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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});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":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":{"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",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f7f02fdfed","additionalClasses":"headline-decoration","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-82f8b34c07","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa minería de datos es una técnica que utiliza algoritmos y principios de análisis estadístico —a menudo combinados con aprendizaje automático y analíticas de datos— para analizar grandes conjuntos de datos y descubrir patrones, anomalías y otra información. La amplia disponibilidad de herramientas de recopilación y almacenamiento de datos permite que incluso las organizaciones más pequeñas recopilen y analicen grandes volúmenes de información, ya sea sobre preferencias de clientes, actividad de usuarios, gestión de inventario u otras funciones empresariales.\u003C/p\u003E\r\n\u003Cp\u003ELas organizaciones utilizan la minería de datos para realizar predicciones avanzadas, identificar cuellos de botella en los sistemas y detectar posibles problemas antes de que tengan impacto. Las nuevas \u003Ca href=\"https://www.snowflake.com/es/product/ai/\"\u003Ecapacidades de IA\u003C/a\u003E pueden democratizar el acceso a la información de la minería de datos, ya que permiten a las partes interesadas consultar patrones de datos y poner a prueba hipótesis sin la intervención directa de un analista o ingeniero de datos. \u003C/p\u003E\r\n\u003Cp\u003EEn este artículo analizamos los fundamentos de la minería de datos y explicamos cómo puede utilizarse esta tecnología para obtener ventajas empresariales clave.\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_":{"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-f45e0c6007","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es la minería de datos?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-87279d3355","text":"\u003Cp\u003ELa recopilación de datos ocurre constantemente a nuestro alrededor, desde el seguimiento de los productos que compramos hasta nuestro ritmo cardíaco a lo largo del día. Las empresas recopilan aún más datos sobre sus operaciones y, mediante técnicas de minería de datos, pueden extraer valor de toda esa información. La minería de datos identifica asociaciones entre puntos de datos o datos históricos para generar información o anticipar escenarios futuros. \u003C/p\u003E\n\u003Cp\u003EPor ejemplo, una cadena de supermercados puede observar que los datos de ventas muestran un aumento de los helados en verano y una mayor demanda de medicamentos para el resfriado en invierno. Puede que este tipo de datos no resulte sorprendente, pero las técnicas de minería de datos ayudan a las organizaciones a descubrir patrones inesperados ocultos en la información. Por ejemplo, un análisis puede revelar que un aumento en la demanda de determinados alimentos o suplementos vitamínicos se correlaciona con un incremento en las ventas de pañales nueve meses después, lo que sugiere que estos productos son populares entre futuras madres. \u003C/p\u003E\n\u003Cp\u003EEl enorme volumen de datos con el que trabajan las organizaciones puede hacer que este tipo de información sea imposible de detectar sin la ayuda de \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003Eherramientas de aprendizaje automático\u003C/a\u003E y análisis estadístico. Las herramientas de minería de datos pueden agrupar puntos de datos relacionados y categorizar la información de formas inesperadas, lo que permite reaccionar con rapidez ante cambios imprevistos y anticipar necesidades futuras.\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-v2-44ccef7442","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Por qué es útil la minería de datos? 4 ventajas"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f0865f4796","text":"\u003Cp\u003ELa minería y las analíticas de datos proporcionan a las organizaciones una visión clara del rendimiento operativo, las preferencias de los clientes y los patrones históricos, lo que permite tomar decisiones mejor fundamentadas. Estas son cuatro de las principales ventajas de la minería de datos:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMejora la toma de decisiones\u003C/h3\u003E\n\u003Cp\u003EEn lugar de basar las decisiones en suposiciones o prácticas habituales del sector, las organizaciones se pueden apoyar en los datos gracias a la minería de datos, que les ayuda a comprender las ventajas y desventajas de cada opción y reduce la incertidumbre en la toma de decisiones.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDetecta fraudes y anomalías\u003C/h3\u003E\n\u003Cp\u003EAl analizar datos históricos y en tiempo real, las herramientas de minería de datos pueden identificar patrones u otras variables que indiquen comportamientos malintencionados o de riesgo. Por ejemplo, el análisis de patrones de uso de cajeros automáticos puede ayudar a los bancos a detectar actividades asociadas al skimming de tarjetas u otros fraudes. Esto permite bloquear transacciones sospechosas y marcarlas para su investigación.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EOptimiza los procesos empresariales\u003C/h3\u003E\n\u003Cp\u003EEl análisis de datos de uso de servicios, del comportamiento en los flujos de compra y de los tiempos de respuesta de los tickets de asistencia puede revelar cuellos de botella operativos y sistemas sobrecargados en toda la organización. Esto ayuda a mejorar la asignación de recursos, reducir el tiempo medio de reparación (MTTR) y disminuir la latencia de los sistemas.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EImpulsa el modelado predictivo\u003C/h3\u003E\n\u003Cp\u003EUna de las aplicaciones más potentes de la minería de datos es la previsión, que extrapola patrones de datos históricos para anticipar comportamientos futuros. Esto resulta especialmente útil en logística y planificación, ya que ayuda a gestionar inventarios para garantizar la disponibilidad de productos, así como para la gestión de recursos al prever la demanda de cómputo necesaria para una operación o lanzamiento concreto.\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-a672e0d9fe","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafíos de la minería de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ab0fdaa53f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EA pesar de su enorme potencial, la minería de datos también presenta desafíos que pueden limitar su eficacia. Estos son algunos de los principales:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGran volumen de datos y costes\u003C/h3\u003E\r\n\u003Cp\u003ELa minería de datos requiere grandes volúmenes de datos, lo que puede suponer una carga importante de almacenamiento y procesamiento a escala. Todas las fases del proceso, desde la \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-ingestion/\"\u003Eingesta\u003C/a\u003E hasta el \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-warehouse/\"\u003Ealmacenamiento\u003C/a\u003E y el \u003Ca href=\"https://www.snowflake.com/en/fundamentals/automated-data-processing/\"\u003Eprocesamiento\u003C/a\u003E, requieren recursos de computación y una inversión significativa que no todas las organizaciones pueden asumir. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EIncertidumbre en los resultados\u003C/h3\u003E\r\n\u003Cp\u003EAunque un proceso de minería de datos identifique un patrón o genere una predicción, no existe garantía de que sean correctos o aporten valor empresarial. Además, cambios inesperados en el mercado o en las preferencias de los consumidores pueden reducir la utilidad de la información obtenida. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EComplejidad de los algoritmos\u003C/h3\u003E\r\n\u003Cp\u003ELas técnicas de minería de datos suelen ser complejas y requieren pruebas iterativas, evaluación continua y mejoras constantes para adaptarse a los cambios. Esto puede resultar costoso y consumir muchos recursos en detrimento de otras operaciones empresariales clave. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EProblemas de calidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003ELa minería de datos depende de la disponibilidad de datos precisos y utilizables. Las ineficiencias en los flujos de datos, los sesgos en los conjuntos de datos, la inclusión involuntaria de datos confidenciales y otros problemas pueden generar riesgos o reducir la calidad de las analíticas.\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-v2-8d97f336e3","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Cómo funciona la minería de datos?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-158920b63d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa minería de datos no se basa en un único algoritmo o herramienta, sino en un proceso estratégico compuesto por varias etapas. Así es como funciona:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Definir los objetivos empresariales\u003C/h3\u003E\n\u003Cp\u003EAntes de recopilar y \u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Eprocesar\u003C/a\u003E datos, es fundamental establecer objetivos claros. Dado que la recopilación y el almacenamiento de datos consumen muchos recursos de cómputo, conviene seleccionar las fuentes más adecuadas y evaluar si existen suficientes datos para extraer información relevante. Definir objetivos realistas también ayuda a los analistas a elegir el modelo de minería de datos más adecuado.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Recopilar y consolidar los datos\u003C/h3\u003E\n\u003Cp\u003EOptimizar la recopilación implica establecer parámetros eficientes para aplicarlos a las fuentes de datos identificadas. Recopilar demasiados datos puede sobrecargar los recursos de almacenamiento y procesamiento, mientras que disponer de pocos datos limita la utilidad del conjunto de datos. También es importante identificar posibles riesgos en las fuentes de datos antes de anonimizar y proteger los datos confidenciales.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Limpiar y preparar los datos\u003C/h3\u003E\n\u003Cp\u003ELa limpieza de datos es una fase crítica que elimina valores atípicos y ruido, y gestiona valores ausentes. La estandarización de formatos es especialmente importante cuando los datos proceden de múltiples fuentes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Entrenar el modelo\u003C/h3\u003E\n\u003Cp\u003EAntes de utilizar los modelos para identificar patrones relevantes, es necesario entrenarlos y ajustarlos. Esto implica modificar los pesos de las variables, por ejemplo, dando más relevancia a datos recientes que a datos antiguos, o ajustando el tamaño del conjunto de datos y el número de dimensiones analizadas.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Minería de patrones\u003C/h3\u003E\n\u003Cp\u003ELa aplicación de un modelo entrenado sobre un gran conjunto de datos sin procesar permite identificar patrones, relaciones o tendencias estadísticamente significativas. Los detalles dependen de los objetivos: un modelo predictivo puede analizar tendencias históricas para anticipar el comportamiento de los usuarios, mientras que un modelo de análisis de texto puede evaluar el sentimiento de los consumidores a partir de reseñas.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E6. Evaluar el rendimiento del modelo\u003C/h3\u003E\n\u003Cp\u003EIncluso cuando un modelo cumple su objetivo, suele beneficiarse de un ajuste adicional, especialmente si se incorporan nuevas fuentes de datos o se desarrollan métodos de análisis más eficientes.\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":{"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-b3f4f55b4e","additionalClasses":"headline-decoration","type":"heading2","lines":["Técnicas de minería de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3cd4e14a65","text":"\u003Cp\u003EExisten múltiples técnicas de minería de datos, cada una adecuada para objetivos y tipos de datos concretos. Algunas de las más utilizadas son:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnálisis de regresión\u003C/h3\u003E\r\n\u003Cp\u003EExamina la relación entre una variable dependiente (un punto de datos concreto) y una o varias variables independientes. Un ejemplo habitual es el análisis de la elasticidad del precio, que mide cómo las variaciones en el precio de un producto afectan a su demanda. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalíticas predictivas \u003C/h3\u003E\r\n\u003Cp\u003EA partir de datos históricos, los algoritmos predictivos crean modelos matemáticos que anticipan comportamientos futuros. Las empresas del \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/\"\u003Esector de la fabricación\u003C/a\u003E utilizan estos modelos para evaluar el uso de la maquinaria e identificar componentes con riesgo de fallo, lo que permite realizar reparaciones o sustituciones de forma proactiva. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EClasificación\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-classification/\"\u003Eclasificación de datos\u003C/a\u003E agrupa información que comparte características predefinidas, como clasificar determinados comportamientos de usuario (por ejemplo, correos electrónicos) como sospechosos o no. Refinar estas clasificaciones permite detectar spam o actividad malintencionada en la red. La clasificación suele ser una forma de aprendizaje automático supervisado, ya que el algoritmo se entrena con datos previamente etiquetados según las características predefinidas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EClusterización\u003C/h3\u003E\r\n\u003Cp\u003ELos algoritmos de clusterización crean grupos de datos en función de características compartidas, sin clasificaciones predefinidas. Se utilizan para descubrir nuevos segmentos o patrones de comportamiento, por ejemplo, para identificar clientes con preferencias de productos similares. La clusterización normalmente se trata de aprendizaje automático no supervisado, ya que analiza datos sin etiquetar. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EÁrboles de decisión\u003C/h3\u003E\r\n\u003Cp\u003EUn árbol de decisión es una estructura visual que divide un conjunto de datos en función de diferentes decisiones, que a su vez se subdividen en decisiones sucesivas hasta llegar a un resultado o probabilidad. Algunos algoritmos de diagnóstico médico utilizan este método para clasificar pacientes según variables como edad, presión arterial o síntomas, y estimar la probabilidad de una determinada enfermedad. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDetección de anomalías\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/anomaly-detection/\"\u003Edetección de anomalías\u003C/a\u003E identifica y supervisa actividades que se desvían del comportamiento esperado, como una consulta a una base de datos que de repente consume mucha más CPU. Esto permite detectar y corregir cuellos de botella o ineficiencias antes de que afecten al rendimiento.\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-b83d1cec2f","additionalClasses":"headline-decoration","type":"heading2","lines":["Ejemplos y casos de uso de minería de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0a1a1994e4","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEquipos de todos los sectores confían en la información basada en datos para mejorar la toma de decisiones y la productividad. Algunos ejemplos de cómo las organizaciones usan la minería de datos en sus operaciones son los siguientes:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESegmentación y focalización de clientes\u003C/h3\u003E\n\u003Cp\u003EMediante técnicas de clusterización, los \u003Ca href=\"https://www.snowflake.com/en/solutions/departments/marketing/\"\u003Eequipos de marketing\u003C/a\u003E pueden segmentar su mercado direccionable de forma más eficaz, agrupando consumidores según preferencias comunes. Esto permite adaptar las campañas a las necesidades y expectativas de cada segmento, mejorar el retorno y detectar nuevas oportunidades.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDetección de fraude en banca\u003C/h3\u003E\n\u003Cp\u003ELos equipos de seguridad pueden clasificar distintos tipos de actividad de usuario, establecer una referencia de comportamiento normal y señalar \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Eposibles actividades fraudulentas\u003C/a\u003E que se salen de la norma, como cargos elevados o realizados en el extranjero. También pueden analizar datos históricos de incidentes de seguridad para detectar anomalías o identificar patrones que anticipen actividades malintencionadas. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEficiencia operativa en logística\u003C/h3\u003E\n\u003Cp\u003ELos modelos de previsión ayudan a los equipos logísticos a mejorar la eficiencia de la cadena de suministro anticipando cambios en la demanda y así garantizar la disponibilidad de productos. Asimismo, pueden analizar complejos \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/improve-supply-chain-performance/\"\u003Econjuntos de datos de la cadena de suministro\u003C/a\u003E para descubrir patrones ocultos, como el impacto del clima en el precio de determinadas materias primas. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAnálisis de riesgos de pacientes en sanidad\u003C/h3\u003E\n\u003Cp\u003ELos analistas sanitarios utilizan la clusterización de datos para identificar nuevos factores de riesgo, incluidos aquellos que quedan fuera de los diagnósticos tradicionales. Al relacionar variables como ubicación o profesión, u otros factores, con determinados problemas médicos, la minería de datos puede mejorar los resultados clínicos y ayudar a los profesionales \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/\"\u003Esanitarios\u003C/a\u003E a ofrecer una atención más especializada. \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__1413360324":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"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-v2-4bc3d4af66","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3843b9cd86","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa minería de datos se ha convertido en un componente esencial para muchas organizaciones, ya que permite identificar nuevas oportunidades, desarrollar mejores productos y aumentar la eficiencia operativa. La diversidad de modelos de minería de datos disponibles facilita la extracción de información valiosa a partir de distintos tipos de datos y la identificación de patrones entre variables aparentemente no relacionadas. Aunque puede requerir una inversión significativa y un alto consumo de recursos, la mayoría de las organizaciones considera que los beneficios analíticos compensan ampliamente estos costes.\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-v2-1b92e7dd55","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre minería de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-be84b46c27","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"¿Cuáles son las funciones de la minería de datos?","richText":"\u003Cp\u003ELa minería de datos cumple múltiples funciones, como prever cambios futuros a partir de un conjunto de datos, supervisar el rendimiento de los sistemas mediante KPI, descubrir relaciones entre variables y optimizar la toma de decisiones al anticipar resultados. Las funciones que adopte cada organización dependerán de sus objetivos y de los tipos de datos disponibles.\u003C/p\u003E\n"},{"title":"¿Qué herramientas de software están disponibles para la minería de datos?","richText":"\u003Cp\u003ELa minería de datos comienza con la recopilación y el preprocesamiento de datos. Muchas organizaciones utilizan herramientas de código abierto, como Apache Spark, para recopilar y procesar grandes volúmenes de datos. Las plataformas de analíticas como \u003Ca href=\"https://www.snowflake.com/es/product/analytics/\"\u003ESnowflake\u003C/a\u003E ofrecen observabilidad, gestión y visualización de datos, lo que ayuda a reducir costes de almacenamiento y procesamiento, además de integrar capacidades basadas en ML e IA. \u003C/p\u003E\n"},{"title":"¿Cómo se aplica la minería de datos en las analíticas empresariales?","richText":"\u003Cp\u003ELas empresas utilizan la minería de datos para evaluar el rendimiento de sus sistemas internos e identificar oportunidades de optimización. También la aplican para mejorar su estrategia de comercialización, analizando el comportamiento de los clientes y el rendimiento de las acciones de marketing, con el fin de identificar los mensajes más eficaces y probar nuevos enfoques de marketing y ventas.\u003C/p\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 aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-18df1cc16e","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-53861f3491","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-b73defe5ce",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-669a001f0e","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGracias a Snowflake, IGS Energy emplea los datos en distintos casos de uso de IA y aprendizaje automático (ML), desde modelos de previsión más rentables hasta una detección de anomalías más precisa, para hacer realidad su misión de lograr un futuro más sostenible para todos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title":{"id":"title","type":"heading4","lines":["IGS Energy usa la IA y el ML para simplificar las previsiones y mejorar la detección de anomalías"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/es/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer la historia"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1cdc4e48-e232-427a-ae0c-2faf287ad557/igs-energy-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",":type":"snowflake-site/components/card-v2","layoutStyle":"vertical"}},":itemsOrder":["card_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-0122012b18",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-0f3b5ad909","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGracias a Snowflake y Apache Iceberg, los equipos de WHOOP han centralizado el acceso a los datos, lo que a su vez ha reducido la complejidad y los costes, y ha mejorado los procesos esenciales.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title":{"id":"title","type":"heading4","lines":["WHOOP mejora las previsiones financieras y optimiza la 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