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[...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});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":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"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"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":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bef988a3ce","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-78538dcf9f","text":"\u003Cp\u003ELas analíticas de big data son un enfoque transformador para procesar grandes conjuntos de datos complejos. Aunque a primera vista puedan sonar áridas, las analíticas abren la puerta a información valiosa: revelan tendencias, optimizan operaciones y permiten la toma de decisiones basadas en datos en todos los sectores. Poder analizar con rapidez y extraer inteligencia de grandes volúmenes de datos estructurados y no estructurados ha impulsado avances e hitos en ámbitos que van desde la sanidad hasta la fabricación. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_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":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f2a2f02722","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué son las analíticas de big data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6cb2a6679d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELas analíticas de big data son el proceso de recopilar y analizar con rapidez conjuntos de datos enormes y diversos para obtener información comercial o científica relevante. Los servicios de analíticas de big data abordan de forma específica los desafíos que presentan los datos que fluyen a velocidades extremas y en cantidades enormes, y que llegan en distintos formatos (estructurado, semiestructurado y no estructurado). Al aprovechar la potencia de cómputo escalable y nativa de la nube, las analíticas extraen información predictiva y tendencias que serían invisibles para los sistemas de procesamiento heredados. En última instancia, permiten a las organizaciones pasar de la elaboración reactiva de informes a una estrategia proactiva, basada en datos, y a un proceso de toma de decisiones más sólido. \u003C/p\u003E\n\u003Cp\u003ELas analíticas de big data se diferencian de las analíticas de datos tradicionales, sobre todo, en que se centran en la escala y la complejidad de los datos, no solo en los resúmenes. Mientras que los métodos tradicionales se basan en muestras limitadas y estructuradas para analizar hechos pasados, las analíticas de big data procesan el conjunto completo de datos de alta velocidad y múltiples formatos para construir modelos predictivos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ELas cinco V de las analíticas de big data\u003C/h3\u003E\n\u003Cp\u003ESi bien el volumen, la velocidad y la variedad han definido tradicionalmente la complejidad del big data, la definición moderna se amplía a las cinco V para abarcar por completo los desafíos esenciales y los resultados necesarios de las analíticas de big data. Estos factores adicionales (veracidad y valor) determinan si los datos son fiables y, en última instancia, rentables para la empresa.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVolumen\u003C/b\u003E: Se refiere a la magnitud de los datos que se generan cada día, del orden de petabytes o incluso exabytes. Las bases de datos tradicionales sencillamente no pueden almacenar ni consultar de forma eficiente tal cantidad de información.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVelocidad\u003C/b\u003E: Es la rapidez con la que se crean, se recopilan y se procesan los datos. En muchos casos de uso modernos, como la detección de fraude o las operaciones bursátiles, la información debe extraerse casi en tiempo real, a menudo en cuestión de milisegundos.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVariedad\u003C/b\u003E: Hoy los datos llegan en formatos muy diversos, como registros transaccionales (estructurados), datos de sensores y registros web (semiestructurados) y publicaciones en redes sociales o vídeos (no estructurados). Para ser eficaces, las analíticas deben poder armonizar y analizar conjuntamente todos estos tipos de datos.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EVeracidad\u003C/b\u003E: Los datos deben ser fiables y precisos para su uso en analíticas o modelos de IA; de lo contrario, podrían derivar en resultados perjudiciales. Trabajar con datos de baja veracidad exige procesos sólidos de depuración de datos, gobernanza y control de calidad para garantizar que la información analítica obtenida sea fiable y no induzca a error.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EValor\u003C/b\u003E: El valor es el objetivo final de las analíticas de big data: extraer información relevante, útil y rentable a partir de un conjunto de datos masivo. Si una empresa logra gestionar con éxito el volumen, la velocidad, la variedad y la veracidad, el valor es el resultado empresarial que se obtiene. El valor se traduce en ventaja competitiva, ahorro de costes, decisiones optimizadas, innovación o nuevas fuentes de ingresos. En definitiva, los datos solo son “grandes” si, al final, aportan valor a una organización.\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"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":"work","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-796ebdf257","additionalClasses":"headline-decoration","type":"heading2","lines":["Big data y datos tradicionales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-b19f5fd3b4","text":"\u003Cp\u003ENo hay un único factor que determine si los datos son big data o datos tradicionales. Algunas diferencias fundamentales incluyen el valor (mencionado antes) y si puede analizarse de forma eficaz con herramientas tradicionales o heredadas. Los datos tradicionales suelen ser estructurados, como los de las bases de datos, y se analizan mediante métodos estadísticos y herramientas de consulta clásicas como SQL. El big data se mueve a gran velocidad e incluye enormes conjuntos de datos en formatos dispares, con datos estructurados, no estructurados y semiestructurados. Las herramientas tradicionales de análisis de datos no pueden procesar ni analizar la escala o la complejidad del big data; por eso, el big data requiere sistemas distribuidos y herramientas avanzadas como el aprendizaje automático (ML). \u003C/p\u003E\n\u003Cp\u003ELas analíticas de datos tradicionales manejan un volumen asumible de información; por ejemplo, generar un informe de ventas al final del día a partir de una única base de datos financiera estructurada, procesándolo en lotes predecibles. En cambio, las soluciones de analíticas de big data son necesarias cuando se trabaja con un volumen masivo de datos de transmisión, como una aplicación global de transporte compartido que monitoriza millones de vehículos. En una situación así, por ejemplo, los datos deben ingerirse y procesarse con gran velocidad (en milisegundos) para calcular la hora estimada de llegada en tiempo real y aplicar precios dinámicos. \u003C/p\u003E\n\u003Cp\u003EEn este caso, el big data también debe gestionar una enorme variedad, al integrar coordenadas GPS estructuradas con texto e imágenes no estructurados procedentes de los comentarios de los conductores. Se necesitan técnicas avanzadas para gestionar la veracidad (la fiabilidad de los datos) y asegurar que se extrae el valor empresarial final, una complejidad para la que los sistemas tradicionales sencillamente no están diseñados.\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_copy","text_copy"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":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":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-740d1fd079","additionalClasses":"headline-decoration","type":"heading2","lines":["Cómo funcionan las analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-139a9569b1","text":"\u003Cp\u003ELas analíticas de big data se basan en un flujo de trabajo integral y sistemático, pensado para gestionar una escala y una complejidad enormes y, en última instancia, convertir la información sin procesar en información práctica. Para ilustrar este proceso, usemos el ejemplo hipotético de una gran empresa de tarjetas de crédito que quiere prevenir el fraude financiero en tiempo real.\u003C/p\u003E\r\n\u003Cp\u003EEl flujo de trabajo de este ejemplo muestra cómo la información de las analíticas de big data transforma los flujos continuos de datos transaccionales en modelos predictivos y alertas inmediatas, lo que exige tecnologías especializadas de nube en cada fase.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERecopilación de datos (ingesta)\u003C/h3\u003E\r\n\u003Cp\u003EEsta fase inicial consiste en reunir grandes volúmenes de datos de alta velocidad procedentes de fuentes diversas. Por ejemplo, la empresa de tarjetas de crédito ingiere de forma continua miles de millones de eventos en tiempo real: cada compra, retirada de dinero en cajeros e intento de inicio de sesión en línea a partir de pagos con tarjeta, API de comercio electrónico y registros de aplicaciones de todo el mundo.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ELimpieza y preprocesamiento de datos\u003C/h3\u003E\r\n\u003Cp\u003ELos datos sin procesar suelen estar desordenados, por lo que requieren depuración inmediata, estandarización y enriquecimiento para garantizar su precisión. Esta es la fase de transformación. El \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-discovery/\"\u003Edescubrimiento de datos\u003C/a\u003E forma parte inherente de estas fases, ya que consiste en explorar y comprender los datos antes o durante el análisis. Los duplicados, los errores o los datos de baja calidad pueden generar información engañosa. En el ejemplo de la empresa de tarjetas de crédito, se comprueba al instante que los flujos de datos no tengan campos vacíos, se estandarizan (por ejemplo, para garantizar que todas las zonas horarias sean uniformes) y se enriquecen con datos externos, como listas negras de fraude conocidas o el historial de ubicación del cliente.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAlmacenamiento y gestión de datos\u003C/h3\u003E\r\n\u003Cp\u003ELos enormes conjuntos de datos ya transformados deben almacenarse en arquitecturas escalables y flexibles que separen el cómputo del almacenamiento. Los datos limpios de la empresa de tarjetas de crédito se almacenan en una plataforma de datos en la nube, que gestiona petabytes de registros y permite que distintos equipos de análisis accedan a la única fuente de verdad sin que el rendimiento se vea afectado.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnálisis de datos \u003C/h3\u003E\r\n\u003Cp\u003EAquí es donde se aplican técnicas avanzadas como el aprendizaje automático y el modelado estadístico para descubrir patrones y predecir resultados. Por ejemplo, los modelos de aprendizaje automático pueden analizar el historial de compras, la ubicación y los hábitos de gasto de un usuario en relación con una transacción en tiempo real. Si el modelo detecta una anomalía estadísticamente significativa (por ejemplo, una tarjeta utilizada en dos continentes en menos de una hora), marca esa transacción como anómala.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVisualización de datos e informes\u003C/h3\u003E\r\n\u003Cp\u003ELa fase final consiste en presentar los resultados complejos mediante paneles o acciones automatizadas para el usuario empresarial. En la empresa de tarjetas de crédito hipotética, se activan al instante sistemas automatizados para bloquear la transacción sospechosa y enviar al cliente un SMS de alerta de fraude. Mientras tanto, el analista de datos consulta paneles que muestran tendencias agregadas de intentos de fraude por regiones y tipos de tarjeta para la planificación estratégica.\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"]},"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":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3488756848","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Por qué son importantes las analíticas de big data?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-66d3431387","text":"\u003Cp\u003ELas analíticas de big data son un componente importante de la inteligencia empresarial (BI), porque van más allá de la elaboración de informes retrospectivos y dan paso a información y análisis predictivos. Disponer de este mecanismo para convertir grandes repositorios de datos, incluso datos no estructurados, en información práctica aporta a las empresas una ventaja competitiva notable, al impulsar desde los ingresos y la eficiencia hasta la experiencia del cliente. \u003C/p\u003E\r\n\u003Cp\u003EEste proceso mejora de inmediato la toma de decisiones al sustituir las suposiciones por inteligencia que responde a qué es probable que ocurra a continuación y cuál es la mejor forma de actuar, creando así una ventaja competitiva.\u003C/p\u003E\r\n\u003Cp\u003EMás allá de la estrategia, el big data tiene un impacto directo en los resultados finales y en las relaciones con los clientes. Impulsa la eficiencia operativa al analizar datos de transmisión de cada máquina y sistema, lo que permite el mantenimiento predictivo y la eliminación del gasto innecesario en flujos de trabajo globales. Al mismo tiempo, favorece un conocimiento más profundo del cliente al combinar datos diversos, desde transacciones hasta el sentimiento en redes sociales, para construir una visión 360 detallada que impulsa ofertas hiperpersonalizadas y refuerza la fidelidad del cliente. El análisis de datos a altas velocidades también actúa como principal defensa para mitigar riesgos, al detectar y neutralizar al instante \u003Ca href=\"https://www.snowflake.com/en/solutions/departments/cybersecurity/\"\u003Eamenazas\u003C/a\u003E como el fraude financiero y las intrusiones cibernéticas en tiempo real.\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"]},"container_copy_copy_259718203":{"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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f7c1021e8a","additionalClasses":"headline-decoration","type":"heading2","lines":["Métodos de analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cbb412dee0","text":"\u003Cp\u003ELas analíticas de big data se fundamentan en varios enfoques analíticos principales:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalíticas descriptivas\u003C/h3\u003E\r\n\u003Cp\u003ELas analíticas descriptivas son el nivel más básico: utilizan los datos para responder a la pregunta de qué ocurrió en el pasado. Agregan, contabilizan y resumen datos para aportar contexto sobre hechos y resultados anteriores, como las cifras de ventas de un trimestre pasado. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalíticas diagnósticas\u003C/h3\u003E\r\n\u003Cp\u003ETécnicas como la minería de datos y el análisis de causalidad buscan explicar por qué ocurrió algo e identificar la causa raíz de un resultado concreto, como una campaña específica que generó leads o redujo la tasa de abandono.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalíticas predictivas\u003C/h3\u003E\r\n\u003Cp\u003EA partir de modelos estadísticos y aprendizaje automático, este tipo de análisis aprovecha patrones históricos para predecir probabilidades futuras y responder a la pregunta: “¿qué es probable que ocurra a continuación?”. Puede aplicarse a muchos sectores y casos de uso, como prever la demanda de ventas, anticipar el mantenimiento de flotas de vehículos o detectar cuándo es probable que el alumnado se retrase y necesite apoyo preventivo. Las analíticas predictivas son una herramienta potente en \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003Emarketing\u003C/a\u003E, donde la información basada en datos puede orientar las campañas y ayudar a captar, retener y fidelizar a los clientes. Aquí es donde la potencia de las analíticas de big data habilita las capacidades de los \u003Ca href=\"https://www.snowflake.com/es/product/snowflake-intelligence/\"\u003Emodelos de IA y ML\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalíticas prescriptivas\u003C/h3\u003E\r\n\u003Cp\u003EEs una de las modalidades más avanzadas de las analíticas de big data: van más allá de predecir lo que podría ocurrir para indicar qué deberíamos hacer al respecto. A menudo recurren a la optimización y la simulación para guiar las decisiones en tiempo real, como ajustar dinámicamente los precios para maximizar el beneficio en función del inventario y la demanda actuales.\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"]},"container_copy_289473020":{"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":"equation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-9258179c8b","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas de las analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-ac300acc0a","text":"\u003Cp\u003ELas analíticas de big data aportan valor medible en toda la empresa, ya que ofrecen una serie de ventajas críticas, basadas en datos, que tienen un impacto directo en la rentabilidad, la estrategia y el riesgo. Entre estas ventajas se incluyen:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EEficiencia operativa y reducción de costes\u003C/h3\u003E\r\n\u003Cp\u003EAnalizar datos de transmisión procedentes de la cadena de suministro y de sensores ayuda a las empresas a identificar y eliminar ineficiencias, asignar recursos con precisión y aplicar un mantenimiento predictivo para evitar costosos tiempos de inactividad de los equipos. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMejor información sobre los clientes\u003C/h3\u003E\r\n\u003Cp\u003EAl integrar y analizar transacciones junto con datos no estructurados, como el sentimiento en redes sociales, las organizaciones obtienen una visión 360 del consumidor detallada, muy por encima de una lectura demográfica básica.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EToma de decisiones con mayor rapidez\u003C/h3\u003E\r\n\u003Cp\u003EEl big data permite a los responsables tomar decisiones con rapidez y dejar atrás la intuición al aportar inteligencia basada en datos de alta fidelidad, que no solo anticipa resultados futuros, sino que también sugiere el mejor curso de acción.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMejora del desarrollo de productos\u003C/h3\u003E\r\n\u003Cp\u003ELas analíticas descubren patrones ocultos en el comportamiento y las preferencias de los clientes, lo que ayuda a los equipos de producto a optimizar la oferta existente o a desarrollar con rapidez nuevas funciones innovadoras que respondan directamente a la demanda del mercado.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVentaja competitiva\u003C/h3\u003E\r\n\u003Cp\u003EAl aprovechar la información detallada y oportuna para orientar la estrategia y la innovación, las analíticas de big data permiten a las empresas mejorar de forma significativa su posición en el mercado. Esto se traduce directamente en un mayor crecimiento de los ingresos, una mayor satisfacción del cliente y mucho más. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGestión de riesgos y detección de fraudes\u003C/h3\u003E\r\n\u003Cp\u003ELa capacidad de procesar datos a una velocidad extrema es esencial para la defensa moderna: permite a las entidades financieras y a los equipos de seguridad detectar y neutralizar al instante transacciones anómalas o intrusiones cibernéticas en tiempo real.\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"]},"container_copy_copy__1985496925":{"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":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a59b413c92","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafíos de las analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-594d10406e","text":"\u003Cp\u003EA pesar del potencial transformador de las analíticas de big data, las organizaciones se enfrentan a retos importantes para aprovechar esta información de forma eficaz, principalmente por la escala y la complejidad inherentes a las cinco V. Estas dificultades abarcan ámbitos técnicos, de seguridad y de talento, y exigen soluciones modernas e integradas para superarlas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVolumen de datos\u003C/h3\u003E\r\n\u003Cp\u003EAlmacenar y procesar petabytes de información es costoso, y la infraestructura tradicional a menudo no es capaz de gestionar la magnitud de datos que se generan a diario.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVariedad de datos\u003C/h3\u003E\r\n\u003Cp\u003ELa necesidad de integrar y armonizar muchos formatos de datos diferentes, desde bases de datos estructuradas hasta vídeo y texto no estructurados, da lugar a complejos \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Eflujos\u003C/a\u003E de datos que requieren mucho trabajo.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVelocidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003EProcesar flujos continuos de datos entrantes a alta velocidad en tiempo real requiere tecnologías de transmisión especializadas, que deben construirse y mantenerse de forma eficiente.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECalidad y veracidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003EDado que los datos proceden de numerosas fuentes con distintos niveles de fiabilidad, garantizar la precisión, la coherencia y la fiabilidad de la información sigue siendo un obstáculo fundamental.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESeguridad y privacidad\u003C/h3\u003E\r\n\u003Cp\u003ELa protección de grandes conjuntos de datos distribuidos que contienen información confidencial de clientes introduce complejos riesgos de cumplimiento y retos de gobernanza, que pueden estar sujetos a distintas normativas internacionales.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EEscalabilidad y rendimiento \u003C/h3\u003E\r\n\u003Cp\u003ELas organizaciones se enfrentan al reto de garantizar que su plataforma de datos pueda adaptarse con rapidez para responder a una demanda variable sin comprometer la velocidad de las consultas ni incurrir en costes operativos excesivos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ETalento y experiencia\u003C/h3\u003E\r\n\u003Cp\u003ELos métodos de análisis de big data requieren científicos de datos e ingenieros cualificados y con experiencia específica para gestionar, optimizar y extraer valor de tecnologías de big data complejas.\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"]},"container_copy":{"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":"types","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-bb7a5fcef2","additionalClasses":"headline-decoration","type":"heading2","lines":["Casos de uso de las analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-eca260d20c","text":"\u003Cp\u003ELas analíticas de big data, cuando se basan en modernas plataformas de datos en la nube, permiten identificar casos de uso diferenciados y de alto valor, adaptados a los desafíos específicos de los datos de los principales sectores. Estos casos de uso incluyen:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESanidad\u003C/h3\u003E\r\n\u003Cp\u003EAl unificar notas clínicas no estructuradas, datos de ensayos clínicos e información de reclamaciones, los proveedores pueden crear una visión 360 del paciente para personalizar las vías de atención y mejorar los resultados de salud en general.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EFinanzas\u003C/h3\u003E\r\n\u003Cp\u003ELas entidades financieras aprovechan el procesamiento de alta velocidad de las analíticas de big data para analizar miles de millones de transacciones en tiempo real con el fin de detectar fraudes al instante, realizar analíticas predictivas y fomentar estrategias sofisticadas de trading algorítmico.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERetail\u003C/h3\u003E\r\n\u003Cp\u003ELas \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-for-retailers/\"\u003Eempresas de retail\u003C/a\u003E combinan datos de secuencias de clics en línea, registros de programas de fidelización y niveles de inventario para predecir la demanda con precisión y ofrecer recomendaciones de productos hiperpersonalizadas que impulsan las ventas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EFabricación\u003C/h3\u003E\r\n\u003Cp\u003ELa capacidad de integrar enormes volúmenes de datos de sensores de tecnología de la información (TI) y tecnología operativa (OT) procedentes de la planta de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-manufacturing/\"\u003Efabricación\u003C/a\u003E permite el mantenimiento predictivo y ayuda a optimizar los procesos de producción para lograr una mayor eficiencia operativa.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ETransporte y logística\u003C/h3\u003E\r\n\u003Cp\u003ELas organizaciones analizan datos GPS y meteorológicos en tiempo real junto con tendencias históricas para optimizar rutas de entrega complejas, gestionar de forma proactiva las operaciones de la flota y reforzar la resiliencia de la cadena de suministro.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EEducación\u003C/h3\u003E\r\n\u003Cp\u003ELas universidades centralizan expedientes del alumnado, registros del profesorado y administrativos para crear una visión 360, lo que les permite identificar a estudiantes en riesgo y personalizar las intervenciones de aprendizaje para mejorar la retención. El análisis también ayuda a reducir las ineficiencias asociadas al uso de los edificios y respalda la relación con antiguos alumnos y la captación de fondos. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EEntretenimiento\u003C/h3\u003E\r\n\u003Cp\u003ELas empresas de medios de comunicación analizan grandes volúmenes de hábitos de visualización y datos de sentimiento en redes sociales para guiar la estrategia de creación de contenido y optimizar las recomendaciones de contenido en tiempo real, maximizando así la interacción y la retención de suscriptores.\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"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy_copy":{"id":"title-v2-2e327de817","additionalClasses":"headline-decoration","type":"heading2","lines":["Herramientas de analíticas de big data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-859c8574a5","text":"\u003Cp\u003ELa magnitud y complejidad de los datos modernos hacen que las herramientas relacionales de un solo servidor ya no sean suficientes. Las analíticas de big data requieren un ecosistema sólido de tecnologías especializadas. Estas herramientas suelen clasificarse por función: almacenamiento, procesamiento y análisis. Aquí tienes algunos ejemplos de herramientas populares de analíticas de big data:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHadoop\u003C/h3\u003E\n\u003Cp\u003EApache Hadoop es un marco fundamental de código abierto, diseñado para gestionar y procesar enormes conjuntos de datos mediante la distribución del workload en una red de servidores estándar. Ofrece capacidades esenciales de almacenamiento tolerante a fallos y a escala de petabytes y de procesamiento por lotes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESpark\u003C/h3\u003E\n\u003Cp\u003EApache Spark es un motor de procesamiento avanzado de varios lenguajes, diseñado para ofrecer velocidad. Acelera el workload analítico, especialmente el aprendizaje automático, al mantener los datos en memoria en todo el clúster, lo que se traduce en un rendimiento superior frente a los sistemas basados en disco.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHerramientas de analíticas de flujos\u003C/h3\u003E\n\u003Cp\u003EEste software especializado se utiliza para ingerir y analizar los datos en el mismo instante en que se generan: un enfoque de “datos en movimiento”. Esto es crucial para cualquier caso de uso que requiera información inmediata, como la monitorización de sensores del Internet de las cosas (IdC) o la detección de fraude financiero en milisegundos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAlmacenamiento distribuido\u003C/h3\u003E\n\u003Cp\u003EEstas arquitecturas están diseñadas para dividir archivos enormes en fragmentos y distribuirlos de forma segura entre numerosas máquinas físicas o virtuales. Este diseño garantiza una escalabilidad prácticamente ilimitada y una alta durabilidad de los datos, y elimina los puntos de error únicos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHerramientas de minería de datos\u003C/h3\u003E\n\u003Cp\u003EEste software se sitúa por encima de la capa de almacenamiento y de procesamiento, y emplea algoritmos estadísticos avanzados y de aprendizaje automático para analizar de forma autónoma enormes conjuntos de datos. Estas herramientas pueden sacar a la luz correlaciones ocultas, identificar valores atípicos y descubrir patrones para el modelado predictivo.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBases de datos NoSQL\u003C/h3\u003E\n\u003Cp\u003ESe trata de una familia diversa de tecnologías de bases de datos no relacionales, pensadas para manejar modelos de datos flexibles, incluidos datos semiestructurados y no estructurados. Aportan la agilidad y la escalabilidad horizontal masiva que las bases de datos rígidas con esquema fijo no pueden ofrecer.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAlmacenes de datos\u003C/h3\u003E\n\u003Cp\u003ELos \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Ealmacenes de datos\u003C/a\u003E son plataformas modernas, nativas de la nube, optimizadas para consultas analíticas de alto rendimiento sobre grandes volúmenes de datos limpios y estructurados. 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