridClassNames":"aem-Grid aem-Grid--12 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Sharipova","personTitle":"Lokale Redakteurin, Snowflake","layoutStyle":"horizontal","containerStyle":"left-aligned","avatar":{"id":"avatar","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f7c921fe-d10f-4d90-ac38-c79aff7cf132/gulnaz-photo.jpg?quality=85&preferwebp=true","alt":"Gulnaz Sharipova","lazyEnabled":true,"width":"512",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/person-chip"}},":itemsOrder":["person_chip_343411615","person_chip"]}},":itemsOrder":["text_1842995591","title_v2","text","container"]},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","id":"container-882768e608","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-63c144eb25","youtubeVideoId":"bF6FAMeGEZc","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_205440979":{"id":"flexible-column-container-1c4ce03cc3","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container","id":"container-b1569c60f9","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-b91646680f","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EData Governance im Überblick:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-516469655c","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/\"\u003EData Governance-Frameworks\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/models/\"\u003EData Governance-Modelle\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-privacy/\"\u003EDatenschutz\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-lineage/\"\u003EDatenherkunft\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/\"\u003EDatenrichtlinien\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-catalog/\"\u003EDatenkatalog\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/ai/\"\u003EData Governance für KI\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"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false},"flexible_column_cont_549182334":{"id":"flexible-column-container-c51e777aae","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","id":"hub-body-content","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout_what-is-data-governance_0":{"id":"text-20223501f2","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDATA GOVERNANCE DEFINITION\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EData Governance ist das Framework von Richtlinien, Rollen, Prozessen und Technologien, das sicherstellt, dass Daten über ihren gesamten Lebenszyklus hinweg konsistent und verantwortungsvoll verwaltet, geschützt und genutzt werden. Dies umfasst Eigentümerschaft, Zugriff, Qualität, Klassifizierung und Überprüfbarkeit.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_overview_0":{"id":"text-675ef4a103","text":"\u003Cp\u003EWenn bei Daten etwas schiefgeht – ein Compliance-Verstoß, ein Modell, das mit der falschen Population trainiert wurde, eine Metrik, die von verschiedenen Teams unterschiedliche interpretiert wird –, können Unternehmen die darauf folgenden Fragen oft nicht beantworten. Die Eigentümerschaft ist unklar, die Datenherkunft (Data Lineage) ist unvollständig, Zugriffsprotokolle existieren nicht oder reichen nicht weit genug zurück. Das Problem wird erst durch eine Aufsichtsbehörde oder bei einer Überprüfung nach einem Vorfall sichtbar.\u003C/p\u003E\r\n\u003Cp\u003EData Governance ist die wiederkehrende Praxis, Eigentümerschaft, Klassifizierung, Datenherkunft und Audit-Kontrollen so aufzubauen, dass diese Antworten verfügbar sind, noch bevor sie benötigt werden. Da sich Daten zunehmend über verschiedene Systeme, Clouds, Partner und KI-Workflows hinwegfließen, steigen die Anforderungen an die Governance kontinuierlich. In den meisten Unternehmen ist es viel schwieriger als es sein sollte, verlässliche Antworten auf Datenfragen zu liefern. Dieser Leitfaden zeigt, wie Sie das ändern können.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-data-governance":{"id":"title-v2-aef87757e4","additionalClasses":"anchor-title anchor-title--what-is-data-governance","type":"heading2","lines":["Was ist Data Governance?"],":type":"snowflake-site/components/title-v2"},"text_what-is-data-governance_0":{"id":"text-b209e18186","text":"\u003Cp\u003EData Governance ist das System von Richtlinien, Rollen, Prozessen und Technologien, das definiert, wie ein Unternehmen Daten über ihren gesamten Lebenszyklus hinweg verwaltet. Sie legt fest, wem die Daten gehören, was sie bedeuten, wie sie klassifiziert werden, wer darauf zugreifen kann, wie die Datenqualität gemessen und wie die Nutzung auditiert wird. In der Praxis ist Data Governance ein Betriebsmodell, um Daten in großem Maßstab vertrauenswürdig, geschützt und nutzbar zu machen.\u003C/p\u003E\n\u003Cp\u003EEin ausgereiftes Data Governance-Programm beantwortet vier Fragen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EWelche Daten existieren und was bedeuten sie?\u003C/li\u003E\n\u003Cli\u003EWem gehören sie und wer trägt die Verantwortung für ihre Nutzung?\u003C/li\u003E\n\u003Cli\u003EWer darf auf sie zugreifen, sie teilen oder in KI-Workflows verwenden?\u003C/li\u003E\n\u003Cli\u003EKann das Unternehmen belegen, wie sie transformiert, geschützt und genutzt wurden?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDie Beantwortung dieser Fragen hängt von Metadaten, Data Stewardship, Datenqualität, Datenschutzkontrollen, Compliance-Prozessen und klaren Verantwortlichkeiten ab. Ohne das Zusammenspiel dieser Elemente können Teams keine zuverlässige Data Governance gewährleisten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-data-governance-matters-now":{"id":"title-v2-6eb2274cc2","additionalClasses":"anchor-title anchor-title--why-data-governance-matters-now","type":"heading2","lines":["Warum Data Governance jetzt wichtiger denn je ist"],":type":"snowflake-site/components/title-v2"},"text_why-data-governance-matters-now_0":{"id":"text-e1a5bd9835","text":"\u003Cp\u003EData Governance ist schwieriger geworden, da Daten nicht mehr in einer einzigen Reporting-Umgebung isoliert sind. Eine Produktnutzungstabelle speist möglicherweise Analytics, Kundensupport-Workflows, das Partner-Reporting, Machine-Learning-Modelle und Executive-Dashboards. Eine \u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/\"\u003EData Governance-Richtlinie\u003C/a\u003E, die für ein Dashboard funktioniert, greift nicht automatisch für jede nachgelagerte Kopie, Transformation oder jeden KI-Prompt, der auf dieselben Daten zugreift.\u003C/p\u003E\n\u003Cp\u003EDiese Fragmentierung ist eines der größten Hindernisse für eine effektive Data Governance und skalierbare KI. „Wenn Daten für unterschiedliche Zwecke an verschiedenen Orten verteilt sind, wird es extrem schwierig, diese Daten zu kontrollieren und zu sichern”, sagt Baris Gutelkin, VP of Product, AI bei Snowflake. Er argumentiert, dass die Investition in eine einzige, standardisierte Datenbasis im gesamten Unternehmen leistungsfähigere Anwendungsfälle für generative KI ermöglicht und gleichzeitig Data Governance und Sicherheit vereinfacht.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-data-governance-matters-now_0":{"id":"quote-item-9271431577","alignment":"left","layout":"simple","quoteSourceName":"Baris Gutelkin","quoteSourceTitle":"VP of Product, AI bei Snowflake","showQuoteIcon":true,"quote":"Wenn Daten für einen Zweck an einem Ort und für einen anderen Zweck an einem anderen Ort liegen, wird es extrem schwierig, diese Daten zu kontrollieren und zu sichern","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-data-governance-matters-now_1":{"id":"text-adad124332","text":"\u003Cp\u003EDamit eine solche Datenbasis in der Praxis funktioniert, reicht es nicht aus, Daten zu zentralisieren – es bedarf einer konsistenten, skalierbaren Governance. Teams müssen sensible Datenfelder klassifizieren, Eigentümerschaften zuweisen, die Datenherkunft verfolgen, Maskierungen durchsetzen, die Aktualität überwachen und die Nutzung überprüfen, ohne sich auf Silowissen oder unzusammenhängende Tabellenkalkulationen verlassen zu müssen. Eine gut umgesetzte Governance macht vertrauenswürdige Daten leichter auffindbar und sicherer in der Nutzung – ohne zusätzliche Hürden für die Teams, die sie benötigen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-data-governance-matters-now_0":{"id":"text-b0a47ed4da","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EHÄUFIGE FALLE\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EUnternehmen behandeln Data Governance oft als einmaliges Projekt und nicht als fortlaufende operative Disziplin, die in die täglichen Workflows eingebettet ist. Dies führt zu unklaren Zuständigkeiten, unvollständigen Metadaten und Kontrollen, die sich bei der Datenübertragung zwischen Systemen und KI-Anwendungsfällen nicht automatisch übertragen – was die Rückverfolgung von Problemen und das Bestehen von Audits erschwert.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_data-governance-for-ai":{"id":"title-v2-b843ddd92c","additionalClasses":"anchor-title anchor-title--data-governance-for-ai","type":"heading2","lines":["Data Governance für KI"],":type":"snowflake-site/components/title-v2"},"text_data-governance-for-ai_0":{"id":"text-94fe223927","text":"\u003Cp\u003EKI erhöht die Anforderungen an die Data Governance, da kontrollierte Daten nun von Modellen und Agenten abgerufen, zusammengefasst, transformiert oder verarbeitet werden können. Wenn ein KI-Agent im Namen von Nutzer:innen handelt, häufen sich die Fragen: Welche Datenzeilen hat er abgerufen, was hat er in einen Prompt aufgenommen und gibt es für all das einen Audit-Trail?\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/ai/\"\u003EData Governance für KI\u003C/a\u003E konzentriert sich auf die Daten, die von KI-Systemen verwendet werden. Dazu gehören die Herkunft der Trainingsdaten, die Datenklassifizierung von PII und sensiblen Daten, Kontrollen für Einwilligungen und zulässige Nutzung, Bias und die Repräsentativität von Quelldaten, die Datenherkunft zwischen Datensets, Features, Prompts, Outputs und nachgelagerten Entscheidungen sowie Zugriffskontrollen für Agenten und Audit-Trails für Abrufe, den Prompt-Kontext und generierte Outputs.\u003C/p\u003E\r\n\u003Cp\u003EDies steht in engem Zusammenhang mit \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003EKI-Governance\u003C/a\u003E, ist aber nicht dasselbe. Data Governance für KI kontrolliert die Daten, die KI-Systeme verwenden. KI-Governance kontrolliert das Modell oder System selbst: Modellfreigaben, Evaluierungen, Model Cards, Monitoring, Drift, menschliche Aufsicht und \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-risk-management/\"\u003ERisikomanagement\u003C/a\u003E. Die beiden Programme müssen miteinander verknüpft werden. Ein risikoreicher KI-Workflow kann nicht effektiv gesteuert werden, wenn das Unternehmen nicht weiß, aus welchen Datenquellen er gespeist wird, welche sensiblen Datenfelder beim Abruf auftauchen können, wer die Datennutzung genehmigt hat und ob sich die Outputs zu kontrollierten Quellen zurückverfolgen lassen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EErfahren Sie, wie Snowflake dabei hilft, Daten für vertrauenswürdige KI zu verwalten:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-for-ai_0":{"id":"embed-b8ced2cb89","youtubeVideoId":"42GWL5x3Xpg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_data-governance-principles":{"id":"title-v2-18a68b415d","additionalClasses":"anchor-title anchor-title--data-governance-principles","type":"heading2","lines":["Prinzipien der Data Governance"],":type":"snowflake-site/components/title-v2"},"text_data-governance-principles_0":{"id":"text-317a5fa638","text":"\u003Cp\u003EPrinzipien der Data Governance leiten die Entscheidungen eines Programms in Bezug auf Richtlinien, Ownership, Technologien und Prozesse. Zu den gängigen Prinzipien gehören:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EVerantwortlichkeit:\u003C/b\u003E Jedes kritische Daten-Asset hat einen benannten Owner, der für Zugriffsentscheidungen, Qualitätsprobleme und Definitionskonflikte verantwortlich ist.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETransparenz:\u003C/b\u003E Nutzer:innen können Definitionen, Herkunft, Qualitätssignale und den Richtlinienkontext für die Daten-Assets einsehen, mit denen sie arbeiten.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDatenqualität:\u003C/b\u003E Kontrollierte Daten werden an expliziten Erwartungen gemessen und nicht einfach als zuverlässig vorausgesetzt.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDatenschutz und Sicherheit:\u003C/b\u003E Sensible Daten werden während ihres gesamten Lebenszyklus klassifiziert, geschützt und überwacht.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStewardship:\u003C/b\u003E Benannte Stewards pflegen Definitionen, beheben Probleme und unterstützen die verantwortungsvolle Nutzung über alle Domänen hinweg.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStandardisierung:\u003C/b\u003E Begriffe, Richtlinien und Kontrollen sind domänenübergreifend konsistent, wobei Ausnahmen dokumentiert und genehmigt werden.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditfähigkeit:\u003C/b\u003E Das Unternehmen kann nachweisen, wie auf Daten zugegriffen wurde und wie sie geändert, geteilt und genutzt wurden.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEthische Nutzung:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/data-ethics/\"\u003EDatenethik\u003C/a\u003E bedeutet, dass Daten auf faire, diskriminierungsfreie und den Erwartungen der Nutzer:innen entsprechende Weise verwendet werden und Mechanismen zur Erkennung und Eindämmung schädlicher oder unbeabsichtigter Folgen unterstützt\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EDiese Prinzipien spiegeln sich in konkreten Mechanismen wider: Ownership-Felder in einem Katalog, Sensibilitäts-Tags für Datenfelder, Maskierungsrichtlinien für regulierte Daten, Datenherkunftspfade für kritische Berichte und Audit-Logs für Zugriffsüberprüfungen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-frameworks-and-standards":{"id":"title-v2-3417b9f46c","additionalClasses":"anchor-title anchor-title--data-governance-frameworks-and-standards","type":"heading2","lines":["Frameworks und Standards für Data Governance"],":type":"snowflake-site/components/title-v2"},"text_data-governance-frameworks-and-standards_0":{"id":"text-5a33467085","text":"\u003Cp\u003EData Governance-Frameworks und -Standards helfen Unternehmen dabei, ein Programm zu strukturieren, Fähigkeiten zu definieren und zu entscheiden, was zuerst implementiert werden soll. Einige konzentrieren sich auf den Reifegrad des Datenmanagements, während andere den Schwerpunkt auf IT-Governance, Architektur, Qualität oder Cloud-Kontrollen legen.\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework oder Standard\u003C/th\u003E\n\u003Cth\u003EAm besten geeignet für\u003C/th\u003E\n\u003Cth\u003EWas damit gesteuert wird\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EUmfassendes Design von Datenmanagement-Programmen\u003C/td\u003E\n\u003Ctd\u003EWissensbereiche, Rollen und Disziplinen des Datenmanagements\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EReifegrad des Enterprise-Datenmanagements\u003C/td\u003E\n\u003Ctd\u003EBetriebsmodell, Kontrollen, Verantwortlichkeit und Reifegrad\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003ECloud-Datenmanagement-Kontrollen\u003C/td\u003E\n\u003Ctd\u003EGovernance-Kontrollen für Cloud- und hybride Datenumgebungen\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EAusrichtung der IT-Governance\u003C/td\u003E\n\u003Ctd\u003ERisiko, Kontrollen, Verantwortlichkeit und Enterprise-Governance\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EAusrichtung der Unternehmensarchitektur\u003C/td\u003E\n\u003Ctd\u003EDatenarchitektur, Anwendungsabhängigkeiten und Architektur-Governance\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/fair-principles/\"\u003EFAIR-Prinzipien\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EWiederverwendung von wissenschaftlichen Daten und Forschungsdaten\u003C/td\u003E\n\u003Ctd\u003EAuffindbarkeit, Zugänglichkeit, Interoperabilität und Wiederverwendbarkeit\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.iso.org/standard/81745.html\" target=\"_blank\"\u003EISO 8000\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EDatenqualität und Stammdaten\u003C/td\u003E\n\u003Ctd\u003EQualitätsanforderungen, Datenaustausch und Stammdaten-Prozesse\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://datagovernance.com/the-dgi-data-governance-framework/\" target=\"_blank\"\u003EDGI Data Governance Framework\u003C/a\u003E\u003C/td\u003E\n\u003Ctd\u003EDesign von Governance-Programmen\u003C/td\u003E\n\u003Ctd\u003EEntscheidungsrechte, Verantwortlichkeit und Richtlinienprozesse\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EEine Gesundheitsorganisation könnte DAMA-DMBOK nutzen, um grundlegende Datenmanagement-Fähigkeiten zu definieren, CDMC, um Cloud-Kontrollen abzubilden, und die Data-Governance-Anforderungen von HIPAA, um Erwartungen an Zugriff, Aufbewahrung und Audits festzulegen.\u003C/p\u003E\n\u003Cp\u003EFrameworks bieten Struktur, aber ein Programm benötigt dennoch Verantwortliche, Metadaten, Klassifizierung, Qualitätsregeln, Zugriffsrichtlinien, Audit-Prozesse und Technologien, um diese Entscheidungen dort umzusetzen, wo Daten genutzt werden.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/frameworks/\"\u003EErfahren Sie mehr über Data Governance-Frameworks →\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-operating-models":{"id":"title-v2-339f830417","additionalClasses":"anchor-title anchor-title--data-governance-operating-models","type":"heading2","lines":["Betriebsmodelle für Data Governance"],":type":"snowflake-site/components/title-v2"},"text_data-governance-operating-models_0":{"id":"text-29813d67a2","text":"\u003Cp\u003EEin Governance-Programm benötigt ein Betriebsmodell, das zur Arbeitsweise des Unternehmens passt. Ein globales Unternehmen mit Dutzenden von Geschäftsbereichen kann nicht jede Tabelle durch ein zentrales Team verwalten, aber ein vollständig dezentrales Modell könnte zu inkonsistenten Definitionen, doppelten Richtlinien und inkonsistenter Kontrolle führen.\u003C/p\u003E\r\n\u003Cp\u003EDie meisten Unternehmen entscheiden sich für eines von drei Modellen:\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EModell\u003C/th\u003E\r\n\u003Cth\u003ESo funktioniert es\u003C/th\u003E\r\n\u003Cth\u003EAm besten geeignet für\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EZentralisiert\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEin zentrales Governance-Team definiert Richtlinien, Standards und Freigaben\u003C/td\u003E\r\n\u003Ctd\u003EKleinere Programme, stark regulierte Daten oder Governance in der Frühphase\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EFöderiert\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDomänen verwalten ihre Daten lokal, befolgen aber gemeinsame Governance-Standards\u003C/td\u003E\r\n\u003Ctd\u003EGroße Unternehmen mit ausgeprägtem Domänen-Ownership\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EHybrid\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEin zentrales Team legt Richtlinien und Plattformstandards fest, während die Domänen das tägliche Stewardship übernehmen\u003C/td\u003E\r\n\u003Ctd\u003EDie ausgereiftesten Unternehmensprogramme\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EEin hybrides Modell ist oft am praktischsten. Ein zentrales Team definiert Klassifizierungsstandards, Richtlinienvorlagen, Kataloganforderungen und Audit-Vorgaben. Domänenteams sind für ihre Datenprodukte verantwortlich, pflegen Definitionen, beheben Qualitätsprobleme und genehmigen den Zugriff basierend auf dem lokalen Kontext.\u003C/p\u003E\r\n\u003Cp\u003EUnabhängig davon, welches Modell ein Unternehmen verwendet, müssen die Entscheidungsrechte explizit festgelegt sein. Wenn zwei Teams den Begriff „aktive:r Kund:in“ unterschiedlich definieren, sollte das Governance-Modell festlegen, wer den Konflikt auflöst. Wenn ein Partner Zugriff auf ein sensibles Dataset anfordert, sollte das Modell festlegen, wer die Anfrage genehmigt, welche Nachweise erforderlich sind und wie die Entscheidung protokolliert wird.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_data-governance-operating-models_0":{"id":"card-v2-b4e04cd2bd","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/town-of-gilbert/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Customer Story lesen"},":type":"snowflake-site/components/card-v2","layoutStyle":"horizontal","type":"content-card","image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","alt":"Town of Gilbert Logo","lazyEnabled":true,"width":"624",":type":"snowflake-site/components/image"},"text":{"id":"text","text":"\u003Cp\u003EDie Town of Gilbert hat mit Snowflake 11 öffentlich zugängliche Dashboards eingeführt, um die Transparenz bei Bürgerdiensten wie Kriminalprävention, psychologischer Nothilfe, Schlaglochreparaturen und Recycling zu verbessern. Die Stadt nutzt Governance- und Sicherheitsfunktionen wie rollenbasierte Zugriffskontrolle, Metadaten-Felder und Dynamic Data Masking, um den vertrauenswürdigen Zugriff auf sensible Daten des öffentlichen Sektors zu unterstützen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title":{"id":"title","type":"heading4","lines":["Customer Story: Town of Gilbert"],":type":"snowflake-site/components/title-v2"}},"title_the-core-components-of-data-governance":{"id":"title-v2-b969750586","additionalClasses":"anchor-title anchor-title--the-core-components-of-data-governance","type":"heading2","lines":["Die Kernkomponenten der Data Governance"],":type":"snowflake-site/components/title-v2"},"text_the-core-components-of-data-governance_0":{"id":"text-d198104439","text":"\u003Cp\u003EEin Data Governance-Programm basiert auf Prinzipien und Frameworks, wird aber durch spezifische operative Komponenten umgesetzt. Diese Komponenten machen Governance in den Systemen sichtbar, die täglich genutzt werden: Kataloge, Tags, Lineage-Diagramme, Zugriffsrichtlinien, Qualitätsprüfungen, Stewardship-Workflows und Audit-Logs.\u003C/p\u003E\r\n\u003Ch3\u003EMetadatenmanagement\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/metadata-management/\"\u003EMetadaten\u003C/a\u003E liefern den Kontext, der Personen und Systemen erklärt, was ein Daten-Asset ist, woher es stammt und wie es verwendet werden sollte. Sie können Tabellennamen, Spaltentypen, Owner, Geschäftsdefinitionen, Vertraulichkeitskennzeichnungen, Aktualitätsziele, Datenherkunftspfade, Nutzungsmuster oder Kostenprofile beschreiben.\u003C/p\u003E\r\n\u003Cp\u003EDie meisten Data Governance-Programme stützen sich auf drei Arten von Metadaten:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EBusiness-Metadaten\u003C/b\u003E umfassen Definitionen, Owners, Domänen, Glossarbegriffe und Zertifizierungsstatus – sie helfen Teams zu verstehen, ob ein Daten-Asset relevant und für die Nutzung freigegeben ist.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETechnische Metadaten\u003C/b\u003E umfassen Schemata, Datentypen, Transformationen, Abhängigkeiten und Datenherkunft (Data Lineage) – sie helfen Engineers und Architekt:innen zu verstehen, wie Daten fließen und sich verändern.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EOperative Metadaten\u003C/b\u003E umfassen Aktualität, Nutzung, Kosten, Qualitätsergebnisse und Zugriffsmuster – sie helfen Teams zu überwachen, ob Daten aktuell und vertrauenswürdig sind und angemessen genutzt werden.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/vs-technical-governance/\"\u003EErfahren Sie mehr über den Unterschied zwischen Data Governance und technischer Governance →\u003C/a\u003E\u003C/p\u003E\r\n\u003Ch3\u003EDatenklassifizierung (Data Classification)\u003C/h3\u003E\r\n\u003Cp\u003EBei der Datenklassifizierung werden Daten anhand ihrer Sensitivität, ihrer Domäne, gesetzlicher Vorschriften (einschließlich der \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-data-sovereignty/\"\u003EDatensouveränität\u003C/a\u003E) oder ihrer zulässigen Nutzung Labels versehen. Beispielsweise kann ein Datenfeld als PII, geschützte Gesundheitsdaten, Zahlungskartendaten, vertrauliche Finanzdaten oder genehmigte Trainingsdaten getaggt werden. Diese Labels steuern dann Zugriffsüberprüfungen, Maskierungsrichtlinien, Aufbewahrungsregeln, Freigabegenehmigungen und Einschränkungen für die KI-Nutzung.\u003C/p\u003E\r\n\u003Cp\u003EDie Klassifizierung ist besonders wichtig, da sensible Daten selten isoliert an einem einzigen Ort liegen. E-Mail-Adressen, Kunden-IDs, Diagnosecodes, Geodatenfelder und Transaktionsdetails fließen oft über Pipelines, Dashboards und Anwendungstabellen. Ein Data Governance-Programm muss diese Felder identifizieren, bevor es sie konsequent schützen kann.\u003C/p\u003E\r\n\u003Ch3\u003EDatenkatalog\u003C/h3\u003E\r\n\u003Cp\u003EEin \u003Ca href=\"https://www.snowflake.com/de/data-governance/data-catalog/\"\u003EDatenkatalog\u003C/a\u003E ist das durchsuchbare Inventar, das Governance erst praktisch nutzbar macht. Er bietet Analyst:innen, Engineers, Stewards und Geschäftsanwender:innen einen zentralen Ort, um Daten-Assets zu finden, Definitionen zu lesen, die Datenherkunft zu überprüfen, Eigentümer:innen zu ermitteln, Qualitätssignale zu prüfen und Zugriff anzufordern.\u003C/p\u003E\r\n\u003Cp\u003EModerne Kataloge zeigen zertifizierte Datenprodukte an, fügen Richtlinienkontext hinzu, zeigen, ob eine Tabelle aktuell genug für die Nutzung ist, und helfen Teams, die Duplizierung ähnlicher Datasets zu vermeiden. Ein guter Katalog beantwortet praktische Fragen, bevor jemand eine Abfrage schreibt: Was bedeutet diese Tabelle? Wer ist dafür verantwortlich? Ist sie genehmigt? Welche nachgelagerten Assets hängen von ihr ab? Enthält sie sensible Daten?\u003C/p\u003E\r\n\u003Ch3\u003EDatenherkunft (Data Lineage)\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-lineage/\"\u003EDie Datenherkunft\u003C/a\u003E verfolgt Daten von der Quelle bis zur Nutzung. Sie zeigt, wie sich ein Datenfeld, eine Tabelle oder eine Metrik durch Ingestion, Transformation, Modellierung, Reporting, Sharing und KI-Workflows bewegt. Die Datenherkunft kann auf Tabellenebene ansetzen, um aufzuzeigen, wie Tabellen von anderen Tabellen oder Quellen abhängen; auf Feldebene, um zu veranschaulichen, wie bestimmte Datenfelder transformiert oder wiederverwendet werden; oder systemübergreifend, um darzustellen, wie Daten über Tools, Clouds oder Plattformen hinweg fließen.\u003C/p\u003E\r\n\u003Cp\u003EWenn ein reguliertes Datenfeld in einen Bericht, ein Modell oder ein externes Datenprodukt einfließt, hilft die Datenherkunft dabei aufzuzeigen, woher sie stammt, wie sie sich verändert hat und was betroffen sein könnte, wenn sich die Quelle ändert.\u003C/p\u003E\r\n\u003Ch3\u003ERichtlinienmanagement\u003C/h3\u003E\r\n\u003Cp\u003EIm Richtlinienmanagement werden Data Governance-Regeln in durchsetzbare Kontrollen transformiert. Es umfasst Zugriffsrichtlinien, Maskierungsrichtlinien, Einschränkungen auf Zeilenebene, Aufbewahrungsregeln, Data-Sharing-Regeln, Richtlinien zur zulässigen Nutzung und Ausnahme-Workflows.\u003C/p\u003E\r\n\u003Cp\u003EEine Richtlinie sollte festlegen, wer unter welchen Bedingungen, zu welchem Zweck und mit welchem Überprüfungsprozess auf welche Daten zugreifen kann. Ein effektives Richtlinienmanagement schließt auch Ausnahmen ein: Einige Nutzer:innen benötigen möglicherweise temporären Zugriff für ein Audit, eine Datenmigration oder die Reaktion auf Vorfälle. Die Governance sollte erfassen, wer die Ausnahme genehmigt hat, warum sie gewährt wurde und wann sie abläuft.\u003C/p\u003E\r\n\u003Ch3\u003EDatenqualität\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/a-practical-guide-to-data-quality/\"\u003EDatenqualität\u003C/a\u003E bestimmt, ob Daten genau, vollständig, konsistent, aktuell, eindeutig und valide genug für ihren vorgesehenen Verwendungszweck sind. Eine Produkttabelle, die für interne Experimente verwendet wird, kann andere Qualitätsanforderungen haben als eine Umsatztabelle für die Finanzberichterstattung oder eine Schadensfalltabelle für Analysen im Gesundheitswesen.\u003C/p\u003E\r\n\u003Cp\u003EEine Tabelle kann eine:n Eigentümer:in, eine Glossardefinition und eine Zugriffsrichtlinie haben, aber wenn ihre Datensätze veraltet oder unvollständig sind, können sich die Nutzer:innen nicht darauf verlassen. Moderne Programme verlagern die Qualität durch Datenverträge, Pipeline-Tests und kontinuierliches Monitoring in eine frühere Phase des Lebenszyklus.\u003C/p\u003E\r\n\u003Ch3\u003EDatenschutz und Sicherheit\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-privacy/\"\u003EDer Datenschutz\u003C/a\u003E regelt, wie personenbezogene und sensible Daten erfasst, genutzt, aufbewahrt, geteilt und gelöscht werden. Datensicherheit regelt, wie Daten vor unbefugtem Zugriff, Missbrauch oder Offenlegung geschützt werden. Beide hängen von Klassifizierung, Eigentümerschaft, Richtlinien und Überprüfbarkeit ab, weshalb sie in der Regel innerhalb desselben Governance-Frameworks verwaltet werden.\u003C/p\u003E\r\n\u003Cp\u003EDatenschutzkontrollen können das Einwilligungsmanagement, Workflows für Betroffenenanfragen, Aufbewahrungsregeln, Tokenisierung und Datenmaskierung umfassen. Sicherheitskontrollen können rollenbasierte Zugriffskontrolle, Row Access Policies, Verschlüsselung, Monitoring und Verfahren zur Reaktion auf Vorfälle umfassen. Die Data Governance verbindet diese Kontrollen mit Daten-Assets. Sie zeigt auf, welche Tabellen sensible Daten enthalten, wer darauf zugreifen kann, welche Richtlinien gelten und ob die Nutzung später überprüft werden kann.\u003C/p\u003E\r\n\u003Ch3\u003EData Sharing und Collaboration\u003C/h3\u003E\r\n\u003Cp\u003EKontrollierte Daten müssen die sichere Wiederverwendung über Domänen, Partner und externe Ökosysteme hinweg unterstützen und nicht nur den Zugriff innerhalb einer einzelnen Umgebung kontrollieren.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EData Mesh ordnet ein klares Domänen-Ownership zu und wahrt gleichzeitig föderierte Governance-Standards.\u003C/li\u003E\r\n\u003Cli\u003EDatenprodukte bündeln Daten mit einer Eigentümerschaft, Definition, einem Qualitätsziel und einem Lebenszyklus.\u003C/li\u003E\r\n\u003Cli\u003EDatenverträge definieren die Erwartungen zwischen Produzenten und Konsumenten, einschließlich Schema, Aktualität und Qualität.\u003C/li\u003E\r\n\u003Cli\u003EData Clean Rooms ermöglichen es Parteien, an kontrollierten Daten zusammenzuarbeiten, ohne die zugrunde liegenden Rohdatensätze offenzulegen.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EJedes geteilte Daten-Asset ist mit Annahmen verbunden: wem es gehört, was es bedeutet, ob es aktuell ist, welche Richtlinien gelten und ob die Empfängerseite es für den vorgesehenen Zweck nutzen darf. Governance hilft dabei, diese Annahmen explizit festzulegen und durchsetzbar zu machen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-core-components-of-data-governance_0":{"id":"card-v2-2d0377e56d","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/siemens-1/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Customer Story lesen"},":type":"snowflake-site/components/card-v2","layoutStyle":"horizontal","type":"content-card","image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--7e2d4df5-3c0b-4aa6-bc0e-45de50dbc34d/siemens-transparent.png?quality=85&preferwebp=true","alt":"Siemens logo","lazyEnabled":true,"width":"624",":type":"snowflake-site/components/image"},"text":{"id":"text","text":"\u003Cp\u003ESiemens, ein weltweit führendes Technologieunternehmen, hat mit Snowflake ein unternehmensweites Data Mesh aufgebaut – damit wurden über 600 Datenprojekte ermöglicht, Tausende von Data Warehouses integriert und eine skalierbare AI Data Cloud geschaffen, die KI, Automatisierung und unternehmensübergreifende Collaboration unterstützt.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title":{"id":"title","type":"heading4","lines":["Customer Story: Siemens AG"],":type":"snowflake-site/components/title-v2"}},"title_data-stewardship-and-governance-roles":{"id":"title-v2-68b6b25505","additionalClasses":"anchor-title anchor-title--data-stewardship-and-governance-roles","type":"heading2","lines":["Rollen in Data Stewardship und Governance"],":type":"snowflake-site/components/title-v2"},"text_data-stewardship-and-governance-roles_0":{"id":"text-5c2fc25ce2","text":"\u003Cp\u003EData Governance hängt von bestimmten Personen mit klarer Entscheidungsbefugnis ab. \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-stewardship/\"\u003EData Stewardship\u003C/a\u003E ist die operative Ebene, die Governance-Entscheidungen mit der täglichen Datenarbeit verknüpft. In einem ausgereiften Programm arbeiten Stewards mit Data Ownern, Custodians, Datenschutzverantwortlichen, Sicherheitsteams und einem Governance Council zusammen, um Definitionen zu pflegen, die Qualität zu überwachen, Zugriffsmuster zu überprüfen und domänenübergreifende Definitionskonflikte zu eskalieren.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ERolle\u003C/th\u003E\r\n\u003Cth\u003EGovernance-Verantwortung\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Data Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELegt die Datenstrategie des Unternehmens fest, sponsert das Data Governance-Programm und trägt die exekutive Verantwortung für die Datenergebnisse.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData Owner\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EBesitzt die fachliche Entscheidungskompetenz über eine Domäne, ein Datenprodukt, eine Metrik oder ein kritisches Dataset.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData Steward\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPflegt Definitionen, Qualitätserwartungen, Metadaten, Zugriffsrichtlinien und die Problemlösung für eine Domäne oder ein Asset.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData Custodian\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EVerwaltet die technische Umgebung, in der Daten gespeichert, verarbeitet, gesichert und gepflegt werden.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData Protection Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EÜberwacht die Datenschutzverpflichtungen für kontrollierte personenbezogene Daten, insbesondere dort, wo Gesetze eine formelle Datenschutzrolle vorschreiben.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Privacy Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELeitet die übergeordnete Datenschutzstrategie, Richtlinien und das Risikomanagement im gesamten Unternehmen.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGovernance Analyst\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EUnterstützt bei der Dokumentation von Richtlinien, der Katalogpflege, dem Reporting, der Problemverfolgung und den Governance-Metriken.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGovernance Council\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELöst domänenübergreifende Konflikte, genehmigt Standards und priorisiert Data Governance-Initiativen.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EDie genaue Rollenverteilung variiert je nach Unternehmen, aber eines bleibt gleich: Data Governance erfordert sowohl geschäftliche Autorität als auch technische Verwahrung. Große Unternehmen formalisieren dies oft durch ein Governance Council, dokumentierte Eskalationspfade und Stewardship auf Domänenebene.\u003C/p\u003E\r\n\u003Cp\u003EAuf die praktischen Details kommt es an. Sollten sich zwei Teams über die Definition einer Kennzahl uneinig sein, sollte im Programm festgelegt werden, wer die Entscheidung trifft. Wenn für ein kontrolliertes Datenfeld eine neue Datenmaskierungs-Richtlinie erforderlich ist, sollte der Steward wissen, welcher Sicherheits- oder Datenschutzpartner hinzugezogen werden muss. Wenn ein Problem mit der Datenqualität Auswirkungen auf einen nachgelagerten Bericht hat, sollte die Datenherkunft diese Auswirkungen aufzeigen, und das Stewardship-Team sollte festlegen, wer für die Behebung zuständig ist.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-process-and-strategy":{"id":"title-v2-f76cc3833c","additionalClasses":"anchor-title anchor-title--data-governance-process-and-strategy","type":"heading2","lines":["Prozess und Strategie für Data Governance"],":type":"snowflake-site/components/title-v2"},"text_data-governance-process-and-strategy_0":{"id":"text-1c52a302ff","text":"\u003Cp\u003EDie praktische Einführung einer Data Governance-Strategie folgt in der Regel diesem Ablauf:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EWählen Sie eine prioritäre Domäne:\u003C/b\u003E Beginnen Sie mit Customer 360, der Finanzberichterstattung, kontrollierten Daten, Supply Chain Analytics oder KI-Trainingsdaten – überall dort, wo das Geschäftsrisiko oder der Compliance-Druck am höchsten ist.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EErfassen Sie kritische Datenbestände:\u003C/b\u003E Identifizieren Sie die Tabellen, Views, Dateien, Metriken und Berichte, die in dieser Domäne am wichtigsten sind.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EKlassifizieren Sie sensible und regulierte Daten:\u003C/b\u003E Taggen Sie PII, PHI, Zahlungsdaten, vertrauliche Datensätze und andere kontrollierte Datentypen.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EWeisen Sie Owner und Stewards zu:\u003C/b\u003E Legen Sie fest, wer für Definitionen, Zugriffsentscheidungen, Qualitätserwartungen und Problemlösungen verantwortlich ist.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDefinieren Sie Richtlinien:\u003C/b\u003E Legen Sie Regeln für Zugriff, Maskierung, Aufbewahrung, Data Sharing, KI-Nutzung und Ausnahmen fest.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EErfassen Sie die Herkunft und Qualitätssignale:\u003C/b\u003E Verfolgen Sie kritische Datenflüsse und überwachen Sie Aktualität, Vollständigkeit und Gültigkeit.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EÜberprüfen Sie Zugriff und Nutzung:\u003C/b\u003E Nutzen Sie Audit-Datensätze, um zu validieren, wer auf sensible Daten zugegriffen hat und ob die Richtlinien wie vorgesehen funktioniert haben.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDomäne für Domäne erweitern:\u003C/b\u003E Verwenden Sie Standards, Templates und gewonnene Erkenntnisse erneut, während das Programm skaliert.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003EZu den nützlichen Erfolgskennzahlen zählen die Katalog-Adoption, der Anteil der kritischen Datenbestände mit zugewiesenen Ownern, die Klassifizierungsabdeckung, der Abdeckungsgrad der Richtlinien, die Bearbeitungszeit bei Datenqualitätsproblemen, der Abschluss von Zugriffsprüfungen und die Verringerung der Anzahl von Audit-Feststellungen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/strategy/best-practices/\"\u003EErfahren Sie mehr über Best Practices für Data Governance →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-and-regulatory-compliance":{"id":"title-v2-34b6ab0cee","additionalClasses":"anchor-title anchor-title--data-governance-and-regulatory-compliance","type":"heading2","lines":["Data Governance und Einhaltung gesetzlicher Vorschriften"],":type":"snowflake-site/components/title-v2"},"text_data-governance-and-regulatory-compliance_0":{"id":"text-c618bf5a50","text":"\u003Cp\u003EAufsichtsbehörden können verlangen, dass ein Unternehmen nachweisen kann, welche regulierten Daten es speichert, wer darauf zugegriffen hat, wie diese geschützt wurden, wie lange sie aufbewahrt wurden und ob die erforderlichen Kontrollen angewendet wurden. Governance unterstützt \u003Ca href=\"https://www.snowflake.com/en/fundamentals/governance-risk-compliance/\"\u003EGRC\u003C/a\u003E (Governance, Risk, Compliance), indem sie Datenbestände mit Richtlinien, Eigentümer:innen, Kontrollen und Audit-Trails verknüpft.\u003Ca href=\"https://www.snowflake.com/en/data-governance/regulatory-compliance/governance-risk-compliance/\"\u003E\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003EHier einige Beispiele:\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EGovernance-Verpflichtung\u003C/th\u003E\r\n\u003Cth\u003EVorschriften und Standards (Beispiele)\u003C/th\u003E\r\n\u003Cth\u003EWas Governance nachweisen hilft\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EPersonenbezogene und sensible Daten schützen\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDSGVO, BDSG,  CCPA/CPRA, LGPD, PDPA, HIPAA\u003C/td\u003E\r\n\u003Ctd\u003EWelche personenbezogenen Daten existieren, wo diese liegen, wer darauf zugreifen kann und wie Betroffenenanfragen bearbeitet werden – einschließlich der spezifischen BDSG-Anforderungen für Beschäftigtendaten und Videoüberwachung\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EIntegrität der Berichterstattung wahren\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESOX, BCBS 239, Basel III\u003C/td\u003E\r\n\u003Ctd\u003EWie Finanz- oder Risikodaten definiert, transformiert, kontrolliert und abgestimmt werden\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EZahlungsdaten schützen\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPCI DSS\u003C/td\u003E\r\n\u003Ctd\u003EWo Karteninhaberdaten liegen und welche Kontrollen greifen\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EBetriebliche Resilienz verwalten\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDORA, NIS2\u003C/td\u003E\r\n\u003Ctd\u003EWie kritische Systeme, Drittanbieter sowie ICT-Risiken (Informations- und Kommunikationstechnologie) überwacht werden\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EKI-bezogene Datennutzung steuern\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEU AI Act und neue KI-Gesetze\u003C/td\u003E\r\n\u003Ctd\u003EWelche Daten in KI-Systemen verwendet werden, ob dies angemessen ist und wie eine risikoreiche Nutzung kontrolliert wird\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E\u003Cbr\u003E\r\nIn Deutschland legt das \u003Ca rel=\"noopener noreferrer\" href=\"https://www.bmi.bund.de/DE/themen/verfassung/datenschutz/bundesdatenschutzgesetz/bundesdatenschutzgesetz-node.html\" target=\"_blank\"\u003EBundesdatenschutzgesetz\u003C/a\u003E (BDSG) nationale Anforderungen fest, die die DSGVO ergänzen – insbesondere für den Beschäftigtendatenschutz und den Einsatz von Videoüberwachung. Im April 2026 trat zudem das \u003Ca rel=\"noopener noreferrer\" href=\"https://bmds.bund.de/service/gesetzgebungsverfahren/daten-governance-gesetz\" target=\"_blank\"\u003EDaten-Governance-Gesetz\u003C/a\u003E in Kraft: Deutschlands Umsetzung des \u003Ca rel=\"noopener noreferrer\" href=\"https://eur-lex.europa.eu/legal-content/en/LSU/?uri=oj:JOL_2022_152_R_0001\" target=\"_blank\"\u003EEU Data Governance Act\u003C/a\u003E. Es schafft den Rechtsrahmen für Datenvermittlungsdienste und die Weiterverwendung öffentlicher Daten, mit der Bundesnetzagentur als zentraler Anlaufstelle. Damit wird Data Governance in Deutschland nicht nur zur Best Practice, sondern zur gesetzlichen Anforderung.\u003C/p\u003E\r\n\u003Cp\u003EDer \u003Ca rel=\"noopener noreferrer\" href=\"https://artificialintelligenceact.eu/\" target=\"_blank\"\u003EEU AI Act\u003C/a\u003E ist ein gutes Beispiel dafür, warum Governance-Zeitpläne eine so kritische Rolle spielen. Die Verordnung gilt schrittweise: Allgemeine Bestimmungen und Verbote gelten ab dem 2. Februar 2025, Regeln für Allzweck-KI ab dem 2. August 2025 und eine breitere Einführung erfolgt bis zum 2. August 2027. Für Unternehmen, die verwaltete Unternehmensdaten in KI-Systemen verwenden, entsteht dadurch die praktische Notwendigkeit, die Herkunft der Trainingsdaten, die Datenklassifizierung sensibler Daten, Zugriffsberechtigungen und die Überprüfbarkeit lückenlos \u003Ci\u003Enachzuvollziehen\u003C/i\u003E.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ESehen Sie sich das Video an und erfahren Sie, wie die Funktionen von Snowflake – wie die Überwachung sensibler Daten – es Ihnen ermöglichen, Ihre sensiblen Daten mit nur wenigen Klicks zu identifizieren und einen umfassenden Überblick darüber zu erhalten.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-and-regulatory-compliance_0":{"id":"embed-3339e47355","youtubeVideoId":"HeZUJ1aBuz0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_why-run-data-governance-on-snowflake":{"id":"title-v2-31bbc9f460","additionalClasses":"anchor-title anchor-title--why-run-data-governance-on-snowflake","type":"heading2","lines":["Warum Data Governance auf Snowflake betreiben?"],":type":"snowflake-site/components/title-v2"},"text_why_run_data_go":{"id":"text-134aedc216","text":"\u003Cp\u003EData Governance lässt sich leichter aufrechterhalten, wenn Richtlinien, Metadaten, die Datenherkunft, Qualitätsüberwachung und Zugriffskontrollen nah an dem Ort angesiedelt sind, an dem Daten gespeichert, verarbeitet, geteilt und genutzt werden. Die Governance-Funktionen von Snowflake sind direkt in dieselbe Umgebung integriert, in der Unternehmen Daten, Anwendungen und KI-Workloads verwalten – anstatt über ein separates Tool angewendet zu werden, das synchron gehalten werden muss.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEinheitlicher Katalog mit integrierter Datenherkunft:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E bietet Katalog, Datenherkunft auf Feldebene, aktive Metadaten und Richtliniendurchsetzung auf einer einzigen Oberfläche. Snowflake Horizon Catalog kann den Bedarf an einem separaten Katalog-Tool reduzieren, da der Governance-Kontext nah an den Daten angesiedelt ist.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EVon Grund auf Compliance-orientiert:\u003C/b\u003E \u003Ca href=\"https://trust.snowflake.com/\"\u003ESnowflake Compliance Center\u003C/a\u003E bietet die Überwachung des Sicherheitsstatus sowie Bescheinigungen zur Unterstützung von Standards wie \u003Ca rel=\"noopener noreferrer\" href=\"https://www.hhs.gov/hipaa/index.html\" target=\"_blank\"\u003EHIPAA\u003C/a\u003E, PCI DSS, SOC 2 Type II, ISO 27001, \u003Ca rel=\"noopener noreferrer\" href=\"https://www.fedramp.gov/\" target=\"_blank\"\u003EFedRAMP\u003C/a\u003E Moderate und IRAP. Je nach Implementierung können bestimmte Funktionen und Kundenkonfigurationen erforderlich sein.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPolicy-as-Code für sensible Daten:\u003C/b\u003E Dynamic Data Masking, Row Access Policies, Tag-based Masking und External Tokenization implementieren automatisierte Sicherheits-Controls direkt auf Datenebene. Bei korrekter Konfiguration kann die Klassifizierung dazu beitragen, dass Richtlinien konsistent über Abfragen, Anwendungen, Sharing und KI-Workflows hinweg angewendet werden.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EÜberprüfbare Nutzung:\u003C/b\u003E Access History und Query History können dabei helfen, detaillierte Zugriffs- und Transformationsdatensätze zu erfassen, die Audits und behördliche Überprüfungen unterstützen. Bei richtiger Konfiguration können Unternehmen leichter erkennen, wer wann auf eine sensible Spalte zugegriffen hat.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EIntegrierte Governance-Kontrollen für KI-Workflows:\u003C/b\u003E Cortex Guard wendet Richtlinienkontrollen auf LLM-Ein- und -Ausgaben an, um das Risiko zu verringern, dass sensible Daten in ungeeignete Modellkontexte gelangen. Datenmetrikfunktionen können verwendet werden, um die Qualität von Trainingsdaten im Laufe der Zeit zu überwachen, sodass die Daten, die in KI-Systeme eingespeist werden, denselben Standards entsprechen wie die Daten für Berichte.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EKontrolliertes Sharing ohne Kopien:\u003C/b\u003E Secure Data Sharing, Listings und Data Clean Rooms ermöglichen es Unternehmen, Erkenntnisse mit Partnern und externen Beteiligten zu teilen, ohne Rohdaten über Sicherheitsgrenzen hinweg zu übertragen. Data Governance-Controls lassen sich kontinuierlich einfacher und konsistent durchsetzen, da die Daten die Plattform zu keinem Zeitpunkt verlassen.\u003C/p\u003E\r\n\u003Cp\u003EZusammen bieten diese Funktionen eine zentrale Governance-Oberfläche für Data Warehouses, Data Lakes, offene Tabellenformate wie Iceberg, Anwendungen und KI – so müssen Kontrollen nicht jedes Mal neu erstellt werden, wenn Daten in eine neue Umgebung verschoben werden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-governance-as-an-operating-discipline":{"id":"title-v2-c4c93216f4","additionalClasses":"anchor-title anchor-title--build-governance-as-an-operating-discipline","type":"heading2","lines":["Data Governance als feste operative Disziplin etablieren"],":type":"snowflake-site/components/title-v2"},"text_build-governance-as-an-operating-discipline_0":{"id":"text-ad1b53f28c","text":"\u003Cp\u003EGovernance-Reife ist kein binärer Zustand. Die meisten Programme decken einige Bereiche ab, weisen in anderen jedoch Lücken auf. Diese Lücken werden meist im denkbar ungünstigsten Moment sichtbar. Bei einer Compliance-Prüfung könnte ein Datenfeld auffallen, das nie klassifiziert wurde. Oder ein KI-Ergebnis wird in Frage gestellt, aber niemand kann nachvollziehen, welche Daten dazu beigetragen haben.\u003C/p\u003E\r\n\u003Cp\u003EDie Unternehmen, die solche Situationen vermeiden, sind nicht diejenigen, die eine \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/implementation/\"\u003EGovernance-Implementierung\u003C/a\u003E abgeschlossen haben. Es sind diejenigen, die Verantwortlichkeiten, Lineage, Klassifizierung und Audit-Kontrollen in ihre Arbeitsabläufe integriert haben – sodass die Antwort bereits vorliegt, wenn die Frage aufkommt.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/use-cases/\"\u003EErfahren Sie mehr über Anwendungsfälle für Data Governance →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-governance-as-an-operating-discipline_0":{"id":"text-4d2baed78e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EWICHTIGSTE ERKENNTNIS\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EData Governance ist kein einmaliges Projekt, sondern eine fortlaufende Disziplin, die Daten in großem Maßstab vertrauenswürdig, nutzbar und überprüfbar macht. Durch die Etablierung klarer Verantwortlichkeiten, konsistenter Richtlinien sowie Transparenz bei Daten, Qualität und Zugriff können Unternehmen kritische Fragen zu ihren Daten beantworten, noch bevor Probleme entstehen. Da die Nutzung von Daten und KI zunimmt, ist die Einbettung von Data Governance in alltägliche Workflows – anstatt sie als separate Kontrollebene zu behandeln – genau das, was es Teams ermöglicht, schneller zu agieren und gleichzeitig Risiken zu reduzieren.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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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-5ac16fd451","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-2f6bbef9ad","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn diesen Grundlagen\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-7a530528ed","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-data-governance\"\u003EWas ist Data Governance?\u003C/li\u003E\n\u003Cli data-anchor=\"why-data-governance-matters-now\"\u003EWarum Data Governance jetzt wichtiger denn je ist\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-for-ai\"\u003EData Governance für KI\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-principles\"\u003EPrinzipien der Data Governance\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-frameworks-and-standards\"\u003EFrameworks und Standards für Data Governance\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-operating-models\"\u003EBetriebsmodelle für Data Governance\u003C/li\u003E\n\u003Cli data-anchor=\"the-core-components-of-data-governance\"\u003EDie Kernkomponenten der Data Governance\u003C/li\u003E\n\u003Cli data-anchor=\"data-stewardship-and-governance-roles\"\u003ERollen in Data Stewardship und Governance\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-process-and-strategy\"\u003EProzess und Strategie für Data Governance\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-and-regulatory-compliance\"\u003EData Governance und Einhaltung gesetzlicher Vorschriften\u003C/li\u003E\n\u003Cli data-anchor=\"why-run-data-governance-on-snowflake\"\u003EWarum Data Governance auf Snowflake betreiben?\u003C/li\u003E\n\u003Cli data-anchor=\"build-governance-as-an-operating-discipline\"\u003EData Governance als feste operative Disziplin etablieren\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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Fragen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-d8d0c898de","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EIhre häufigsten Fragen zu Data Governance - beantwortet von Snowflake-Experten\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"]},"flexible_column_content_container_2":{"id":"hub-faq-accordions","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-4299330105","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Was ist der Unterschied zwischen Data Governance und Datenmanagement?","richText":"\u003Cp\u003EDatenmanagement umfasst das Sammeln, Speichern, Transformieren, Integrieren und Bereitstellen von Daten. Data Governance definiert die Regeln für diese Prozesse: wem die Daten gehören, wie sie definiert sind, wer sie nutzen darf, wie die Qualität gemessen wird und wie die Compliance nachgewiesen wird. Weitere Details finden Sie in unserem Grundlagen zu \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance-vs-data-management/\"\u003EData Governance vs. Datenmanagement\u003C/a\u003E.\u003C/p\u003E\n"},{"title":"Wer ist für Data Governance verantwortlich?","richText":"\u003Cp\u003EDie Führungsverantwortung liegt oft bei einem Chief Data Officer oder einer ähnlichen Führungsrolle, während die operative Verantwortung im Alltag gemeinschaftlich von Data Ownern, Data Stewards, Data Custodians, Sicherheits-, Datenschutz- und Compliance-Teams sowie Data Governance Councils getragen wird.\u003C/p\u003E\n"},{"title":"Was sind die größten Herausforderungen bei der Data Governance?","richText":"\u003Cp\u003EDie größten Herausforderungen sind meist organisatorischer Natur: unklare Verantwortlichkeiten, mangelndes Executive Sponsorship, inkonsistente Definitionen und Governance, die fälschlicherweise als reine IT-Aufgabe statt als strategische Business-Aufgabe behandelt wird. Technische Probleme wie unvollständige Metadaten, eingeschränkte Datenherkunft oder ungleichmäßige Klassifizierung lassen sich leichter lösen, sobald Verantwortlichkeiten und Prozesse klar definiert sind.\u003C/p\u003E\n"},{"title":"Wie unterstützt Data Governance die KI?","richText":"\u003Cp\u003EData Governance kontrolliert die Daten, die KI-Systeme verwenden. Sie hilft Teams dabei, die Datenherkunft zu verstehen, sensible Felder zu klassifizieren, Zugriffsrichtlinien durchzusetzen, die Qualität zu überwachen, die zulässige Nutzung zu dokumentieren und nachzuverfolgen, welche Datenquellen zu KI-Ergebnissen oder -Entscheidungen beitragen.\u003C/p\u003E\n"},{"title":"Ist Data Governance für die Compliance erforderlich?","richText":"\u003Cp\u003EDie meisten Vorschriften schreiben kein spezifisches Governance-Programm vor, aber Compliance erfordert in der Regel Governance-Funktionen. Unternehmen müssen wissen, welche regulierten Daten sie besitzen, wo diese liegen, wer darauf zugreifen kann, wie sie geschützt sind und ob sie bei einem Audit Nachweise erbringen 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