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aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-b94dc303c0","additionalClasses":"headline-decoration","type":"heading2","lines":["Visão geral"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3eb434cead","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO conceito de big data surgiu na década de 1990 com o advento da Internet e a adoção generalizada de práticas de negócios que priorizam a digitalização. As organizações obtiveram acesso a uma série de pontos de dados sobre suas funções de negócios, clientes e setores como um todo. O big data é composto de grandes conjuntos de dados complicados que cresceram e expandiram para além dos recursos operacionais dos sistemas tradicionais de gerenciamento de dados.\u003C/p\u003E\n\u003Cp\u003ECom frequência, o big data não inclui apenas grandes conjuntos de dados estruturados tradicionais, mas também dados \u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003Esemiestruturados\u003C/a\u003E e \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Enão estruturados\u003C/a\u003E em vários formatos. \u003C/p\u003E\n\u003Cp\u003EAgora, as empresas podem coletar dados avançados em diversos formatos, incluindo, entre outros, arquivos de áudio, páginas da web, processos internos, transações de clientes e muito mais. Graças à complexidade do big data, diferentes ferramentas e recursos são necessários para coletar, gerenciar e analisar devidamente todas as informações. \u003C/p\u003E\n\u003Cp\u003ENas últimas décadas, o surgimento e o crescimento contínuo do big data trouxeram grandes oportunidades para as organizações descobrirem novos insights e melhorarem o processo decisório. \u003C/p\u003E\n\u003Cp\u003ENeste artigo, vamos analisar as características exclusivas do big data e como, quando usado de forma eficaz, ele pode ajudar as organizações a melhorar a eficiência, a inovação e o crescimento. \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_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f15e9858fe","additionalClasses":"headline-decoration","type":"heading2","lines":["O que é Big Data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b5c716014f","text":"\u003Cp\u003EBig data refere-se a grandes e complexos conjuntos de dados que podem incluir dados estruturados, como dados de estoque, e dados não estruturados, como arquivos de áudio ou conteúdo de mídias sociais. Como esses conjuntos de dados são enormes e continuam a crescer com o passar do tempo, eles muitas vezes não podem ser contidos em sistemas tradicionais de gerenciamento de dados.  \u003C/p\u003E\n\u003Cp\u003ENos últimos anos, os custos de armazenamento de dados também diminuíram, o que significa que as organizações podem armazenar e reter mais dados. Embora isso aumente o potencial de extração insights, também aumenta a complexidade. Agora, as organizações precisam de mais\u003Ca href=\"https://www.snowflake.com/pt_br/product/analytics/\"\u003E ferramentas de análise de dados\u003C/a\u003E e conhecimento para obter insights desses grandes conjuntos de dados e tomar decisões de negócios bem-elaboradas. \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_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-344c527d73","additionalClasses":"headline-decoration","type":"heading2","lines":["Como funciona o big data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e494ed46b3","text":"\u003Cp\u003EBig data é um conjunto de grandes quantidades de conjuntos de dados diversos e complexos. Ele funciona coletando grandes volumes de dados de várias fontes, muitas vezes em tempo real. Essas fontes de dados incluem métricas sobre processos internos de negócios, sentimento do cliente, engajamento e muito mais. \u003C/p\u003E\n\u003Cp\u003EA velocidade de coleta dos dados significa que há muitas informações que os sistemas precisam processar. Para gerenciar tudo isso, os \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/what-is-data-engineering/\"\u003Eengenheiros de dados\u003C/a\u003E e analistas precisam processar e estruturar os dados usando sistemas de processamento baseados na nuvem especializados que têm mais armazenamento e capacidade de processamento do que os sistemas tradicionais. Para entender todos os dados, as organizações usam aprendizado de máquina e práticas especializadas de aprendizado de máquina para analisar os dados de forma eficaz. As organizações buscam padrões e tendências nos dados para tomar decisões de negócios transformadoras e que sejam bem-informadas.\u003C/p\u003E\n\u003Cp\u003EPara tirar o máximo proveito do big data e influenciar o potencial de negócios da maneira mais eficaz possível, as organizações se adaptaram às suas práticas e processos de dados. Agora, as organizações reconhecem que precisam de recursos mais avançados e atualizados de coleta, processamento, armazenamento e análise de dados.\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__1444615495":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a34a39fe4b","additionalClasses":"headline-decoration","type":"heading2","lines":["Principais características do Big Data","(os 5 Vs)"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f6ccf54deb","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO big data possui cinco características fundamentais conhecidas como "os cinco Vs do Big Data". Elas ilustram como o big data se diferencia dos conjuntos de dados tradicionais. Os valores de V são: volume, velocidade, variedade, veracidade e valor. Confira a seguir cada característica:\u003C/p\u003E\r\n\u003Ch4\u003EVolume\u003C/h4\u003E\r\n\u003Cp\u003EHoje, há simplesmente mais dados para as organizações armazenarem, gerenciarem e analisarem. Com mais informações disponíveis, as organizações precisam se ajustar para melhorar o uso e lidar com seus dados, conforme eles continuam a crescer.\u003C/p\u003E\r\n\u003Ch4\u003EVelocidade\u003C/h4\u003E\r\n\u003Cp\u003EAtualmente, as organizações criam dados mais rápido do que nunca. Essa realidade pressiona as organizações a processar e analisar dados em um ritmo mais rápido, muitas vezes em tempo real, para tomar decisões rápidas e relevantes. Os clientes também esperam receber feedback quase instantâneo sobre os produtos recomendados para compra. Para lidar com as demandas dos clientes, as organizações precisam se ajustar. \u003C/p\u003E\r\n\u003Ch4\u003EVariedade\u003C/h4\u003E\r\n\u003Cp\u003EO big data contém diferentes formatos de dados, incluindo dados não estruturados, como texto em formato livre, imagens, vídeos e muito mais. Ele também inclui dados estruturados, como planilhas, e dados semiestruturados, como dados de sensores. Gerenciar essa variedade requer bancos de dados e ferramentas flexíveis para permitir uma análise de dados abrangente. \u003C/p\u003E\r\n\u003Ch4\u003EVeracidade\u003C/h4\u003E\r\n\u003Cp\u003EA precisão é um problema com big data. Por causa das várias fontes e tipos e da enorme quantidade delas, há um alto potencial para erros. Dados confiáveis são essenciais para análises precisas e tomada de decisões bem fundamentadas. As organizações precisam assumir a responsabilidade de garantir a qualidade dos dados por meio de esforços de limpeza, validação e verificação de dados. \u003C/p\u003E\r\n\u003Ch4\u003EValor\u003C/h4\u003E\r\n\u003Cp\u003EDados precisos e de alta qualidade podem fornecer um valor comercial considerável, aumentando a receita, adicionando eficiência e estimulando a inovação. Saber onde é possível gerar valor a partir de big data pode ajudar as organizações a criar estratégias mais eficazes para explorá-lo. \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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-86e5d5fe99","additionalClasses":"headline-decoration","type":"heading2","lines":["Benefícios do Big Data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-108c5f9ed9","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO big data pode melhorar radicalmente as operações comerciais, levando a melhores resultados de negócios. Os benefícios do big data são: \u003C/p\u003E\r\n\u003Ch4\u003EMelhora das decisões estratégicas\u003C/h4\u003E\r\n\u003Cp\u003EO big data permite que as organizações tomem decisões mais fundamentadas e estratégicas. Para gerenciar sua cadeia de fornecedores, as organizações podem analisar conjuntos de dados complexos de forma eficaz e metódica, desenvolvendo previsões confiáveis para gerenciar melhor o abastecimento de estoque e as necessidades de pedidos. O uso de automação e insights em tempo real pode aumentar o impacto geral nos negócios. \u003C/p\u003E\r\n\u003Ch4\u003EMelhora da experiência do cliente\u003C/h4\u003E\r\n\u003Cp\u003EAs organizações podem analisar os dados dos clientes para entender melhor as necessidades e o comportamento deles. Isso permite que as organizações criem campanhas mais personalizadas para cada tipo de cliente, colocando as necessidades específicas dos clientes em primeiro lugar. As organizações podem desenvolver perfis de clientes para oferecer recomendações personalizadas com base em informações demográficas, engajamento de marketing e muito mais. \u003C/p\u003E\r\n\u003Ch4\u003EOtimiza operações e prevê tendências\u003C/h4\u003E\r\n\u003Cp\u003EEm todas as organizações, cada departamento pode usar os dados para otimizar suas atividades. Isso pode incluir simplificar processos e reduzir desperdícios usando análise de big data para prever necessidades de manutenção, prever tendências, implementar melhorias de processos e realizar mudanças de pessoal. \u003C/p\u003E\r\n\u003Ch4\u003EPromove a inovação\u003C/h4\u003E\r\n\u003Cp\u003EO big data abre portas para recursos de previsão e análise de dados preditiva. Com o uso de big data, as organizações podem analisar as tendências, o comportamento e o feedback dos clientes, além de predisposições mais amplas de mercado para melhorar os produtos existentes ou desenvolver novos produtos.\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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-d00b32342c","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafios de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-da93a76728","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEmbora o conceito de big data tenha mudado muito a forma como as organizações obtêm insights significativos sobre seus negócios, isso não aconteceu sem a presença de desafios. A seguir, analisamos algumas das dificuldades mais comuns que as organizações enfrentam em relação a big data. \u003C/p\u003E\r\n\u003Ch4\u003EPrivacidade e segurança dos dados\u003C/h4\u003E\r\n\u003Cp\u003EA constante mudança das leis e regulamentações é um grande desafio. As organizações devem cumprir várias leis de privacidade e segurança, como \u003Ca href=\"https://www.snowflake.com/en/blog/gdpr-best-practices/\"\u003ERGPD\u003C/a\u003E e \u003Ca href=\"https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act\"\u003EHIPAA\u003C/a\u003E, o que pode ser difícil quando os conjuntos de dados são grandes e crescem de modo constante. Os clientes também esperam muito que as empresas estejam protegendo seus dados pessoais. Isso aumenta a pressão para que as empresas implementem medidas de segurança de dados que protejam os dados dos clientes.\u003C/p\u003E\r\n\u003Ch4\u003EEscalabilidade\u003C/h4\u003E\r\n\u003Cp\u003EQuanto mais dados, maior é a necessidade de armazenamento e de recursos de processamento. Gerenciar essas ferramentas de armazenamento requer recursos caros e especializados. Mesmo com os serviços de nuvem, armazenar e gerenciar todos esses dados é uma tarefa árdua e que exige muitos recursos. As organizações precisam recrutar talentos especializados capazes de se conectar e colaborar de forma eficaz e eficiente com a força de trabalho existente. \u003C/p\u003E\r\n\u003Ch4\u003EFalta de habilidades\u003C/h4\u003E\r\n\u003Cp\u003EPara lidar com o recebimento de grandes volumes de dados complexos, as organizações precisam de funcionários capacitados, incluindo analistas e engenheiros de dados, para gerenciar e interpretar os dados. Ter dados é uma coisa. Mas, poder contar com os funcionários certos para interpretá-los, identificar padrões e fazer recomendações é onde está o verdadeiro valor. As organizações também precisam de líderes de negócios tecnicamente especializados, receptivos à tomada de decisões inovadoras e baseadas em dados que vão além de uma planilha ou da intuição pessoal. \u003C/p\u003E\r\n\u003Ch4\u003EComplexidade de integração\u003C/h4\u003E\r\n\u003Cp\u003EÉ difícil combinar vários tipos de fontes de dados de modo eficaz. Por exemplo, os varejistas podem tentar combinar dados de vendas na loja e de cliques no site, ou usar dados de compra e envio para oferecer melhor suporte a uma consulta do cliente. Um sistema de saúde pode precisar reunir históricos eletrônicos de saúde, resultados de exames laboratoriais e informações de seguro-saúde para criar um plano de tratamento completo para um paciente. Para gerenciar esses dados, essas integrações exigem o uso de novas ferramentas e tecnologias, além de analistas de dados especializados e outros recursos de TI.\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__222865509":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-9179be8396","additionalClasses":"headline-decoration","type":"heading2","lines":["Exemplos de big data e casos de uso"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-867f55377b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEm todos os setores, diversas funções de negócios podem empregar o big data com sucesso. Veja alguns exemplos de como o big data funciona em diferentes setores:\u003C/p\u003E\r\n\u003Ch4\u003ESaúde\u003C/h4\u003E\r\n\u003Cp\u003EDe forma bem elaborada, o setor de \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/healthcare-and-life-sciences/\"\u003Esaúde\u003C/a\u003E pode aproveitar o big data para apoiar a missão das instituições de saúde e, ao mesmo tempo, cumprir com os requisitos regulatórios. As organizações de saúde podem melhorar a experiência do paciente e do profissional prestador de serviços de saúde ao \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/patient-and-member-360/\"\u003Ecombinar vários conjuntos de dados de pacientes para uma visão holística da saúde do paciente\u003C/a\u003E. O big data pode reunir históricos eletrônicos de saúde, histórico familiar, dados de dispositivos portáteis, informações sobre o seguro médico e muito mais para influenciar o curso de atendimento a um paciente. Dados relativos às necessidades de agendamento e aos insumos médicos podem ajudar a otimizar a contratação de pessoal e o funcionamento da cadeia de fornecedores. Além disso, a \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003Egovernança de dados\u003C/a\u003E de ponta a ponta pode ajudar seguradoras e fornecedores de serviços de saúde a cumprir requisitos rigorosos de privacidade. \u003C/p\u003E\r\n\u003Ch4\u003EFinanças\u003C/h4\u003E\r\n\u003Cp\u003ENo setor \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/financial-services/\"\u003Efinanceiro\u003C/a\u003E, as organizações podem usar big data para analisar os hábitos de compra de um cliente e \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Edetectar possíveis roubos de identidade praticamente em tempo real\u003C/a\u003E. Ao dar um passo à frente, elas podem implementar recursos de segurança adicionais em torno da autenticação. Ter uma \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/customer-360-in-financial-services/\"\u003Evisão abrangente das atividades e de outras informações do cliente\u003C/a\u003E pode ajudar as empresas a permanecerem alinhadas aos requisitos de segurança e conformidade em constante evolução. As organizações financeiras podem atender melhor seus clientes usando dados para analisar os hábitos de compra deles. Elas podem usar esse conhecimento adquirido para recomendar ofertas especializadas a fim de ajudar os clientes a alcançar seus objetivos financeiros. \u003C/p\u003E\r\n\u003Ch4\u003EVarejo e comércio eletrônico\u003C/h4\u003E\r\n\u003Cp\u003EInsights obtidos a partir de big data podem ajudar a viabilizar iniciativas de marketing eficazes e direcionadas. Ao monitorar as jornadas dos clientes e os padrões de compra, os \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/retail-consumer-goods/\"\u003Evarejistas\u003C/a\u003E podem entender melhor as necessidades e os desejos dos clientes. Esses insights podem ser usados para desenvolver campanhas de marketing personalizadas com recomendações de produtos específicos de acordo com o cliente. Os varejistas também podem gerenciar melhor operações das cadeias de fornecedores deles, além de projeções de vendas e outros fatores, bem como melhorar o desenvolvimento de produtos com base no feedback dos clientes. \u003C/p\u003E\r\n\u003Ch4\u003ESetor industrial\u003C/h4\u003E\r\n\u003Cp\u003EOs \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/manufacturing/\"\u003Efabricantes\u003C/a\u003E podem extrair insights de big data para melhorar a fabricação, as linhas de montagem, a \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/manufacturing/improve-supply-chain-performance/\"\u003Egestão da cadeia de fornecedores\u003C/a\u003E e muito mais. Por exemplo, as organizações podem usar os \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/power-smart-manufacturing/\"\u003Edados de sensores\u003C/a\u003E para prever quando é necessária uma manutenção de rotina e prever falhas nos equipamentos para evitar tempo de inatividade e reduzir os gastos gerais com reparos. Ao identificar os padrões que indicam a probabilidade de falha, antes mesmo dela ocorrer, os fabricantes podem planejar melhor e alocar recursos de forma mais eficaz. \u003C/p\u003E\r\n\u003Ch4\u003EGoverno e serviço público\u003C/h4\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/public-sector/\"\u003EGovernos e organizações de serviços públicos\u003C/a\u003E podem usar big data para entender melhor as necessidades de suas comunidades. As organizações podem superar as preocupações relativas à segurança, por exemplo, reunindo dados de tráfego e tendências de motoristas para otimizar as autoestradas e melhorar a manutenção das mesmas. Isso pode ajudar as organizações governamentais a realizar melhorias com mais rapidez, aumentando a confiança da população de que o governo estão agindo em seu melhor interesse.\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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-e714d22c6e","additionalClasses":"headline-decoration","type":"heading2","lines":["Práticas recomendadas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-45015f392c","additionalClasses":"list--blue-bullets","text":"\u003Ch4\u003EDefinir objetivos claros\u003C/h4\u003E\r\n\u003Cp\u003EPara ajudar as organizações a se manterem focadas e não se perderem com muita informação, a análise de dados deve apoiar claros objetivos de negócios. Ao alinhar as \u003Ca href=\"https://www.snowflake.com/pt_br/solutions/industries/public-sector/\"\u003Einiciativas de análise de dados\u003C/a\u003E às prioridades estratégicas, é possível minimizar esforços improdutivos e retrabalhos, permitindo que os líderes obtenham insights de alto valor com maior agilidade.\u003C/p\u003E\r\n\u003Ch4\u003EFacilitar uma boa qualidade e relevância dos dados\u003C/h4\u003E\r\n\u003Cp\u003EDados de baixa qualidade ou irrelevantes podem levar a decisões erradas. Por exemplo, um varejista pode fazer projeções de vendas incorretas se o conjunto de dados incluir registros duplicados, vendas de produtos diferentes daqueles que estão sob análise ou dados muito antigos para serem utilizados. As organizações devem empregar fortes estruturas de governança de dados, além de ferramentas e técnicas de qualidade de dados confiáveis para que os dados sejam pertinentes, precisos e relevantes.\u003C/p\u003E\r\n\u003Ch4\u003EUsar soluções de armazenamento e processamento com escala dimensionável\u003C/h4\u003E\r\n\u003Cp\u003ECom a expansão dos dados, as organizações precisam aumentar sua capacidade de armazenamento de dados, recursos de pessoal e processos de TI para oferecer suporte à gestão e à análise de dados em escala de petabytes e acima. Soluções modernas, com escala dimensionável, incluem sistemas de armazenamento distribuído, \u003Ca href=\"https://www.snowflake.com/pt_br/product/data-engineering/\"\u003Edata lakes baseados na nuvem\u003C/a\u003E e estruturas de processamento avançadas que podem, de modo automático, ajustar a escala dos recursos conforme necessário, com o máximo de eficiência.\u003C/p\u003E\r\n\u003Ch4\u003EPriorizar a privacidade e a segurança\u003C/h4\u003E\r\n\u003Cp\u003EA proteção dos dados confidenciais e o cumprimento dos regulamentos de privacidade e conformidade em constante evolução exigem que as organizações implementem diretrizes eficazes para o processamento de dados. Regulamentos importantes, como RGDP e HIPAA, exigem medidas de segurança rigorosas para prevenir violações dos dados confidenciais dos clientes. Os clientes querem confiar na segurança de seus dados e, portanto, proteger esses dados é uma prioridade máxima para qualquer empresa. \u003C/p\u003E\r\n\u003Ch4\u003EPromover uma cultura colaborativa e baseada em dados\u003C/h4\u003E\r\n\u003Cp\u003ECientistas de dados, equipes de TI e líderes empresariais devem colaborar para usar os dados para alcançar os objetivos de negócios. Técnicas para criar uma cultura de dados ampla e colaborativa incluem a criação de equipes interfuncionais, projetos ou competições de inovação interna. Outras ideias incluem incentivar o desenvolvimento piloto de novas ferramentas ou processos, disponibilizar recursos externos de treinamento e compartilhar dicas, técnicas e descobertas por meio de sessões de aprendizado e reuniões de “lunch-and-learn”.\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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-5f37ff712c","additionalClasses":"headline-decoration","type":"heading2","lines":["Futuro de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d35439902d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO big data é complexo, incluindo conjuntos de dados diversos e variados. Embora esse seja um ativo para as organizações, pois pode gerar um fluxo contínuo de insights em potencial, também pode ser um desafiado para as organizações armazenar e analisar dados de forma eficaz para gerar resultados valiosos. \u003C/p\u003E\n\u003Cp\u003EOlhando para o futuro, os recursos de big data, como os próprios dados, vão continuar a crescer. A evolução contínua das ferramentas e das tecnologias de análise de big data vai continuar a motivar a inovação e a aumentar as discussões éticas. As empresas terão que lidar com o modo de armazenar, gerenciar e analisar dados de forma ética. \u003C/p\u003E\n\u003Cp\u003EAs \u003Ca href=\"https://www.snowflake.com/pt_br/product/ai/\"\u003Einovações em IA e ML\u003C/a\u003E, incluindo o surgimento do processamento de linguagem natural e da \u003Ca href=\"https://www.snowflake.com/pt_br/product/features/cortex/\"\u003EIA generativa\u003C/a\u003E na análise de dados, serão assuntos cada vez mais frequentes. Dessa forma, os dados serão democratizados, permitindo que usuários menos técnicos \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/data-agents/\"\u003E\"façam perguntas sobre os dados\"\u003C/a\u003E diretamente, sem exigir que cientistas de dados convertam uma questão de negócios em código. O resultado pode ser decisões mais rápidas e melhores. A Internet das Coisas, onde vários dispositivos na rede de uma organização fornecem dados de sensores, e o processamento de borda, onde o processamento de dados é feito na periferia, vão gerar mais dados, aumentando a necessidade de automatizar as ações. \u003C/p\u003E\n\u003Cp\u003EEm resumo, o futuro do big data é ainda mais amplo. Mais dados, mais ferramentas, mais necessidade de insights e mais valor para as organizações que aprenderem a controlá-los.\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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8e0c41967b","additionalClasses":"headline-decoration","type":"heading2","lines":["Perguntas frequentes sobre big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-d3cc3024ba","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Que ferramentas e tecnologias estão associadas ao big data?","richText":"\u003Cp\u003E1. \u003Cb\u003EGestão e armazenamento de dados:\u003C/b\u003E voltados para armazenar e gerenciar grandes conjuntos de dados, que podem ser estruturados, não estruturados ou semiestruturados. As tecnologias incluem sistemas de arquivos distribuídos, bancos de dados NoSQL, \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/data-warehouse/\"\u003Edata warehouses\u003C/a\u003E e \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E2. \u003Cb\u003EComputação e processamento de dados:\u003C/b\u003E desenvolvidos para transferir dados entre bancos de dados relacionais. Com eficiência, eles coletam, agregam e movem dados de diferentes fontes para um armazenamento centralizado.\u003C/p\u003E\n\u003Cp\u003E3. \u003Cb\u003EData warehousing e análise de dados:\u003C/b\u003E facilitam a leitura, a gravação e o gerenciamento de grandes conjuntos de dados por meio de data warehouses na nuvem, com escala altamente dimensionável, sem servidor e com boa relação custo-benefício.\u003C/p\u003E\n\u003Cp\u003E4. \u003Cb\u003EVisualização de dados e criação de relatórios:\u003C/b\u003E esses dashboards interativos são usados pelas equipes de inteligência de mercado (BI) para visualização de dados, criação de relatórios e análise de dados avançada. \u003C/p\u003E\n\u003Cp\u003E5. \u003Cb\u003EAprendizado de máquina e IA:\u003C/b\u003E fornecendo poder computacional avançado, esses tipos de software baseados em algoritmos encontram padrões e insights em big data.\u003C/p\u003E\n\u003Cp\u003E6. \u003Cb\u003EOrquestração e gerenciamento:\u003C/b\u003E de modo programático, criam, agendam e monitoram fluxos de trabalho por meio de sistemas de código aberto para automatizar a implementação, o ajuste da escala e o gerenciamento de aplicações.\u003C/p\u003E\n"},{"title":"Como o big data se distingue dos dados tradicionais?","richText":"\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EO big data possui vários grandes conjuntos de dados com dados de diferentes formatos, incluindo texto, áudio e vídeo. Por ser não estruturado, ele não se ajusta de forma direta ou organizada aos bancos de dados tradicionais, exigindo um processamento mais avançado para gerar valor. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EOs dados tradicionais são formados por dados estruturados com parâmetros claros e podem ser facilmente armazenados e analisados em bancos de dados tradicionais padrão.  \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn 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financeiras.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["WHOOP melhora a previsão financeira de IA/ML e a experiência dos associados"],":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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Ler o caso"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f81c05bb-1dac-41bf-8f3c-854e1d7492fa/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","alt":"simon data logo","lazyEnabled":true,"height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-79484ea045",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-6f2782de63","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003ELíderes de engenharia de dados do Capital One compartilham como o Snowflake resolve vários desafios de dados em uma das maiores instituições financeiras do setor bancário.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Capital One investe no Snowflake para ciência de 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