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aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-beb461747c","additionalClasses":"headline-decoration","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c4438486d8","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBig Data kam in den 1990er Jahren mit dem Beginn des Internetzeitalters und der breiten Einführung digitaler Geschäftspraktiken auf. Unternehmen erhielten Zugang zu einer Flut von Datenpunkten über ihre Geschäftsfunktionen, Kunden und ganze Branchen. Big Data setzt sich aus großen, komplexen Datasets zusammen, die die Verarbeitungskapazitäten herkömmlicher Datenmanagementsysteme übersteigen.\u003C/p\u003E\n\u003Cp\u003EBig Data umfasst oft nicht nur große Mengen klassischer strukturierter Daten, sondern auch \u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003Esemistrukturierte\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Eunstrukturierte Daten\u003C/a\u003E in einer Vielzahl von Formaten. \u003C/p\u003E\n\u003Cp\u003EUnternehmen können heute zuverlässige Daten in verschiedensten Formaten erfassen, darunter Audiodateien, Webseiten, interne Prozesse, Kundentransaktionen und mehr. Angesichts der Komplexität von Big Data sind unterschiedliche Tools und Ressourcen erforderlich, um alle Informationen ordnungsgemäß zu erfassen, zu verwalten und zu analysieren. \u003C/p\u003E\n\u003Cp\u003EDas Aufkommen und das stetige Wachstum von Big Data in den letzten Jahrzehnten bieten Unternehmen enorme Chancen, neue Erkenntnisse zu gewinnen und die Entscheidungsfindung zu verbessern. \u003C/p\u003E\n\u003Cp\u003EIn diesem Artikel untersuchen wir die einzigartigen Merkmale von Big Data und zeigen, wie Unternehmen bei effektiver Nutzung Effizienz, Innovation und Wachstum steigern können. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__659376865":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ad6f63160f","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Big Data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-30bf9ce502","text":"\u003Cp\u003EBig Data bezieht sich auf große und komplexe Datasets, die strukturierte Daten wie Bestandsdaten sowie unstrukturierte Daten wie Audiodateien oder Social-Media-Inhalte umfassen können. Da diese Datasets riesig sind und im Laufe der Zeit weiter wachsen, können sie oft nicht in herkömmlichen Datenmanagementsystemen verwaltet werden.  \u003C/p\u003E\n\u003Cp\u003EIn den letzten Jahren sind zudem die Kosten für Datenspeicherung gesunken, sodass Unternehmen mehr ihrer Daten speichern und aufbewahren können. Dies erhöht zwar das Potenzial zur Gewinnung von Erkenntnissen, steigert aber auch die Komplexität. Unternehmen benötigen heute mehr\u003Ca href=\"https://www.snowflake.com/de/product/analytics/\"\u003E Analytics-Tools \u003C/a\u003Eund Fachwissen, um Erkenntnisse aus diesen großen Datasets zu gewinnen und fundierte Geschäftsentscheidungen zu treffen. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0b78e24996","additionalClasses":"headline-decoration","type":"heading2","lines":["Wie funktioniert Big Data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-399cafd5c8","text":"\u003Cp\u003EBig Data ist eine Sammlung großer Mengen verschiedener und komplexer Datasets. Dabei werden große Datenmengen aus verschiedenen Quellen erfasst, oft in Echtzeit. Zu diesen Datenquellen gehören Kennzahlen zu internen Geschäftsprozessen, Kundenstimmung, Engagement und mehr. \u003C/p\u003E\r\n\u003Cp\u003EDie Geschwindigkeit der Datenerfassung führt dazu, dass Systeme Unmengen an Informationen verarbeiten müssen. Um all dies zu verwalten, müssen \u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-data-engineering/\"\u003EData Engineers\u003C/a\u003E und Analyst:innen die Daten mithilfe spezialisierter Cloud-Computing-Systeme verarbeiten und strukturieren, die über mehr Speicher- und Rechenressourcen verfügen als herkömmliche Systeme. Um aus all den Daten werthaltige Erkenntnisse zu gewinnen, nutzen Unternehmen Machine Learning und spezialisierte Machine-Learning-Verfahren, um die Daten effektiv zu analysieren. Unternehmen suchen in den Daten nach Mustern und Trends, um transformative Geschäftsentscheidungen zu unterstützen.\u003C/p\u003E\r\n\u003Cp\u003EUm Big Data optimal zu nutzen und das Geschäftspotenzial voll auszuschöpfen, haben Unternehmen ihre Datenpraktiken und -prozesse angepasst. Unternehmen erkennen heute, dass sie modernste Funktionen für Datenerfassung, -verarbeitung, -speicherung und -analyse benötigen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-c22879ed68","additionalClasses":"headline-decoration","type":"heading2","lines":["Wichtige Merkmale von Big Data (die 5 Vs)"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3082f85fdc","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBig Data weist fünf Hauptmerkmale auf, die als die „fünf Vs von Big Data“ bekannt sind. Sie verdeutlichen, wie sich Big Data von herkömmlichen Datasets unterscheidet. Die Vs stehen für: Volume (Volumen), Velocity (Geschwindigkeit), Variety (Vielfalt), Veracity (Wahrhaftigkeit) und Value (Wert). Im Folgenden schlüsseln wir die einzelnen Merkmale auf:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVolume (Volumen)\u003C/h4\u003E\r\n\u003Cp\u003EHeute gibt es schlichtweg mehr Daten, die Unternehmen speichern, verwalten und analysieren müssen. Da mehr Informationen verfügbar sind, müssen sich Unternehmen anpassen, um ihre stetig wachsenden Daten bestmöglich zu nutzen und zu handhaben.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVelocity (Geschwindigkeit)\u003C/h4\u003E\r\n\u003Cp\u003EUnternehmen generieren Daten heute schneller als je zuvor. Diese Realität setzt Unternehmen unter Druck, Daten schneller zu verarbeiten und zu analysieren – oft in Echtzeit –, um rasche und wirkungsvolle Entscheidungen zu treffen. Kunden erwarten zudem nahezu sofortiges Feedback zu empfohlenen Produkten. Um mit den Kundenanforderungen Schritt zu halten, müssen sich Unternehmen anpassen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVariety (Vielfalt)\u003C/h4\u003E\r\n\u003Cp\u003EBig Data umfasst verschiedene Datenformate, darunter unstrukturierte Daten wie Freitext, Bilder, Videos und mehr. Dazu gehören auch strukturierte Daten wie Tabellenkalkulationen und semistrukturierte Daten wie Sensordaten. Die Verwaltung dieser Vielfalt erfordert flexible Datenbanken und Tools, um umfassende Datenanalysen zu ermöglichen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVeracity (Glaubwürdigkeit und Datenqualität)\u003C/h4\u003E\r\n\u003Cp\u003EGenauigkeit ist eine Herausforderung bei Big Data. Aufgrund der Vielzahl an Quellen und Typen sowie der schieren Menge ist das Fehlerpotenzial hoch. Doch zuverlässige Daten sind für genaue Analytics und fundierte Entscheidungen unerlässlich. Unternehmen müssen eigenverantwortlich sicherstellen, dass die Datenqualität durch Bereinigung, Validierung und Verifizierung gewährleistet ist. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EValue (Wert)\u003C/h4\u003E\r\n\u003Cp\u003EGenaue, hochwertige Daten können einen erheblichen geschäftlichen Mehrwert bieten, den Umsatz steigern, Effizienzpotenziale aufdecken und Innovationen fördern. Wenn Unternehmen erkennen, wo in Big Data potenzieller Wert liegt, können sie eine effektivere Strategie zu deren Nutzung entwickeln. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3c168df457","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile von Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c343594248","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBig Data hat das Potenzial, Geschäftsabläufe drastisch zu verbessern und so zu optimierten Geschäftsergebnissen zu führen. Zu den Vorteilen von Big Data gehören: \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVerbesserung der strategischen Entscheidungsfindung\u003C/h4\u003E\r\n\u003Cp\u003EBig Data ermöglicht es Unternehmen, fundiertere und strategischere Entscheidungen zu treffen. Für das Lieferkettenmanagement können Unternehmen komplexe Datasets effektiv und methodisch analysieren und zuverlässige Prognosen entwickeln, um Bestandsnachschub und Bestellbedarf besser zu steuern. Der Einsatz von Automatisierung und Echtzeit-Insights kann den geschäftlichen Nutzen weiter steigern. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EVerbesserung des Kundenerlebnisses\u003C/h4\u003E\r\n\u003Cp\u003EUnternehmen können Kundendaten analysieren, um Bedürfnisse und Verhalten ihrer Kundschaft besser zu verstehen. Dies ermöglicht es Unternehmen, maßgeschneiderte Kampagnen für jeden Kundentyp zu erstellen und die individuellen Bedürfnisse der Kundschaft in den Mittelpunkt zu stellen. Unternehmen können Kundenprofile entwickeln, um personalisierte Angebote auf Basis von demografischen Informationen, Marketing-Engagement und mehr bereitzustellen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EOptimierung von Abläufen und Prognose von Trends\u003C/h4\u003E\r\n\u003Cp\u003EIn allen Unternehmen kann jede Abteilung Daten nutzen, um Abläufe zu optimieren. Dazu gehört die Straffung von Prozessen und die Reduzierung von Verschwendung, indem \u003Ca href=\"https://www.snowflake.com/de/fundamentals/big-data-analytics/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBig-Data-Analysen\u003C/a\u003E genutzt werden, um Wartungsbedarf vorherzusagen, Trends zu prognostizieren, Prozessverbesserungen umzusetzen und die Personaleinsatzplanung anzupassen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EErmöglichung von Innovationen\u003C/h4\u003E\r\n\u003Cp\u003EBig Data öffnet die Tür für \u003Ca href=\"https://www.snowflake.com/de/fundamentals/predictive-analytics/\" target=\"_blank\"\u003Eprädiktive Analytik\u003C/a\u003E und Prognosefunktionen. Mit Big Data können Unternehmen Trends, Kundenverhalten, Kundenfeedback und breitere Markttrends untersuchen, um bestehende Produkte zu verbessern oder neue zu entwickeln.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7de392ed38","additionalClasses":"headline-decoration","type":"heading2","lines":["Herausforderungen durch Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-281e5a713c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAuch wenn Big Data die Art und Weise, wie Unternehmen aussagekräftige Erkenntnisse über ihr Geschäft gewinnen, erheblich verändert hat, gibt es auch Herausforderungen. Im Folgenden schlüsseln wir einige der häufigsten Schwierigkeiten auf, mit denen Unternehmen in Bezug auf Big Data konfrontiert sind. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EDatenschutz und Sicherheit\u003C/h4\u003E\n\u003Cp\u003ESich ständig ändernde Gesetze und Vorschriften stellen eine große Herausforderung dar. Unternehmen müssen verschiedene Datenschutz- und Sicherheitsgesetze wie \u003Ca href=\"https://www.snowflake.com/en/blog/gdpr-best-practices/\"\u003EDSGVO\u003C/a\u003E und \u003Ca href=\"https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act\"\u003EHIPAA\u003C/a\u003E einhalten, was bei großen und stetig wachsenden Datasets schwierig sein kann. Kund:innen haben zudem hohe Erwartungen an den Schutz ihrer persönlichen Daten durch Unternehmen. Dies erhöht den Druck auf Unternehmen, Sicherheitsmaßnahmen zum Schutz von Kundendaten zu implementieren.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESkalierbarkeit\u003C/h4\u003E\n\u003Cp\u003EMit mehr Daten steigen auch der Speicherbedarf und die Anforderungen an Verarbeitungsressourcen. Die Verwaltung dieser Speicher-Tools erfordert kostspielige, spezialisierte Ressourcen. Selbst mit Cloud-Diensten ist das Speichern und Verwalten all dieser Daten anspruchsvoll und ressourcenintensiv. Unternehmen müssen spezialisierte Fachkräfte rekrutieren, die effektiv und effizient mit der bestehenden Belegschaft zusammenarbeiten können. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EKompetenzlücken\u003C/h4\u003E\n\u003Cp\u003EAngesichts des Zustroms großer Mengen komplexer Daten benötigen Unternehmen qualifizierte Mitarbeitende, darunter Datenanalyst:innen und Data Engineers, um die Daten aufzubereiten und sinnvoll zu nutzen. Daten zu haben ist das eine, aber die richtigen Mitarbeitenden zu haben, um die Daten zu interpretieren, Muster zu erkennen und Empfehlungen abzugeben, ist der eigentliche Wert. Zudem benötigen Unternehmen technisch versierte Führungskräfte, die offen für innovative, datengestützte Entscheidungen sind, die über vertraute Tabellenkalkulationen oder Bauchgefühl hinausgehen. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EIntegrationskomplexität\u003C/h4\u003E\n\u003Cp\u003EEs ist eine Herausforderung, mehrere Arten von Datenquellen effektiv zu kombinieren. Händler könnten beispielsweise versuchen, Ladenverkäufe und Website-Klickdaten zu kombinieren oder Kauf- und Versanddaten zu nutzen, um eine Kundenanfrage besser zu unterstützen; ein Gesundheitsdienstleister muss möglicherweise elektronische Patientenakten, Laborergebnisse und Versicherungsdaten zusammenführen, um einen vollständigen Behandlungsplan für eine Patientin zu erstellen. Solche Integrationen erfordern neue Tools und Technologien zur Bewältigung der Datenflut sowie spezialisierte Datenanalyst:innen und andere IT-Ressourcen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6a8584e4b0","additionalClasses":"headline-decoration","type":"heading2","lines":["Big-Data-Beispiele und Anwendungsfälle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-61ac6f6dad","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVerschiedene Unternehmensfunktionen in unterschiedlichen Branchen können Big Data nutzen, um Großes zu erreichen. Hier sind einige Beispiele dafür, wie Big Data verschiedene Branchen unterstützt:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EGesundheitswesen\u003C/h4\u003E\r\n\u003Cp\u003EDas \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/healthcare-and-life-sciences/\"\u003EGesundheitswesen\u003C/a\u003E kann Big Data gezielt nutzen, um seinen Auftrag zu erfüllen und gleichzeitig regulatorische Anforderungen einzuhalten. Gesundheitsorganisationen können die Erfahrung für Patient:innen und Leistungserbringer verbessern, indem sie \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/patient-and-member-360/\"\u003Everschiedene Patienten-Datasets für eine ganzheitliche Sicht auf die Patientengesundheit kombinieren\u003C/a\u003E. Big Data kann elektronische Gesundheitsakten, Familienanamnese, Daten von Wearables, Versicherungsinformationen und mehr zusammenführen, um den Behandlungsverlauf von Patient:innen zu beeinflussen. Daten zu Terminplanung und medizinischem Materialbedarf können helfen, Personaleinsatz und Lieferkettenabläufe zu optimieren. Und eine durchgängige \u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003EData Governance\u003C/a\u003E kann Versicherern und Gesundheitsdienstleistern helfen, strenge Datenschutzanforderungen zu erfüllen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFinanzwesen\u003C/h4\u003E\r\n\u003Cp\u003EIm \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/financial-services/\"\u003EFinanzwesen\u003C/a\u003E können Unternehmen Big Data nutzen, um das Ausgabeverhalten von Kund:innen zu analysieren und \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Emöglichen Identitätsdiebstahl in Echtzeit zu erkennen\u003C/a\u003E. Sie können noch einen Schritt weiter gehen und zusätzliche Sicherheitsfunktionen rund um die Authentifizierung implementieren. Eine \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/customer-360-in-financial-services/\"\u003Eumfassende Sicht auf Transaktionen und andere Kundeninformationen\u003C/a\u003E kann Unternehmen helfen, die sich ständig weiterentwickelnden Sicherheits- und Compliance-Anforderungen einzuhalten. Finanzinstitute können ihre Kund:innen besser bedienen, indem sie Daten nutzen, um deren Ausgabegewohnheiten zu analysieren. Sie können diese Erkenntnisse nutzen, um spezielle Angebote zu empfehlen, die Kund:innen helfen, ihre finanziellen Ziele zu erreichen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EHandel und E-Commerce\u003C/h4\u003E\r\n\u003Cp\u003EErkenntnisse aus Big Data können effektive, gezielte Marketingmaßnahmen ermöglichen. Durch die Nachverfolgung von Customer Journeys und Ausgabemustern können \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/retail-consumer-goods/\"\u003EHändler\u003C/a\u003E die Bedürfnisse und Wünsche ihrer Kund:innen besser verstehen. Sie können diese Erkenntnisse nutzen, um personalisierte Marketingkampagnen mit kundenspezifischen Produktempfehlungen zu entwickeln. Sie können auch ihre Lieferkettenabläufe, Absatzprognosen und andere Faktoren besser steuern und ihre Produktentwicklung auf Basis von Kundenfeedback verbessern. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFertigung\u003C/h4\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/manufacturing/\"\u003EFertigungsunternehmen\u003C/a\u003E können Erkenntnisse aus Big Data ziehen, um Produktion, Montagelinien, \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/improve-supply-chain-performance/\"\u003ELieferkettenmanagement\u003C/a\u003E und mehr zu verbessern. Unternehmen können beispielsweise \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/power-smart-manufacturing/\"\u003ESensordaten\u003C/a\u003E nutzen, um vorherzusagen, wann Wartungsarbeiten erforderlich sind und Geräteausfälle zu prognostizieren, um Ausfallzeiten zu vermeiden und Gesamtausgaben für Reparaturen zu senken. Durch die Identifizierung von Mustern, die auf eine bevorstehende Störung hinweisen, bevor diese eintritt, können Hersteller besser planen und Ressourcen effektiver zuteilen. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EÖffentlicher Sektor\u003C/h4\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/public-sector/\"\u003EBehörden und Organisationen des öffentlichen Dienstes\u003C/a\u003E können Big Data nutzen, um die Bedürfnisse der Bevölkerung besser zu verstehen. Organisationen können Sicherheitsbedenken zuvorkommen, indem sie beispielsweise Verkehrsdaten und Fahrertrends zusammenführen, um Straßennetze zu optimieren und die Instandhaltung zu verbessern. Dies kann Behörden helfen, Verbesserungen schneller umzusetzen und das Vertrauen der Bürger:innen zu stärken, dass im besten Interesse gehandelt wird.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1413360324":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-d1b3bc09c5","additionalClasses":"headline-decoration","type":"heading2","lines":["Best Practices für Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-de1b979786","additionalClasses":"list--blue-bullets","text":"\u003Ch4\u003EKlare Ziele definieren\u003C/h4\u003E\r\n\u003Cp\u003EDamit Unternehmen fokussiert bleiben und sich nicht in zu vielen Informationen verlieren, sollte die Datenanalyse klare Geschäftsziele unterstützen. Die Ausrichtung von \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/public-sector/\"\u003EAnalytics-Initiativen\u003C/a\u003E an Prioritäten kann Fehlstarts und Sackgassen minimieren und Führungskräfte schneller zu wertvollen Erkenntnissen führen.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EHohe Datenqualität und Relevanz sicherstellen\u003C/h4\u003E\r\n\u003Cp\u003EMinderwertige oder irrelevante Daten können zu fehlerhaften Entscheidungen führen. Ein Händler könnte beispielsweise schlechte Umsatzprognosen erstellen, wenn das Dataset doppelte Einträge, Verkäufe anderer Produkte als des analysierten oder veraltete Daten enthält. Unternehmen müssen starke Data-Governance-Rahmenbedingungen sowie zuverlässige Tools und Techniken für Datenqualität einsetzen, damit Daten aktuell, genau und relevant sind.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003ESkalierbare Speicher- und Verarbeitungslösungen nutzen\u003C/h4\u003E\r\n\u003Cp\u003EAngesichts wachsender Datenmengen müssen Unternehmen ihre Datenspeicherkapazität, Personalressourcen und IT-Prozesse erweitern, um Datenmanagement und -analyse im Petabyte-Bereich und darüber hinaus zu unterstützen. Moderne skalierbare Lösungen umfassen verteilte Speichersysteme, \u003Ca href=\"https://www.snowflake.com/de/product/data-engineering/\"\u003Ecloudbasierte Data Lakes\u003C/a\u003E und fortschrittliche Verarbeitungs-Frameworks, die Ressourcen bei Bedarf automatisch und mit maximaler Effizienz skalieren können.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EDatenschutz und Sicherheit priorisieren\u003C/h4\u003E\r\n\u003Cp\u003EDer Schutz sensibler Daten und die Einhaltung sich entwickelnder Datenschutz- und Compliance-Vorschriften erfordern von Unternehmen die Implementierung effektiver Richtlinien für die Datenverarbeitung. Wichtige Vorschriften wie DSGVO und HIPAA erfordern strenge Sicherheitsmaßnahmen, um Verletzungen vertraulicher Kundendaten zu verhindern. Kund:innen möchten darauf vertrauen können, dass ihre Daten sicher sind; daher hat der Schutz dieser Daten für jedes Unternehmen hohe Priorität. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EEine kollaborative, datengestützte Kultur fördern\u003C/h4\u003E\r\n\u003Cp\u003EData Scientists, IT-Teams und Führungskräfte müssen zusammenarbeiten, um Daten zur Erreichung von Geschäftszielen zu nutzen. Zu den Techniken zum Aufbau einer breiten, kollaborativen Datenkultur gehören die Bildung funktionsübergreifender Teams sowie interne Innovationsprojekte oder Wettbewerbe. Weitere Ideen sind die Förderung von Pilotprojekten für neue Tools oder Prozesse, die Bereitstellung externer Lernressourcen sowie der Austausch von Tipps, Techniken und Erkenntnissen durch Lunch-and-Learn-Sessions.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__659376865":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-dbadd66370","additionalClasses":"headline-decoration","type":"heading2","lines":["Die Zukunft von Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8d6b1f0aa6","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBig Data ist komplex und umfasst diverse und unterschiedliche Datasets. Dies ist zwar ein Gewinn für Unternehmen, da es einen stetigen Zustrom potenzieller Erkenntnisse generieren kann, doch die Speicherung und effektive Analyse der Daten zur Erzielung wertvoller Ergebnisse kann auch eine Herausforderung darstellen. \u003C/p\u003E\n\u003Cp\u003EAuch in Zukunft werden Big-Data-Funktionen – ebenso wie die Daten selbst – weiter wachsen. Die stetige Weiterentwicklung von Tools und Technologien für Big-Data-Analytics wird sowohl Innovationen vorantreiben als auch ethische Fragen aufwerfen. Unternehmen werden sich damit auseinandersetzen müssen, wie sie Daten auf ethische Weise speichern, verwalten und analysieren. \u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/ai/\"\u003EKI- und ML-Innovationen\u003C/a\u003E, einschließlich des Aufkommens von Natural Language Processing und \u003Ca href=\"https://www.snowflake.com/de/product/features/cortex/\"\u003Egenerativer KI\u003C/a\u003E in der Datenanalytik, werden zunehmend zum Mainstream. Dies wird Daten demokratisieren, da auch weniger technisch versierte Anwender:innen direkt \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/data-agents/\"\u003E„Fragen an die Daten stellen“\u003C/a\u003E können, ohne dass Data Scientists eine geschäftliche Frage in Code übersetzen müssen. Das Ergebnis können schnellere und bessere Entscheidungen sein. Das Internet of Things (IoT), bei dem zahlreiche Geräte in einem Unternehmensnetzwerk Sensordaten liefern, und Edge Computing, bei dem die Datenverarbeitung am Rand des Netzwerks erfolgt, werden mehr Daten generieren und den Bedarf an automatisierten Aktionen erhöhen. \u003C/p\u003E\n\u003Cp\u003EKurz gesagt: Die Zukunft von Big Data bedeutet „mehr“. Mehr Daten, mehr Tools, mehr Hunger nach Erkenntnissen und mehr Wertschöpfung für die Unternehmen, die lernen, dies zu meistern.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f81e7e89e8","additionalClasses":"headline-decoration","type":"heading2","lines":["Häufig gestellte Fragen zu Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-131984da68","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Welche Tools und Technologien sind mit Big Data verbunden?","richText":"\u003Cp\u003E1. \u003Cb\u003EDatenspeicherung und -verwaltung:\u003C/b\u003E Entwickelt für die Speicherung und Verwaltung riesiger Datasets, die strukturiert, unstrukturiert oder semistrukturiert sein können. Zu den Technologien gehören verteilte Dateisysteme, NoSQL-Datenbanken, \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-warehouse/\"\u003EData Warehouses\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-lakes-and-data-lake-architecture/\"\u003EData Lakes\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E2. \u003Cb\u003EDatenverarbeitung:\u003C/b\u003E Entwickelt für die Übertragung von Daten zwischen relationalen Datenbanken. Sie erfassen, aggregieren und verschieben Daten effizient von verschiedenen Quellen in einen zentralen Datenspeicher.\u003C/p\u003E\r\n\u003Cp\u003E3. \u003Cb\u003EData Warehousing und Analytics:\u003C/b\u003E Ermöglichen das Lesen, Schreiben und Verwalten großer Datasets über hochskalierbare, serverlose und kosteneffiziente Cloud Data Warehouses.\u003C/p\u003E\r\n\u003Cp\u003E4. \u003Cb\u003EDatenvisualisierung und Reporting:\u003C/b\u003E Diese interaktiven Dashboards für Datenvisualisierung, Reporting und robuste Analytics werden von Business-Intelligence-Teams genutzt. \u003C/p\u003E\r\n\u003Cp\u003E5. \u003Cb\u003EMaschinelles Lernen und KI:\u003C/b\u003E Diese Art von algorithmusgesteuerter Software bietet fortschrittliche Rechenleistung und findet Muster und Erkenntnisse in Big Data.\u003C/p\u003E\r\n\u003Cp\u003E6. \u003Cb\u003EOrchestrierung und Management:\u003C/b\u003E Programmatische Erstellung, Planung und Überwachung von Workflows über Open-Source-Systeme zur Automatisierung von Bereitstellung, Skalierung und Anwendungsmanagement.\u003C/p\u003E\r\n"},{"title":"Wie unterscheidet sich Big Data von herkömmlichen Daten?","richText":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003EBig Data umfasst große, vielfältige Datasets mit Daten aus einer Vielzahl von Formaten, einschließlich Text, Audio und Video. Sie sind unstrukturiert und passen daher nicht einfach oder sauber in eine herkömmliche Datenbank, was bedeutet, dass eine robustere Verarbeitung erforderlich ist, um einen Mehrwert zu bieten. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EHerkömmliche Daten bestehen aus strukturierten Daten mit klaren Parametern und können problemlos in standardmäßigen, traditionellen Datenbanken gespeichert und analysiert werden.  \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__480869401":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-79fe739a21","additionalClasses":"headline-decoration","type":"heading2","lines":["Kunden, die Snowflake einsetzen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-28a9a11efb","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-648db22526",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-6496d34516","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["WHOOP Improves AI/ML Financial Forecasting While Enhancing Members’ Experiences"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Customer Story lesen (Englisch)"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--15863a57-5aeb-4400-b348-edd4aedb6520/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"simon data logo","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EMit Snowflake und Apache Iceberg konnten die WHOOP-Teams den Datenzugriff zentralisieren, gleichzeitig die Komplexität reduzieren, Kosten senken und kritische Prozesse wie Finanzprognosen und die Entwicklung von Features verbessern.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-f0435c2a85",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-f0bb87923f","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Capital One setzt bei Data Science auf Snowflake"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/de/customers/all-customers/video/capital-one/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Customer Story lesen"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--443f9b1e-5895-4144-8759-4499d982e801/capital-one-customer-card%25402x.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"penske logo","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EDie Data-Engineering-Experten von Capital One berichten, wie Snowflake eine Vielzahl von Datenherausforderungen in einem der größten Finanzunternehmen der Bankenbranche löst.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["title_v2","flexible_column_cont"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__373061683":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-fbf48d7190","type":"heading2","lines":["Ressourcen zu 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