{"id":3308,"date":"2022-03-22T08:30:00","date_gmt":"2022-03-22T14:30:00","guid":{"rendered":"https:\/\/idsinc.com\/?p=3308"},"modified":"2026-03-09T15:13:48","modified_gmt":"2026-03-09T21:13:48","slug":"principio-fc7","status":"publish","type":"post","link":"https:\/\/idsinc.com\/es\/fc-principle7\/","title":{"rendered":"Fact Crashing\u2122: Principio 7"},"content":{"rendered":"\n
\n <\/div>\n\n\n\n

La comprensi\u00f3n de los datos es necesaria para un an\u00e1lisis adecuado<\/h1>\n\n\n\n

Esta es la novena entrega de una serie de blogs sobre Fact Crashing\u2122<\/a>, la aceleraci\u00f3n de la consideraci\u00f3n de datos de ACCI\u00d3N (ambiental, contextual, transaccional, IoT, operacional, de navegaci\u00f3n) en beneficio de la resoluci\u00f3n de disputas.<\/p>\n\n\n\n

Hay 9 Principios de Choque de hechos<\/a>\u2122. Blogs anteriores cubrieron: <\/p>\n\n\n\n

Principio 1: Los datos son evidencia y son detectables<\/strong><\/a><\/p>\n\n\n\n

Principio 2: los datos deben abordarse temprano<\/strong><\/a><\/p>\n\n\n\n

Principio 2: inmersi\u00f3n profunda<\/strong><\/a><\/p>\n\n\n\n

Principio 3: Enmarcar los problemas de casos como consultas centradas en datos<\/strong><\/a><\/p>\n\n\n\n

Principio 4: Identificar, calificar y priorizar las fuentes de datos<\/strong><\/a><\/p>\n\n\n\n

Principio 5: Los Datos Est\u00e1n Sujetos a Obligaciones de Conservaci\u00f3n y Protecci\u00f3n<\/strong><\/a><\/p>\n\n\n\n

Principio 6: la confiabilidad de los datos comienza con la recopilaci\u00f3n adecuada<\/strong><\/a><\/p>\n\n\n\n

Ahora, echemos un vistazo al s\u00e9ptimo principio.<\/p>\n\n\n\n

\n\n\n\n

Para comprender realmente los datos, debe considerarlos como un taburete de tres patas: administrador del sistema (por ejemplo, TI); Propietario de la empresa (p. ej., Ventas) y Usuario final (p. ej., Vendedor).  Tener estas tres perspectivas ayudar\u00e1 a descubrir algunos matices en la respuesta a preguntas como:<\/em><\/strong><\/p>\n\n\n\n

  • \u00bfLos usuarios utilizan el sistema seg\u00fan lo previsto?<\/em><\/li>
  • \u00bfEst\u00e1 consciente TI de c\u00f3mo la empresa est\u00e1 usando el sistema (realidad versus dise\u00f1o)?<\/em><\/li>
  • \u00bfQu\u00e9 modificaciones se han hecho por necesidad comercial?<\/em><\/li><\/ul>\n\n\n\n

    Todas estas son situaciones simples y, sin embargo, demasiado comunes que hemos visto suceder sin culpa de nadie, solo una realidad de negocios. Por ejemplo, los usuarios deciden utilizar un campo o un flujo de trabajo de una manera diferente a la prevista porque "simplemente era m\u00e1s f\u00e1cil". Es importante que este proceso se centre en comprender las realidades de los datos, no en juzgar el negocio en cuanto a la "por qu\u00e9."<\/em> <\/p>\n\n\n\n

    Piernas perdidas<\/em><\/strong><\/h3>\n\n\n\n

    No es raro estar en una situaci\u00f3n en la que te faltan piernas. (por ejemplo, la persona de recursos humanos que conoce este sistema heredado ya no est\u00e1 en la empresa).<\/em>  Eso no significa que no puedas continuar; solo significa que debe hacerlo entendiendo que se est\u00e1 balanceando sobre dos piernas. <\/p>\n\n\n\n

    Como nota al margen r\u00e1pida, un cliente me dijo recientemente, \u201cNo tengo piernas. Diablos, no tengo un taburete.<\/em>  Si bien no es com\u00fan, esto sucede de vez en cuando. Aqu\u00ed, es posible que deba hacer m\u00e1s preguntas sobre los datos que sobre las personas.  (Sugerencia: siempre es \u00fatil hacer las preguntas a continuaci\u00f3n en relaci\u00f3n con los datos, lo que a su vez ayudar\u00e1 a verificar lo que dice la gente).<\/em><\/p>\n\n\n\n

    • \u00bfLos datos de este campo son los que esperaba?<\/em><\/li>
    • \u00bfEs el rango\/variedad de datos lo que esperaba?<\/em><\/li>
    • \u00bfHay una cantidad inusual o inesperada de valores "NULOS"?<\/em><\/li><\/ul>\n\n\n\n

      Perfilar los datos<\/em><\/strong><\/h3>\n\n\n\n

      Como se describe en el Principio 6, existen formas eficientes de establecer r\u00e1pidamente KPI de referencia para la validaci\u00f3n. Lo mismo se aplica a la creaci\u00f3n de perfiles de una fuente de datos para una mejor comprensi\u00f3n. Algunos puntos de datos frecuentes que ayudan a establecer este perfil incluyen:<\/p>\n\n\n\n

      • N\u00famero de registros<\/em><\/li>
      • Intervalo de fechas (y cualquier intervalo)<\/em><\/li>
      • Rango de valores<\/em><\/li>
      • Valor promedio (y valores at\u00edpicos)<\/em><\/li>
      • Valores comunes)<\/em><\/li><\/ul>\n\n\n\n

        Esto proporciona el conocimiento necesario para analizar los datos y tambi\u00e9n proporciona una capa m\u00e1s de validaci\u00f3n a trav\u00e9s de esta "verificaci\u00f3n de cordura" de mirar los datos desde algunas perspectivas est\u00e1ndar.<\/p>\n\n\n\n

        Aprendizaje activo continuo<\/em><\/strong><\/h3>\n\n\n\n

        \u201cAprenda continuamente: \u00a1siempre hay \u201cuna cosa m\u00e1s\u201d que aprender! ~ Steve Jobs<\/p>\n\n\n\n

        Cada interacci\u00f3n que tenga con los datos puede ense\u00f1arle algo (confirme lo que esperaba o d\u00e9 indicadores de lo inesperado). <\/em> No puedo decirle con qu\u00e9 frecuencia escuchamos el \u201cOh s\u00ed, olvid\u00e9 decirte\u2026\u201d<\/em> porque cuando mira a\u00f1os atr\u00e1s en un conjunto de datos, esos matices no siempre son lo m\u00e1s importante. Si estos matices no se tienen en cuenta como parte del an\u00e1lisis, los resultados pueden estar sesgados. <\/p>\n\n\n\n

        Busque aprender sobre este tipo de escenarios dentro de su conjunto de datos.<\/em><\/strong><\/h3>\n\n\n\n

        Hubo una falla de retenci\u00f3n en un punto porque la transferencia de datos al servidor de archivo fall\u00f3 durante algunas semanas antes de que la detect\u00e1ramos.<\/p>\n\n\n\n

        Dejamos de usar el campo A para X y comenzamos a usar el campo D y E el a\u00f1o pasado.<\/p>\n\n\n\n

        Generalmente usamos el Campo M para informar y ejecutar la n\u00f3mina, pero los datos sin procesar est\u00e1n en el Campo Q, R, S y T.<\/p>\n\n\n\n

        Hay tantos matices que le suceden a los datos en el curso normal de los negocios, que es seguro asumir que habr\u00e1 algunos en sus datos. Busque aprenderlo y comprenderlo a lo largo de su esfuerzo.<\/em><\/strong><\/p>\n\n\n\n

        \n\n\n\n

        iDS proporciona soluciones de datos de consulta a corporaciones y bufetes de abogados de todo el mundo, lo que les otorga una ventaja decisiva, tanto dentro como fuera de la sala del tribunal. Nuestros expertos en la materia y estrategas de datos se especializan en encontrar soluciones a problemas de datos complejos, lo que garantiza que los datos se puedan aprovechar como un activo y no como un pasivo.<\/em><\/strong><\/p>","protected":false},"excerpt":{"rendered":"

        Data Understanding is Required for Proper Analysis This is the ninth installment in a blog series on Fact Crashing\u2122, the acceleration of the consideration of ACTION data (Ambient, Contextual, Transactional, IoT, Operational, Navigational) to the benefit of resolving disputes. There are 9 Principles of Fact Crashing\u2122. Earlier blogs covered:  Principle 1: Data is Evidence and […]<\/p>\n","protected":false},"author":7,"featured_media":3431,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10,38],"tags":[16],"class_list":["post-3308","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-methodologies","tag-fact-crashing","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"yoast_head":"\nFact Crashing\u2122 Principle 7: Data Understanding is Required for Analysis<\/title>\n<meta name=\"description\" content=\"In the ninth installment of Fact Crashing\u2122, iDS discusses the 7th principle, Data Understanding is Required for Proper Analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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