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18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_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":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-20654b66de","additionalClasses":"headline-decoration","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d58765653b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl big data surgió en la década de 1990 con los albores de Internet y la adopción generalizada de prácticas empresariales centradas en lo digital. Las organizaciones obtuvieron acceso a una afluencia de puntos de datos sobre sus funciones empresariales, clientes y sectores en su conjunto. El big data está formado por conjuntos de datos grandes y complicados que han ido creciendo y expandiéndose más allá de las capacidades operativas de los sistemas de gestión de datos tradicionales.\u003C/p\u003E\r\n\u003Cp\u003EEl big data a menudo incluye no solo grandes conjuntos de datos estructurados tradicionales, sino también datos \u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003Esemiestructurados\u003C/a\u003E y \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Eno estructurados\u003C/a\u003E en una variedad de formatos. \u003C/p\u003E\r\n\u003Cp\u003ELas empresas ahora pueden recopilar datos fiables en una variedad de formatos, incluidos entre otros, archivos de audio, páginas web, procesos internos, transacciones con los clientes y mucho más. Dada la complejidad del big data, se necesitan diferentes herramientas y recursos para recopilar, gestionar y analizar correctamente toda la información. \u003C/p\u003E\r\n\u003Cp\u003ELa aparición y el crecimiento constante del big data en las últimas décadas ha brindado a las organizaciones enormes oportunidades para descubrir nueva información y mejorar la toma de decisiones. \u003C/p\u003E\r\n\u003Cp\u003EEn este artículo, profundizaremos en las características únicas del big data y cómo, si se aprovecha de forma eficaz, puede ayudar a las organizaciones a mejorar su eficiencia, innovar y crecer.\u003C/p\u003E\r\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-e1ea81890e","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es el big data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ab73e6e502","text":"\u003Cp\u003EEl big data hace referencia a conjuntos de datos grandes y complejos que pueden incluir datos estructurados, como datos de inventario, y datos no estructurados, como archivos de audio o contenido de redes sociales. Dado que estos conjuntos de datos son enormes y siguen creciendo constantemente, a menudo no pueden estar contenidos en los sistemas tradicionales de gestión de datos.  \u003C/p\u003E\r\n\u003Cp\u003EEn los últimos años, los costes de almacenamiento de datos también han disminuido, por lo que las organizaciones pueden almacenar y conservar más datos. Si bien esto aumenta las posibilidades de extraer información, al mismo tiempo incrementa la complejidad del proceso. Actualmente, las organizaciones necesitan \u003Ca href=\"https://www.snowflake.com/es/product/analytics/\"\u003Eherramientas analíticas\u003C/a\u003E más avanzadas y una mayor pericia para extraer conocimiento valioso de estos grandes conjuntos de datos y tomar decisiones fundamentadas. \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_":{"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-c4c0823524","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Cómo funciona el big data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-887f417386","text":"\u003Cp\u003EEl big data es una colección de grandes cantidades de conjuntos de datos diversos y complejos. Funciona mediante la recopilación de grandes volúmenes de datos de diversas fuentes, a menudo en tiempo real. Estas fuentes de datos incluyen métricas de los procesos de negocio internos, el sentimiento de los clientes, la interacción y mucho más. \u003C/p\u003E\r\n\u003Cp\u003EDebido a la velocidad a la que se recopilan los datos, los sistemas necesitan procesar mucha información. Para gestionarlo todo, los \u003Ca href=\"https://www.snowflake.com/es/fundamentals/what-is-data-engineering/\"\u003Eingenieros de datos\u003C/a\u003E y los analistas deben procesar y estructurar los datos mediante el uso de sistemas de computación especializados basados en la nube, que tienen más capacidad de almacenamiento y procesamiento que los sistemas tradicionales. Para tener en cuenta todos los datos, las organizaciones utilizan prácticas de aprendizaje automático (ML) genéricas y especializadas para analizarlos de forma eficaz. Estas buscan patrones y tendencias en los datos para fundamentar la toma de decisiones empresariales transformadoras.\u003C/p\u003E\r\n\u003Cp\u003EPara aprovechar al máximo el big data y optimizar su impacto empresarial, las organizaciones se han adaptado a sus prácticas y procesos de datos. A día de hoy, reconocen que necesitan las capacidades de recopilación, procesamiento y almacenamiento, así como de análisis de datos más potentes y actualizados.\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-v2-10364f7650","additionalClasses":"headline-decoration","type":"heading2","lines":["Características clave del big data (las 5 V)"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-dd4f418be2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl big data tiene cinco características clave conocidas como las “cinco V del big data”, que ilustran en qué se diferencia el big data de los conjuntos de datos tradicionales. Las V son: volumen, velocidad, variedad, veracidad y valor. A continuación, desglosamos cada característica:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EVolumen\u003C/h4\u003E\n\u003Cp\u003EHoy en día, las organizaciones simplemente disponen de más datos para almacenar, gestionar y analizar. Con más información a su disposición, deben adaptarse para utilizar y gestionar su datos de la mejor manera posible a medida que siguen aumentando.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EVelocidad\u003C/h4\u003E\n\u003Cp\u003ELas organizaciones crean datos más rápido que nunca. Esta realidad pone bajo presión a las organizaciones para que procesen y analicen los datos a un ritmo más rápido —a menudo en tiempo real— con el fin de tomar decisiones rápidas y de gran impacto. Los clientes también esperan comentarios casi instantáneos sobre los productos recomendados para comprar. Para responder a las demandas de los clientes, las organizaciones deben adaptarse. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EVariedad\u003C/h4\u003E\n\u003Cp\u003EEl big data incluye diferentes formatos de datos, incluidos los no estructurados, como texto en formato libre, imágenes, vídeos y mucho más. También incluye datos estructurados, como hojas de cálculo, y datos semiestructurados, como datos de sensores. Para gestionar esta variedad se necesitan herramientas y bases de datos flexibles que permitan realizar análisis de datos exhaustivos. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EVeracidad\u003C/h4\u003E\n\u003Cp\u003ELa precisión es un problema con el big data. Debido a las múltiples fuentes y tipos, y a la gran cantidad de datos, el potencial de error es alto. Sin embargo, disponer de datos fiables es esencial para realizar analíticas precisas y tomar decisiones bien fundamentadas. Las organizaciones deben encargarse de garantizar la calidad de los datos mediante iniciativas de limpieza, validación y verificación de datos. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EValor\u003C/h4\u003E\n\u003Cp\u003ELos datos precisos y de alta calidad son una fuente de valor empresarial considerable, ya que pueden impulsar el crecimiento de los ingresos, destapar nuevas eficiencias y estimular la innovación. Reconocer dónde puede encontrarse valor potencial en el big data puede ayudar a las organizaciones a crear una estrategia más eficaz para explotarlo. \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__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-v2-5618c6a6d7","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas del big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1b126a2101","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl big data puede mejorar notablemente las operaciones empresariales y, por tanto, optimizar los resultados. Entre las ventajas del big data se incluyen: \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EImpulsa la toma de decisiones estratégicas\u003C/h4\u003E\n\u003Cp\u003EEl big data permite a las organizaciones tomar decisiones más fundamentadas y estratégicas. Para la gestión de la cadena de suministro, las organizaciones pueden analizar conjuntos de datos complejos de forma eficaz y metódica, así como elaborar predicciones fiables para gestionar mejor el suministro del inventario y los pedidos. El uso de la automatización y la información en tiempo real pueden incrementar el impacto estratégico a nivel general. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMejora las experiencias de los clientes\u003C/h4\u003E\n\u003Cp\u003ELas organizaciones pueden analizar los datos de los clientes para comprender mejor sus necesidades y comportamiento. De esta forma, pueden crear campañas más personalizadas para cada tipo de cliente, lo que sitúa sus necesidades únicas en el eje central de la estrategia. Las organizaciones pueden desarrollar perfiles de clientes para ofrecer personalización a medida en función de la información demográfica, las interacciones de marketing y mucho más. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EOptimiza las operaciones y predice tendencias\u003C/h4\u003E\n\u003Cp\u003EEn todas las organizaciones, todos los departamentos pueden aprovechar los datos para optimizar las operaciones. Esto puede incluir la optimización de procesos y la reducción del despilfarro de recursos mediante el análisis de big data para prever las necesidades de mantenimiento, predecir tendencias, implementar mejoras en los procesos y gestionar los turnos de personal. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EFacilita la innovación\u003C/h4\u003E\n\u003Cp\u003EEl big data abre las puertas a capacidades de analíticas predictivas y previsión. Con el big data, las organizaciones pueden revisar las tendencias, los comportamientos de los clientes, sus comentarios y las tendencias generales del mercado para mejorar los productos existentes o desarrollar otros nuevos.\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__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-cd7e6f594a","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafíos del big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cfe70cdd17","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAunque el big data ha cambiado significativamente la forma en que las organizaciones obtienen información relevante sobre sus negocios, no está exento de desafíos. A continuación, presentamos algunas de las dificultades más frecuentes a las que se enfrentan las empresas con respecto al big data. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EPrivacidad y seguridad de los datos\u003C/h4\u003E\r\n\u003Cp\u003ELa constante evolución de las leyes y las normativas supone un reto importante. Las organizaciones deben cumplir varias leyes de privacidad y seguridad, como el \u003Ca href=\"https://www.snowflake.com/en/blog/gdpr-best-practices/\"\u003ERGPD\u003C/a\u003E en la UE y la \u003Ca href=\"https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act\"\u003EHIPAA\u003C/a\u003E en EE. UU., que puede resultar complejo cuando los conjuntos de datos son grandes y están en constante crecimiento. Asimismo, los clientes albergan grandes expectativas de que las empresas protejan sus datos personales. Esto aumenta la presión sobre las empresas para que implementen medidas de seguridad que protejan los datos de los clientes.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EEscalabilidad\u003C/h4\u003E\r\n\u003Cp\u003ECuantos más datos, más recursos de almacenamiento y procesamiento se necesitan. La gestión de estas herramientas de almacenamiento requiere recursos costosos y especializados. Incluso con los servicios en la nube, almacenar y gestionar todos esos datos es una tarea exigente y requiere muchos recursos. Las organizaciones necesitan contratar a profesionales especializados que puedan conectar y colaborar de forma eficaz con la plantilla existente. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFalta de competencias\u003C/h4\u003E\r\n\u003Cp\u003EDebido a la afluencia de grandes cantidades de datos complejos, las organizaciones necesitan empleados cualificados, como analistas e ingenieros de datos, para gestionar los datos y darles sentido. Disponer de datos es una cosa, pero contar con los empleados adecuados para interpretarlos, identificar patrones y hacer recomendaciones es donde reside el verdadero valor. Las organizaciones también necesitan líderes empresariales con experiencia técnica que estén dispuestos a tomar decisiones innovadoras y basadas en datos que vayan más allá del instinto y de las familiares hojas de cálculo. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EIntegración compleja\u003C/h4\u003E\r\n\u003Cp\u003EEs difícil combinar de manera eficaz varios tipos de fuentes de datos. Por ejemplo, los minoristas pueden buscar la combinación de datos de clics en el sitio web y de ventas en las tiendas físicas, o bien utilizar los datos de compras y envíos para respaldar mejor una consulta de los clientes. Un sistema sanitario puede tener que combinar historiales clínicos electrónicos, resultados de laboratorio y seguros para elaborar un plan de tratamiento completo para un paciente. Dichas integraciones requieren nuevas herramientas y tecnologías para gestionar esta afluencia de datos, analistas de datos especializados y otros recursos informáticos.\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":{"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-v2-59abdcc032","additionalClasses":"headline-decoration","type":"heading2","lines":["Ejemplos y casos de uso de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-9bbbd47d64","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEn todos los sectores, diversas funciones empresariales pueden utilizar el big data para lograr grandes objetivos. A continuación, se muestran algunos ejemplos de cómo se utiliza el big data en diferentes sectores:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003ESanidad\u003C/h4\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/healthcare-and-life-sciences/\"\u003Easistencia sanitaria\u003C/a\u003E puede aprovechar concienzudamente el big data para respaldar sus requisitos normativos. Las organizaciones sanitarias pueden mejorar la experiencia del paciente y del proveedor sanitario \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/patient-and-member-360/\"\u003Emediante la combinación de varios conjuntos de datos de pacientes para obtener una visión integral de su salud\u003C/a\u003E. El big data puede reunir historias clínicas electrónicas, antecedentes familiares, datos de dispositivos portátiles, información sobre seguros y mucho más para influir en el proceso de tratamiento del paciente. Los datos sobre las necesidades de programación y los suministros médicos pueden ayudar a optimizar las operaciones del personal y la cadena de suministro. Además, la \u003Ca href=\"https://www.snowflake.com/es/product/features/horizon/\"\u003Egobernanza de datos\u003C/a\u003E integral puede ayudar a las aseguradoras y los proveedores sanitarios a cumplir los estrictos requisitos de privacidad. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFinanzas\u003C/h4\u003E\r\n\u003Cp\u003EEn el \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/financial-services/\"\u003Esector financiero\u003C/a\u003E, las organizaciones pueden utilizar big data para analizar los hábitos de gasto de los clientes y \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Edetectar posibles robos de identidad en tiempo real\u003C/a\u003E. Si van un paso más allá, pueden implementar funciones de seguridad adicionales en torno a la autenticación. Disponer de una \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/customer-360-in-financial-services/\"\u003Evisión completa de las transacciones y otra información de los clientes\u003C/a\u003E puede ayudar a las organizaciones a mantenerse alineadas con los requisitos de seguridad y cumplimiento, que evolucionan continuamente. Las organizaciones financieras pueden prestar un mejor servicio a sus clientes al utilizar los datos para analizar sus hábitos de gasto. Pueden utilizar esa información para recomendar ofertas especializadas que ayuden a los clientes a alcanzar sus objetivos financieros. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003ERetail y comercio electrónico\u003C/h4\u003E\r\n\u003Cp\u003ELa información del big data puede ayudar a habilitar iniciativas de marketing focalizadas y eficaces. Al hacer un seguimiento del recorrido y los patrones de gasto de los clientes, \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/retail-consumer-goods/\"\u003Elos minoristas\u003C/a\u003E pueden comprender mejor sus necesidades y deseos. Esta información les permite desarrollar campañas de marketing personalizadas con recomendaciones de productos específicas para el cliente. También pueden gestionar mejor las operaciones de su cadena de suministro, las proyecciones de ventas y otros factores, y mejorar el desarrollo de sus productos en función de los comentarios de los clientes. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFabricación\u003C/h4\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/es/solutions/industries/manufacturing/\"\u003ELos fabricantes\u003C/a\u003E pueden extraer información del big data para mejorar la fabricación, las líneas de montaje, la \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/improve-supply-chain-performance/\"\u003Egestión de la cadena de suministro\u003C/a\u003E y mucho más. Por ejemplo, las organizaciones pueden utilizar \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/manufacturing/power-smart-manufacturing/\"\u003Edatos de sensores\u003C/a\u003E para predecir cuándo se necesita un mantenimiento rutinario y predecir fallos en los equipos a fin de evitar el tiempo de inactividad y reducir el gasto general en reparaciones. Al identificar los patrones que predicen cuándo se puede producir un fallo de funcionamiento antes de que se produzca, los fabricantes pueden planificar mejor y asignar recursos de forma más eficaz. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EOrganismos gubernamentales y servicios públicos\u003C/h4\u003E\r\n\u003Cp\u003ELos \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/public-sector/\"\u003Eorganismos gubernamentales y los servicios públicos\u003C/a\u003E pueden utilizar big data para comprender mejor las necesidades de sus comunidades. Pueden adelantarse a las preocupaciones de seguridad, por ejemplo, mediante la recopilación de datos de tráfico y tendencias de conductores para optimizar las carreteras y mejorar el mantenimiento de las mismas. Esto puede ayudar a los organismos gubernamentales a realizar mejoras más rápido, lo que refuerza la confianza de los ciudadanos en las instituciones.\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-v2-72e1f3f4e0","additionalClasses":"headline-decoration","type":"heading2","lines":["Prácticas recomendadas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-33913cb32a","additionalClasses":"list--blue-bullets","text":"\u003Ch4\u003EDefinir objetivos claros\u003C/h4\u003E\r\n\u003Cp\u003EPara ayudar a las organizaciones a mantenerse centradas y no perderse en el exceso de información, el análisis de datos debe respaldar objetivos empresariales claros. Alinear las \u003Ca href=\"https://www.snowflake.com/es/solutions/industries/public-sector/\"\u003Einiciativas analíticas\u003C/a\u003E con las prioridades puede minimizar los inicios en falso y los callejones sin salida, y, por tanto, brindar información de alto valor más rápido a los equipos directivos.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFavorecer la buena calidad y relevancia de los datos\u003C/h4\u003E\r\n\u003Cp\u003ELos datos de baja calidad o irrelevantes pueden dar lugar a decisiones erróneas. Por ejemplo, un minorista podría hacer proyecciones de ventas deficientes si el conjunto de datos incluye registros duplicados, ventas de productos distintas de la que se analiza o datos demasiado antiguos que no son útiles. Por esta razón, las organizaciones deben establecer marcos de gobernanza de datos sólidos, así como herramientas y técnicas fiables de calidad de datos, de manera que los datos sean pertinentes, precisos y relevantes.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EUsar soluciones escalables de almacenamiento y procesamiento\u003C/h4\u003E\r\n\u003Cp\u003ECon la expansión de los datos, las organizaciones deben aumentar su capacidad de almacenamiento de datos, personal especializado y procesos de tecnología de la información (TI) para respaldar la gestión y el análisis de datos a escala de petabytes o superior. Las soluciones escalables modernas incluyen sistemas de almacenamiento distribuido, \u003Ca href=\"https://www.snowflake.com/es/product/data-engineering/\"\u003Edata lakes basados en la nube\u003C/a\u003E y marcos de procesamiento avanzados que pueden ampliar los recursos automáticamente según sea necesario, con la máxima eficiencia.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EDar prioridad a la privacidad y la seguridad\u003C/h4\u003E\r\n\u003Cp\u003ELa protección de los datos confidenciales y el cumplimiento de las cambiantes normativas de privacidad y cumplimiento exigen que las organizaciones implementen directrices eficaces para procesar los datos. Normativas tan importantes como el RGPD y la HIPAA requieren estrictas medidas de seguridad para evitar las filtraciones de datos confidenciales de los clientes. Los clientes quieren confiar en que sus datos están seguros, por lo que protegerlos es una alta prioridad para cualquier empresa. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EFomentar una cultura colaborativa y basada en datos\u003C/h4\u003E\r\n\u003Cp\u003ELos científicos de datos, los equipos de TI y los líderes empresariales deben colaborar para utilizar los datos y alcanzar sus objetivos. Entre las técnicas para crear una cultura de datos amplia y colaborativa se incluyen la creación de equipos interfuncionales, proyectos internos de innovación o concursos. Otras posibilidades son alentar a que se lleven a cabo pruebas piloto de nuevas herramientas o procesos, poner a disposición recursos de aprendizaje externos y compartir consejos, técnicas y hallazgos a través de sesiones de aprendizaje y almuerzo.\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-v2-07f4b92049","additionalClasses":"headline-decoration","type":"heading2","lines":["El futuro del big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-db4eedc34f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl big data es complejo, e incluye conjuntos de datos diversos y variables. Si bien esto es un activo para las organizaciones al generar un flujo continuo de información potencial, al mismo tiempo supone un desafío a la hora de implementar soluciones eficaces de almacenamiento y análisis de los datos que permitan generar resultados valiosos. \u003C/p\u003E\r\n\u003Cp\u003EDe cara al futuro, las capacidades del big data, al igual que las de los propios datos, no harán sino seguir creciendo. La evolución continua de las herramientas y tecnologías de analíticas de big data impulsará la innovación y planteará consideraciones éticas. Las empresas tendrán que lidiar con la forma en que almacenan, gestionan y analizan los datos de una manera ética. \u003C/p\u003E\r\n\u003Cp\u003ELas \u003Ca href=\"https://www.snowflake.com/es/product/ai/\"\u003Einnovaciones en IA y ML\u003C/a\u003E, incluida la aparición del procesamiento del lenguaje natural y la \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003EIA generativa\u003C/a\u003E en las analíticas de datos, serán cada vez más habituales. De esta forma, los datos se democratizarán, ya que los usuarios menos técnicos podrán \u003Ca href=\"https://www.snowflake.com/es/product/use-cases/data-agents/\"\u003E“hacer preguntas a los datos”\u003C/a\u003E directamente, sin necesidad de que los científicos de datos conviertan una pregunta empresarial en código. El resultado puede ser una toma de decisiones más rápida y más fundamentada. El Internet de las cosas, en el que varios dispositivos de la red de una organización proporcionan datos de sensores, y el edge computing (o computación perimetral), donde el procesamiento de datos se realiza en la periferia, generarán más datos y aumentará la necesidad de automatizar acciones. \u003C/p\u003E\r\n\u003Cp\u003EEn resumen, el futuro del big data es aún más amplio: más datos, más herramientas, más hambre de conocimientos y más valor para las organizaciones que aprendan a dominarlo.\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__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-v2-04230e05ff","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-af1d3ac963","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"¿Qué herramientas y tecnologías están asociadas al big data?","richText":"\u003Cp\u003E1. \u003Cb\u003EAlmacenamiento y gestión de datos:\u003C/b\u003E Herramientas diseñadas para almacenar y gestionar conjuntos de datos masivos, que pueden ser estructurados, no estructurados o semiestructurados. Las tecnologías incluyen sistemas de archivos distribuidos, bases de datos NoSQL, \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-warehouse/\"\u003Ealmacenes de datos\u003C/a\u003E y \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E2. \u003Cb\u003EProcesamiento y computación de datos:\u003C/b\u003E Herramientas creadas para transferir datos entre bases de datos relacionales. Recopilan, agregan y mueven datos de diferentes fuentes de forma eficiente a un almacén de datos centralizado.\u003C/p\u003E\n\u003Cp\u003E3. \u003Cb\u003EAlmacenamiento y analíticas de datos:\u003C/b\u003E Herramientas que facilitan la lectura, escritura y gestión de grandes conjuntos de datos mediante almacenes de datos en la nube serverless, rentables y con gran escalabilidad.\u003C/p\u003E\n\u003Cp\u003E4. \u003Cb\u003EVisualización de datos y elaboración de informes:\u003C/b\u003E Herramientas utilizadas por los equipos de inteligencia empresarial, como paneles interactivos para la visualización de datos, elaboración de informes y analíticas eficaces. \u003C/p\u003E\n\u003Cp\u003E5. \u003Cb\u003EML e IA:\u003C/b\u003E Gracias a la potencia de computación avanzada, este tipo de software basado en algoritmos encuentra patrones e información en el big data.\u003C/p\u003E\n\u003Cp\u003E6. \u003Cb\u003EOrquestación y gestión:\u003C/b\u003E Tecnologías que crean, planifican y supervisan flujos de trabajo mediante sistemas de código abierto para automatizar la implementación, la escalabilidad y la gestión de aplicaciones.\u003C/p\u003E\n"},{"title":"¿En qué se diferencia el big data de los datos tradicionales?","richText":"\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EEl big data consiste en conjuntos de datos grandes y diversos con datos de distintos formatos, como texto, audio y vídeo. No está estructurado, por lo que no se adapta fácil ni limpiamente a una base de datos tradicional, lo que significa que necesita un procesamiento más sólido para proporcionar valor. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELos datos tradicionales se componen de datos estructurados con parámetros claros y se pueden almacenar y analizar fácilmente en bases de datos estándar tradicionales.  \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":{"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-v2-2e71c68740","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usan Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-0b4858ec6b","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-7f04e36684",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-27c263c8e8","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["WHOOP mejora las previsiones financieras y la experiencia de los miembros con IA y 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