¿Cómo pueden los análisis mejorar los negocios? - transcripción del episodio 2 de techwise

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Eric Kavanagh: Damas y caballeros, hola y bienvenidos nuevamente al Episodio 2 de TechWise. Sí, de hecho, ¡es hora de conseguir gente sabia! Hoy tengo un grupo de personas realmente inteligentes en la línea para ayudarnos en ese esfuerzo. Mi nombre es Eric Kavanagh, por supuesto. Seré tu anfitrión, tu moderador, para esta sesión de la ronda del rayo. Tenemos mucho contenido aquí, amigos. Tenemos algunos nombres importantes en el negocio, que han sido analistas en nuestro espacio y cuatro de los proveedores más interesantes. Así que vamos a tener muchas buenas acciones en la llamada hoy. Y, por supuesto, usted en la audiencia juega un papel importante al hacer preguntas.

Entonces, una vez más, el programa es TechWise y el tema de hoy es "¿Cómo pueden las analíticas mejorar los negocios?" Obviamente, es un tema candente en el que tratará de comprender los diferentes tipos de análisis que puede hacer y cómo eso puede mejorar sus operaciones porque de eso se trata al final del día.

Así que puedes verme allí arriba en la cima, eso es realmente tuyo. Dr. Kirk Borne, un buen amigo de la Universidad George Mason. Es un científico de datos con una enorme cantidad de experiencia, una experiencia muy profunda en este espacio y minería de datos y big data y todo ese tipo de cosas divertidas. Y, por supuesto, tenemos nuestro propio Dr. Robin Bloor, analista jefe aquí en el Grupo Bloor. Quien se formó como actuario hace muchos, muchos años. Y se ha centrado realmente en todo este espacio de big data y el espacio analítico con bastante atención durante la última media década. Han pasado casi cinco años desde que lanzamos el Grupo Bloor per se. Entonces el tiempo vuela cuando te diviertes.

También vamos a escuchar a Will Gorman, arquitecto jefe de Pentaho; Steve Wilkes, CCO de WebAction; Frank Sanders, director técnico de MarkLogic; y Hannah Smalltree, directora de Treasure Data. Entonces, como he dicho, eso es mucho contenido.

Entonces, ¿cómo pueden los análisis ayudar a su negocio? Bueno, ¿cómo no puede ayudar a su negocio, francamente? Hay todo tipo de formas en que la analítica se puede utilizar para hacer cosas que mejoren su organización.

Así agilizar las operaciones. Esa es una de la que no escuchas tanto como de cosas como marketing o aumento de ingresos o incluso la identificación de oportunidades. Pero agilizar sus operaciones es algo realmente poderoso que puede hacer por su organización porque puede identificar lugares donde puede externalizar algo o puede agregar datos a un proceso en particular, por ejemplo. Y eso puede simplificarlo al no requerir que alguien levante el teléfono para llamar o enviar un correo electrónico. Hay tantas formas diferentes que puede optimizar sus operaciones. Y todo eso realmente ayuda a reducir su costo, ¿verdad? Esa es la clave, reduce el costo. Pero también le permite servir mejor a sus clientes.

Y si piensas en cuán impacientes se han vuelto las personas, y veo esto todos los días en términos de cómo las personas interactúan en línea, incluso con nuestros programas, proveedores de servicios que utilizamos. La paciencia que tiene la gente, la capacidad de atención, se acorta cada día más. Y lo que eso significa es que, como organización, debe responder cada vez más rápido para poder satisfacer a sus clientes.

Entonces, por ejemplo, si alguien está en su sitio de transmisión web o navega tratando de encontrar algo, si se frustra y se va, bueno, es posible que haya perdido un cliente. Y dependiendo de cuánto cobre por su producto o servicio, y tal vez eso sea un gran problema. Así que la conclusión es que la racionalización de las operaciones, creo, es uno de los mejores espacios para aplicar análisis. Y lo hace mirando los números, analizando los datos, descubriendo, por ejemplo, "Oye, ¿por qué estamos perdiendo tanta gente en esta página de nuestro sitio web?" "¿Por qué estamos recibiendo algunas de estas llamadas en este momento?"

Y cuanto más tiempo real pueda responder a ese tipo de cosas, mayores serán las posibilidades que tendrá de estar al tanto de la situación y hacer algo al respecto antes de que sea demasiado tarde. Debido a que existe esa ventana de tiempo cuando alguien se enoja por algo, está insatisfecho o está tratando de encontrar algo pero está frustrado; tienes una ventana de oportunidad para contactarlos, agarrarlos e interactuar con ese cliente. Y si lo hace de la manera adecuada con los datos correctos o una buena imagen del cliente, entendiendo quién es este cliente, cuál es su rentabilidad, cuáles son sus preferencias, si realmente puede manejar eso, lo hará un gran trabajo para aferrarse a sus clientes y obtener nuevos clientes. Y de eso se trata.

Entonces, con eso, se lo entregaré, en realidad, a Kirk Borne, uno de nuestros científicos de datos en la llamada de hoy. Y son bastante raros en estos días, amigos. Tenemos dos de ellos al menos en la llamada, así que es un gran problema. Con eso, Kirk, te lo entregaré para hablar sobre análisis y cómo ayuda a los negocios. Ve a por ello.

Dr. Kirk Borne: Bueno, muchas gracias, Eric. ¿Puedes escucharme?

Eric: Está bien, adelante.

Dr. Kirk: Bien, bien. Solo quiero compartir si hablo durante cinco minutos, y la gente me está saludando. Entonces, los comentarios de apertura, Eric, que hiciste realmente vinculados con este tema del que voy a hablar brevemente en los próximos minutos, que es el uso de Big Data y análisis de datos para las decisiones de apoyo, allí. El comentario que hizo sobre la racionalización operativa, para mí, de alguna manera se enmarca en este concepto de análisis operativo en el que puede ver casi en todas las aplicaciones en el mundo si se trata de una aplicación científica, un negocio, una seguridad cibernética y la aplicación de la ley y gobierno, asistencia sanitaria. Cualquier número de lugares donde tenemos un flujo de datos y estamos tomando algún tipo de respuesta o decisión en reacción a eventos, alertas y comportamientos que vemos en ese flujo de datos.

Entonces, una de las cosas de las que me gustaría hablar hoy es cómo extraen el conocimiento y las ideas de los grandes datos para llegar a ese punto en el que realmente podamos tomar decisiones para tomar medidas. Y con frecuencia hablamos de esto en un contexto de automatización. Y hoy quiero combinar la automatización con el analista humano en el ciclo. Entonces con esto quiero decir, mientras que el analista de negocios juega un papel importante aquí en términos de apuestas, calificación, validación de acciones específicas o reglas de aprendizaje automático que extraemos de los datos. Pero si llegamos a un punto en el que estamos bastante convencidos de que las reglas comerciales que hemos extraído y los mecanismos para alertarnos son válidos, entonces podemos pasar esto a un proceso automatizado. Realmente hacemos esa racionalización operativa de la que Eric estaba hablando.

Así que tengo un poco de juego con las palabras aquí, pero espero que, si te funciona, hablé sobre el desafío D2D. Y D2D, no solo los datos de las decisiones en todos los contextos, estamos viendo esto en la parte inferior de esta diapositiva con la esperanza de que pueda verlo, haciendo descubrimientos y aumentando los ingresos de nuestras canalizaciones de análisis.

Entonces, en este contexto, en realidad tengo este rol de comercializador para mí mismo ahora que trabajo y eso es; lo primero que quiere hacer es caracterizar sus datos, extraer las características, extraer las características de sus clientes o cualquier entidad que esté rastreando en su espacio. Tal vez es un paciente en un entorno de análisis de salud. Tal vez sea un usuario de la Web si está buscando una especie de problema de seguridad cibernética. Pero caracterice y extraiga características y luego extraiga algún contexto sobre ese individuo, sobre esa entidad. Y luego reúnes esas piezas que acabas de crear y las colocas en una especie de colección de la que luego puedes aplicar algoritmos de aprendizaje automático.

La razón por la que lo digo de esta manera es que, digamos, tienes una cámara de vigilancia en un aeropuerto. El video en sí es un enorme, gran volumen y también es muy desestructurado. Pero puede extraer de video vigilancia, biometría facial e identificar individuos en las cámaras de vigilancia. Entonces, por ejemplo, en un aeropuerto, puede identificar individuos específicos, puede rastrearlos a través del aeropuerto mediante la identificación cruzada del mismo individuo en múltiples cámaras de vigilancia. De modo que las características biométricas extraídas que realmente estás extrayendo y rastreando no son el video detallado real en sí. Pero una vez que tenga esas extracciones, puede aplicar reglas de aprendizaje automático y análisis para tomar decisiones sobre si necesita tomar una acción en un caso particular o si algo sucedió incorrectamente o algo que tiene la oportunidad de hacer una oferta. Si, por ejemplo, tiene una tienda en el aeropuerto y ve que ese cliente se acerca y sabe por otra información sobre ese cliente, tal vez él realmente se interesó en comprar cosas en la tienda libre de impuestos o algo así, haz esa oferta.

Entonces, ¿qué tipo de cosas quiero decir con caracterización y potencialización? Por caracterización quiero decir, nuevamente, extraer las características y características de los datos. Y esto puede ser generado por una máquina, luego sus algoritmos pueden extraer, por ejemplo, firmas biométricas de video o análisis de sentimientos. Puede extraer el sentimiento del cliente a través de revisiones en línea o redes sociales. Algunas de estas cosas pueden ser generadas por el ser humano, para que el ser humano, el analista de negocios, pueda extraer características adicionales que mostraré en la siguiente diapositiva.

Algunos de estos pueden ser crowdsourcing. Y por crowdsourcing, hay muchas maneras diferentes en que puedes pensar en eso. Pero de manera muy simple, por ejemplo, sus usuarios visitan su sitio web y ponen palabras de búsqueda, palabras clave, y terminan en una página determinada y realmente pasan tiempo allí en esa página. Que, al menos, realmente entienden que están viendo, navegando, haciendo clic en elementos de esa página. Lo que eso te dice es que la palabra clave que escribieron al principio es el descriptor de esa página porque aterrizó al cliente en la página que estaban anticipando. Y así puede agregar esa información adicional, es decir, los clientes que usan esta palabra clave en realidad identificaron esta página web dentro de nuestra arquitectura de información como el lugar donde ese contenido coincide con esa palabra clave.

Y así, el crowdsourcing es otro aspecto que a veces la gente olvida, ese tipo de seguimiento de las migas de pan de sus clientes, por así decirlo; ¿Cómo se mueven a través de su espacio, ya sea una propiedad en línea o una propiedad real? Y luego use ese tipo de camino que el cliente toma como información adicional sobre las cosas que estamos viendo.

Por lo tanto, quiero decir que las cosas generadas por humanos, o generadas por máquina, terminaron teniendo un contexto en forma de anotación o etiquetado de gránulos o entidades de datos específicos. Si esas entidades son pacientes en un hospital, clientes o lo que sea. Y así, hay diferentes tipos de etiquetado y anotaciones. Algo de eso se trata de los datos en sí. Esa es una de las cosas, qué tipo de información, qué tipo de información, cuáles son las características, las formas, tal vez las texturas y patrones, anomalías, comportamientos no anormales. Y luego extraer algo de semántica, es decir, cómo se relaciona esto con otras cosas que sé, o si este cliente es un cliente de electrónica. Este cliente es un cliente de ropa. O a este cliente le gusta comprar música.

Entonces, al identificar algunas semánticas sobre eso, a estos clientes que les gusta la música les gusta el entretenimiento. Tal vez podríamos ofrecerles alguna otra propiedad de entretenimiento. Entonces, entendiendo la semántica y también alguna procedencia, que básicamente dice: ¿de dónde vino esto, quién proporcionó esta afirmación, a qué hora, en qué fecha y bajo qué circunstancia?

Entonces, una vez que tenga todas esas anotaciones y caracterizaciones, agregue a eso el siguiente paso, que es el contexto, una especie de quién, qué, cuándo, dónde y por qué. Quien es el usuario? ¿En qué canal entraron? ¿Cuál fue la fuente de la información? ¿Qué tipo de reutilizaciones hemos visto en este producto de información o datos en particular? ¿Y qué es, es una especie de valor en el proceso de negocio? Y luego recopile esas cosas y adminístrelas, y realmente ayude a crear una base de datos, si desea pensar de esa manera. Hágalos buscar, reutilizar, por otros analistas de negocios o por un proceso automatizado que, la próxima vez que vea estos conjuntos de características, el sistema pueda tomar esta acción automática. Y así llegamos a ese tipo de eficiencia analítica operativa, pero cuanto más recopilamos información útil y completa, y luego la seleccionamos para estos casos de uso.

Nos ponemos manos a la obra. Hacemos el análisis de datos. Buscamos patrones interesantes, sorpresas, valores atípicos novedosos, anomalías. Buscamos las nuevas clases y segmentos en la población. Buscamos asociaciones y correlaciones y enlaces entre las diversas entidades. Y luego usamos todo eso para impulsar nuestro proceso de descubrimiento, decisión y toma de dólares.

Entonces, de nuevo, aquí tenemos la última diapositiva de datos que tengo, básicamente es resumir, mantener al analista de negocios al tanto, nuevamente, no estás extrayendo a ese humano y es muy importante mantener a ese humano allí.

Entonces, estas características, todas son proporcionadas por máquinas o analistas humanos o incluso crowdsourcing. Aplicamos esa combinación de cosas para mejorar nuestros conjuntos de capacitación para nuestros modelos y terminamos con modelos predictivos más precisos, menos falsos positivos y negativos, un comportamiento más eficiente, intervenciones más eficientes con nuestros clientes o con quien sea.

Entonces, al final del día, realmente solo estamos combinando el aprendizaje automático y los grandes datos con este poder de la cognición humana, que es donde entra ese tipo de anotación de etiquetado. Y eso puede conducir a través de la visualización y el tipo de análisis visual herramientas o entornos de datos inmersivos o crowdsourcing. Y, al final del día, lo que realmente está haciendo es generar nuestro descubrimiento, conocimientos y D2D. Y esos son mis comentarios, así que gracias por escuchar.

Eric: Oye, eso suena genial y déjame seguir adelante y entregarle las llaves al Dr. Robin Bloor para que también le dé su perspectiva. Sí, me gusta escuchar tu comentario sobre la racionalización del concepto de operaciones y estás hablando de análisis operativos. Creo que es un área grande que necesita ser explorada a fondo. Y supongo, muy rápido antes de Robin, te traeré de vuelta, Kirk. Requiere que tengas una colaboración bastante significativa entre varios jugadores de la empresa, ¿verdad? Tienes que hablar con la gente de operaciones; tienes que conseguir tu gente técnica. A veces obtienes tu gente de marketing o tu gente de interfaz web. Estos son típicamente grupos diferentes. ¿Tienes mejores prácticas o sugerencias sobre cómo hacer que todos pongan su piel en el juego?

Dr. Kirk: Bueno, creo que esto viene con la cultura empresarial de la colaboración. De hecho, hablo de las tres C de la cultura analítica. Uno es la creatividad; otro es curiosidad y el tercero es colaboración. Por lo tanto, desea personas creativas y serias, pero también debe lograr que estas personas colaboren. Y realmente comienza desde arriba, ese tipo de construcción de esa cultura con personas que deberían compartir abiertamente y trabajar juntas para alcanzar los objetivos comunes del negocio.

Eric: Todo tiene sentido. Y realmente tienes que conseguir un buen liderazgo en la parte superior para que eso suceda. Así que sigamos y se lo entreguemos al Dr. Bloor. Robin, el piso es tuyo.

Dr. Robin Bloor: De acuerdo. Gracias por esa introducción, Eric. Bien, la forma en que estos funcionan, estos espectáculos, porque tenemos dos analistas; Puedo ver la presentación del analista que los otros chicos no. Sabía lo que Kirk iba a decir y simplemente voy en un ángulo completamente diferente para que no nos traslapemos demasiado.

Entonces, de lo que realmente estoy hablando o tengo la intención de hablar aquí es sobre el rol del analista de datos versus el rol del analista de negocios. Y la forma en que lo estoy caracterizando, bueno, hasta cierto punto, es algo así como Jekyll y Hyde. La diferencia es que específicamente los científicos de datos, al menos en teoría, saben lo que están haciendo. Si bien los analistas de negocios no lo son, está de acuerdo con la forma en que funcionan las matemáticas, en lo que se puede confiar y en lo que no se puede confiar.

Así que vayamos a la razón por la que estamos haciendo esto, la razón por la cual el análisis de datos de repente se ha convertido en un gran problema, aparte del hecho de que podemos analizar cantidades muy grandes de datos y extraer datos de fuera de la organización; ¿Vale la pena? La forma en que miro esto, y creo que esto solo se está convirtiendo en un caso, pero definitivamente creo que es un caso, el análisis de datos es realmente I + D empresarial. Lo que realmente está haciendo de una forma u otra con el análisis de datos es que está viendo un proceso comercial de un tipo o si esa es la interacción con un cliente, ya sea con la forma en que su operación minorista, la forma en que implementa sus tiendas Realmente no importa cuál sea el problema. Estás viendo un proceso comercial determinado y estás tratando de mejorarlo.

El resultado de una investigación y desarrollo exitosos es un proceso de cambio. Y puede pensar en la fabricación, si lo desea, como un ejemplo habitual de esto. Porque en la fabricación, las personas recopilan información sobre todo para tratar de mejorar el proceso de fabricación. Pero creo que lo que sucedió o lo que está sucediendo en Big Data es que todo esto se está aplicando ahora a todas las empresas de cualquier tipo de cualquier manera que se pueda imaginar. Por lo tanto, casi cualquier proceso de negocio está sujeto a examen si puede recopilar datos al respecto.

Entonces esa es una cosa. Si lo desea, eso va a la cuestión del análisis de datos. ¿Qué puede hacer el análisis de datos para el negocio? Bueno, puede cambiar el negocio por completo.

Este diagrama particular que no voy a describir en profundidad, pero este es un diagrama que se nos ocurrió como la culminación del proyecto de investigación que hicimos durante los primeros seis meses de este año. Esta es una forma de representar una arquitectura de big data. Y una serie de cosas que vale la pena señalar antes de pasar a la siguiente diapositiva. Hay dos flujos de datos aquí. Uno es un flujo de datos en tiempo real, que va a lo largo de la parte superior del diagrama. El otro es un flujo de datos más lento que se encuentra en la parte inferior del diagrama.

Mira la parte inferior del diagrama. Tenemos a Hadoop como depósito de datos. Tenemos varias bases de datos. Tenemos toda una información allí con una gran cantidad de actividad ocurriendo en ella, la mayor parte de la cual es actividad analítica.

El punto que estoy haciendo aquí y el único punto que realmente quiero hacer aquí es que la tecnología es difícil. No es simple No es fácil. No es algo que cualquiera que sea nuevo en el juego pueda realmente armar. Esto es bastante complejo. Y si va a instrumentar a una empresa para realizar análisis confiables en todos estos procesos, entonces no es algo que sucederá específicamente rápidamente. Va a requerir que se agregue mucha tecnología a la mezcla.

Bueno. La pregunta de qué es un científico de datos, podría afirmar que soy un científico de datos porque en realidad me entrenaron en estadística antes de entrenarme en informática. E hice un trabajo actuarial durante un período de tiempo, así que sé cómo se organiza una empresa, análisis estadístico, también para funcionar. Esto no es algo trivial. Y hay una gran cantidad de mejores prácticas involucradas tanto en el lado humano como en el lado tecnológico.

Entonces, al hacer la pregunta "¿qué es un científico de datos?", Puse la imagen de Frankenstein simplemente porque es una combinación de cosas que deben tejerse juntas. Hay gestión de proyectos involucrados. Hay un profundo conocimiento en estadística. Existe una experiencia comercial en el dominio, que es más un problema de un analista de negocios que del científico de datos, necesariamente. Existe experiencia o la necesidad de comprender la arquitectura de datos y poder construir un arquitecto de datos y hay ingeniería de software involucrada. En otras palabras, probablemente sea un equipo. Probablemente no sea un individuo. Y eso significa que es probable que sea un departamento que deba organizarse y que su organización deba considerarse con bastante amplitud.

Lanzar a la mezcla el hecho del aprendizaje automático. No podríamos hacer, quiero decir, el aprendizaje automático no es nuevo en el sentido de que la mayoría de las técnicas estadísticas que se utilizan en el aprendizaje automático se conocen desde hace décadas. Hay algunas cosas nuevas, quiero decir que las redes neuronales son relativamente nuevas, creo que solo tienen unos 20 años, por lo que algunas de ellas son relativamente nuevas. Pero el problema con el aprendizaje automático era que realmente no teníamos la potencia de la computadora para hacerlo. Y lo que sucedió, aparte de cualquier otra cosa, es que la energía de la computadora ahora está en su lugar. Y eso significa muchísimo de lo que, digamos, los científicos de datos hemos hecho antes en términos de situaciones de modelado, muestreo de datos y luego ordenarlo para producir un análisis más profundo de los datos. En realidad, en algunos casos podemos simplemente darle poder a la computadora. Simplemente elija algoritmos de aprendizaje automático, tírelos a los datos y vea qué sale. Y eso es algo que un analista de negocios puede hacer, ¿verdad? Pero el analista de negocios necesita entender lo que están haciendo. Quiero decir, creo que ese es el problema realmente, más que cualquier otra cosa.

Bueno, esto es solo para saber más sobre negocios a partir de sus datos que por cualquier otro medio. Einstein no dijo eso, yo dije eso. Acabo de poner su imagen para credibilidad. Pero la situación en realidad está comenzando a desarrollarse en una en la que la tecnología, si se usa correctamente, y las matemáticas, si se usan adecuadamente, podrán administrar un negocio como cualquier individuo. Hemos visto esto con IBM. En primer lugar, podría vencer a los mejores jugadores de ajedrez, y luego podría vencer a los mejores jugadores de Jeopardy; pero eventualmente podremos vencer a los mejores muchachos al dirigir una empresa. Las estadísticas finalmente triunfarán. Y es difícil ver cómo eso no sucederá, simplemente no ha sucedido todavía.

Entonces, lo que estoy diciendo, y este es un mensaje completo de mi presentación, son estos dos temas del negocio. La primera es, ¿puedes acertar con la tecnología? ¿Puede hacer que la tecnología funcione para el equipo que realmente podrá presidirla y obtener beneficios para el negocio? Y luego, en segundo lugar, ¿puedes acertar con la gente? Y ambos son problemas. Y son problemas que, en este momento, no están resueltos.

De acuerdo, Eric, te lo devolveré. O tal vez debería pasarlo a Will.

Eric: en realidad sí. Gracias Will Gorman. Sí, ahí tienes, Will. Entonces veamos. Déjame darte la clave de WebEx. Entonces, ¿qué estás pasando? Pentaho, obviamente, ustedes han estado alrededor por un tiempo y han sido BI de código abierto donde comenzaron. Pero obtuviste mucho más de lo que solías tener, así que veamos qué obtuviste en estos días para el análisis.

Will Gorman: Absolutamente. ¡Hola a todos! Me llamo Will Gorman. Soy el arquitecto jefe de Pentaho. Para aquellos de ustedes que no han oído hablar de nosotros, acabo de mencionar que Pentaho es una empresa de análisis e integración de grandes datos. Hemos estado en el negocio por diez años. Nuestros productos han evolucionado junto con la comunidad de Big Data, comenzando como una plataforma de código abierto para la integración de datos y análisis, innovando con tecnología como Hadoop y NoSQL incluso antes de que se formaran entidades comerciales en torno a esa tecnología. Y ahora tenemos más de 1500 clientes comerciales y muchas más citas de producción como resultado de nuestra innovación en torno al código abierto.

Nuestra arquitectura es altamente integrable y extensible, diseñada específicamente para ser flexible ya que la tecnología de big data en particular está evolucionando a un ritmo muy rápido. Pentaho ofrece tres áreas principales de productos que trabajan juntas para abordar casos de uso de análisis de big data.

El primer producto en el alcance de nuestra arquitectura es Pentaho Data Integration, que está dirigido a tecnólogos e ingenieros de datos. Este producto ofrece una experiencia visual de arrastrar y soltar para definir canalizaciones de datos y procesos para orquestar datos dentro de entornos de big data y entornos tradicionales también. Este producto es una plataforma ligera de integración de datos y metadatabase construida en Java y se puede implementar como un proceso dentro de MapReduce o YARN o Storm y muchas otras plataformas por lotes y en tiempo real.

Nuestra segunda área de producto es el análisis visual. Con esta tecnología, las organizaciones y los OEM pueden ofrecer una rica experiencia de visualización y análisis de arrastrar y soltar para analistas de negocios y usuarios de negocios mediante navegadores y tabletas modernos, lo que permite la creación ad hoc de informes y paneles. Además de la presentación de paneles e informes perfectos para píxeles.

Nuestra tercera área de productos se centra en el análisis predictivo dirigido a científicos de datos, algoritmos de aprendizaje automático. Como se mencionó anteriormente, al igual que las redes neuronales y demás, se pueden incorporar en un entorno de transformación de datos, lo que permite a los científicos de datos pasar del entorno de modelado al de producción, dando acceso para predecir, y eso puede afectar los procesos comerciales de manera muy inmediata y muy rápida.

Todos estos productos están estrechamente integrados en una única experiencia ágil y brindan a nuestros clientes empresariales la flexibilidad que necesitan para abordar sus problemas comerciales. Estamos viendo un panorama de big data en rápida evolución en las tecnologías tradicionales. Todo lo que escuchamos de algunas compañías en el espacio de big data es que el EDW está cerca de su fin. De hecho, lo que vemos en nuestros clientes empresariales es que necesitan introducir big data en los procesos comerciales y de TI existentes y no reemplazar esos procesos.

Este diagrama simple muestra el punto en la arquitectura que vemos a menudo, que es un tipo de arquitectura de implementación EDW con integración de datos y casos de uso de BI. Ahora este diagrama es similar a la diapositiva de Robin en la arquitectura de big data, incorpora datos históricos y en tiempo real. A medida que surgen nuevas fuentes de datos y requisitos en tiempo real, vemos los grandes datos como una parte adicional de la arquitectura general de TI. Estas nuevas fuentes de datos incluyen datos generados por máquina, datos no estructurados, el volumen y la velocidad estándar y una variedad de requisitos sobre los que escuchamos en big data; no encajan en los procesos tradicionales de EDW. Pentaho trabaja en estrecha colaboración con Hadoop y NoSQL para simplificar la ingestión, el procesamiento de datos y la visualización de estos datos, así como combinar estos datos con las fuentes tradicionales para brindar a los clientes una visión completa de su entorno de datos. Hacemos esto de manera controlada para que TI pueda ofrecer una solución analítica completa a su línea de negocio.

Para concluir, me gustaría destacar nuestra filosofía en torno al análisis e integración de big data; Creemos que estas tecnologías funcionan mejor en conjunto con una sola arquitectura unificada, lo que permite una serie de casos de uso que de otro modo no serían posibles. Los entornos de datos de nuestros clientes son más que grandes datos, Hadoop y NoSQL. Cualquier dato es juego limpio. Y las fuentes de Big Data deben estar disponibles y trabajar juntas para impactar el valor comercial.

Finalmente, creemos que para resolver estos problemas comerciales en las empresas de manera muy efectiva a través de datos, TI y las líneas de negocios deben trabajar juntos en un enfoque combinado y gobernado para el análisis de big data. Bueno, muchas gracias por darnos tiempo para hablar, Eric.

Eric: Puedes apostar. No, eso es bueno. Quiero volver a ese lado de su arquitectura cuando lleguemos a las preguntas y respuestas. Así que pasemos al resto de la presentación y muchas gracias por eso. Ustedes definitivamente se han estado moviendo rápidamente los últimos años, tengo que decirlo con seguridad.

Entonces, Steve, déjame seguir y entregártelo. Y simplemente haga clic allí en la flecha hacia abajo y vaya por ella. Entonces Steve, te estoy dando las llaves. Steve Wilkes, simplemente haz clic en la flecha hacia abajo más lejana que hay en tu teclado.

Steve Wilkes: Ahí vamos.

Eric: ahí tienes.

Steve: Sin embargo, esa es una gran introducción que me has dado.

Eric: si.

Steve: Entonces yo soy Steve Wilkes. Soy el CCO en WebAction. Solo hemos existido durante los últimos años y definitivamente también nos hemos estado moviendo rápido desde entonces. WebAction es una plataforma de análisis de big data en tiempo real. Eric mencionó anteriormente, más o menos, cuán importante es el tiempo real y cuán en tiempo real están obteniendo sus aplicaciones. Nuestra plataforma está diseñada para crear aplicaciones en tiempo real. Y para habilitar la próxima generación de aplicaciones basadas en datos que se pueden construir de forma incremental y permitir a las personas crear paneles a partir de los datos generados por esas aplicaciones, pero centrándose en tiempo real.

Nuestra plataforma es en realidad una plataforma completa de extremo a extremo, que hace todo, desde la adquisición de datos, el procesamiento de datos, hasta la visualización de datos. Y permite que múltiples tipos diferentes de personas dentro de nuestra empresa trabajen juntas para crear aplicaciones verdaderas en tiempo real, brindándoles información sobre las cosas que suceden en su empresa tal como sucedieron.

Y esto es un poco diferente de lo que la mayoría de la gente ha estado viendo en Big Data, por lo que el enfoque tradicional, bueno, tradicional en los últimos años, con Big Data ha sido capturarlo de un montón de diferentes fuentes y luego apílelo en un gran depósito o lago o como quiera llamarlo. Y luego procesarlo cuando necesite ejecutar una consulta en él; para ejecutar análisis históricos a gran escala o incluso solo consultas ad hoc de grandes cantidades de datos. Ahora eso funciona para ciertos casos de uso. Pero si quiere ser proactivo en su empresa, si realmente quiere que le digan qué está sucediendo en lugar de saber cuándo algo salió mal hacia el final del día o al final de la semana, entonces realmente necesita moverse a tiempo real

Y eso cambia un poco las cosas. Mueve el procesamiento al medio. De manera efectiva, está tomando esos flujos de grandes cantidades de datos que se generan continuamente dentro de la empresa y los procesa a medida que los obtiene. Y debido a que lo está procesando a medida que lo obtiene, no tiene que almacenar todo. Simplemente puede almacenar la información importante o las cosas que necesita recordar que realmente sucedieron. Entonces, si está rastreando la ubicación GPS de los vehículos que se mueven por el camino, realmente no le importa dónde están cada segundo, no necesita almacenar dónde están cada segundo. Solo debes preocuparte, ¿han dejado este lugar? ¿Han llegado a este lugar? ¿Han conducido, o no, la autopista?

Por lo tanto, es muy importante tener en cuenta que a medida que se generan más y más datos, las tres Vs. La velocidad básicamente determina la cantidad de datos que genera cada día. Cuantos más datos se generen, más tendrá que almacenar. Y cuanto más tenga que almacenar, más tardará en procesarse. Pero si puede procesarlo a medida que lo obtiene, obtendrá un gran beneficio y podrá reaccionar ante eso. Se le puede decir que las cosas están sucediendo en lugar de tener que buscarlas más tarde.

Entonces, nuestra plataforma está diseñada para ser altamente escalable. Tiene tres piezas principales: la pieza de adquisición, la pieza de procesamiento y luego las piezas de visualización de entrega de la plataforma. En el lado de la adquisición, no solo estamos viendo datos de registro generados por la máquina, como registros web o aplicaciones que tienen todos los demás registros que se están generando. We can also go in and do change data capture from databases. So that basically enables us to, we've seen the ETL side that Will presented and traditional ETL you have to run queries against the databases. We can be told when things happen in the database. We change it and we capture it and receive those events. And then there's obviously the social feeds and live device data that's being pumped to you over TCP or ACDP sockets.

There's tons of different ways of getting data. And talking of volume and velocity, we're seeing volumes that are billions of events per day, right? So it's large, large amounts of data that is coming in and needs to be processed.

That is processed by a cluster of our servers. The servers all have the same architecture and are all capable of doing the same things. But you can configure them to, sort of, do different things. And within the servers we have a high-speed query processing layer that enables you to do some real-time analytics on the data, to do enrichments of the data, to do event correlation, to track things happening within time windows, to do predictive analytics based on patterns that are being seen in the data. And that data can then be stored in a variety places - the traditional RDBMS, enterprise data warehouse, Hadoop, big data infrastructure.

And the same live data can also be used to power real-time data-driven apps. Those apps can have a real-time view of what's going on and people can also be alerted when important things happen. So rather than having to go in at the end of the day and find out that something bad really happened earlier on the day, you could be alerted about it the second we spot it and it goes straight to the page draw down to find out what's going on.

So it changes the paradigm completely from having to analyze data after the fact to being told when interesting things are happening. And our platform can then be used to build data-driven applications. And this is really where we're focusing, is building out these applications. For customers, with customers, with a variety of different partners to show true value in real-time data analysis. So that allows people that, or companies that do site applications, for example, to be able track customer usage over time and ensure that the quality of service is being met, to spot real-time fraud or money laundering, to spot multiple logins or hack attempts and those kind of security events, to manage things like set-top boxes or other devices, ATM machines to monitor them in real time for faults, failures that have happened, could happen, will happen in the future based on predictive analysis. And that goes back to the point of streamlining operations that Eric mentioned earlier, to be able to spot when something's going to happen and organize your business to fix those things rather than having to call someone out to actually do something after the fact, which is a lot more expensive.

Consumer analytics is another piece to be able to know when a customer is doing something while they're still there in your store. Data sent to management to be able to in real time monitor resource usage and change where things are running and to be able to know about when things are going to fail in a much more timely fashion.

So that's our products in a nutshell and I'm sure we'll come back to some of these things in the Q&A session. Gracias.

Eric: Yes, indeed. Gran trabajo. Okay good. And now next stop in our lightning round, we've got Frank Sanders calling in from MarkLogic. I've known about these guys for a number of years, a very, very interesting database technology. So Frank, I'm turning it over to you. Just click anywhere in that. Use the down arrow on your keyboard and you're off to the races. Ahí tienes.

Frank Sanders: Thank you very much, Eric. So as Eric mentioned, I'm with a company called MarkLogic. And what MarkLogic does is we provide an enterprise NoSQL database. And perhaps, the most important capability that we bring to the table with regards to that is the ability to actually bring all of these disparate sources of information together in order to analyze, search and utilize that information in a system similar to what you're used to with traditional relational systems, right?

And some of the key features that we bring to the table in that regard are all of the enterprise features that you'd expect from a traditional database management system, your security, your HA, your DR, your backup are in store, your asset transactions. As well as the design that allows you to scale out either on the cloud or in the commodity hardware so that you can handle the volume and the velocity of the information that you're going to have to handle in order to build and analyze this sort of information.

And perhaps, the most important capability is that fact that we're scheme agnostic. What that means, practically, is that you don't have to decide what your data is going to look like when you start building your applications or when you start pulling those informations together. But over time, you can incorporate new data sources, pull additional information in and then use leverage and query and analyze that information just as you would with anything that was there from the time that you started the design. Okay?

So how do we do that? How do we actually enable you to load different sorts of information, whether it be text, RDF triples, geospatial data, temporal data, structured data and values, or binaries. And the answer is that we've actually built our server from the ground up to incorporate search technology which allows you to put information in and that information self describes and it allows you to query, retrieve and search that information regardless of its source or format.

And what that means practically is that - and why this is important when you're doing analysis - is that analytics and information is most important ones when it's properly contextualized and targeted, right? So a very important key part of any sort of analytics is search, and the key part is search analytics. You can't really have one without the other and successfully achieve what you set out to achieve. Right?

And I'm going to talk briefly about three and a half different use cases of customers that we have at production that are using MarkLogic to power this sort of analytics. Bueno. So the first such customer is Fairfax County. And Fairfax County has actually built two separate applications. One is based around permitting and property management. And the other, which is probably a bit more interesting, is the Fairfax County police events application. What the police events application actually does is it pulls information together like police reports, citizen reports and complaints, Tweets, other information they have such as sex offenders and whatever other information that they have access to from other agencies and sources. Then they allow them to visualize that and present this to the citizens so they can do searches and look at various crime activity, police activity, all through one unified geospatial index, right? So you can ask questions like, "what is the crime rate within five miles" or "what crimes occurred within five miles of my location?" Bueno.

Another user that we've got, another customer that we have is OECD. Why OECD is important to this conversation is because in addition to everything that we've enabled for Fairfax County in terms of pulling together information, right; all the information that you would get from all various countries that are members of the OECD that they report on from an economic perspective. We actually laid a target drill into that, right. So you can see on the left-hand side we're taking the view of Denmark specifically and you can kind of see a flower petal above it that rates it on different axes. Right? And that's all well and good. But what the OECD has done is they've gone a step further.

In addition to these beautiful visualizations and pulling all these information together, they're actually allowing you in real time to create your own better life index, right, which you can see on the right-hand side. So what you have there is you have a set of sliders that actually allow you to do things like rank how important housing is to you or income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and your work/life balance. And dynamically based on how you are actually inputting that information and weighting those things, MarkLogic's using its real-time indexing capability and query capability to actually then change how each and every one of these countries is ranked to give you an idea of how well your country or your lifestyle maps through a given country. Okay?

And the final example that I'm going to share is MarkMail. And what MarkMail really tries to demonstrate is that we can provide these capabilities and you can do the sort of analysis not only on structured information or information that's coming in that's numerical but actually on more loosely structured, unstructured information, right? Things like emails. And what we've seen here is we're actually pulling information like geolocation, sender, company, stacks and concepts like Hadoop being mentioned within the context of an email and then visualizing it on the map as well as looking at who those individuals and what list across that, a sent and a date. This where you're looking at things that are traditionally not structured, that may be loosely structured, but are still able to derive some structured analysis from that information without having to go to a great length to actually try and structure it or process it at a time. And that's it.

Eric: Hey, okay good. And we got one more. We've got Hannah Smalltree from Treasure Data, a very interesting company. And this is a lot of great content, folks. Thank you so much for all of you for bringing such good slides and such good detail. So Hannah, I just gave the keys to you, click anywhere and use the down arrow on your keyboard. You got it. Llevatelo.

Hannah Smalltree: Thank you so much, Eric. This is Hannah Smalltree from Treasure Data. I'm a director with Treasure Data but I have a past as a tech journalist, which means that I appreciate two things. First of all, these can be long to sit through a lot of different descriptions of technology, and it can all sound like it runs together so I really want to focus on our differentiator. And the real-world applications are really important so I appreciate that all of my peers have been great about providing those.

Treasure Data is a new kind of big data service. We're delivered entirely on the cloud in a software as a service or managed-service model. So to Dr. Bloor's point earlier, this technology can be really hard and it can be very time consuming to get up and running. With Treasure Data, you can get all of these kinds of capabilities that you might get in a Hadoop environment or a complicated on-premise environment in the cloud very quickly, which is really helpful for these new big data initiatives.

Now we talk about our service in a few different phases. We offer some very unique collection capabilities for collecting streaming data so particularly event data, other kinds of real-time data. We'll talk a little bit more about those data types. That is a big differentiator for our service. As you get into big data or if you are already in it then you know that collecting this data is not trivial. When you think about a car with 100 sensors sending data every minute, even those 100 sensors sending data every ten minutes, that adds up really quickly as you start to multiply the amount of products that you have out there with sensors and it quickly becomes very difficult to manage. So we are talking with customers who have millions, we have customers who have billions of rows of data a day that they're sending us. And they're doing that as an alternative to try and to manage that themselves in a complicated Amazon infrastructure or even try to bring it into their own environment.

We have our own cloud storage environment. We manage it. We monitor it. We have a team of people that's doing all that tuning for you. And so the data flows in, it goes into our managed storage environment.

Then we have embedded query engines so that your analyst can go in and run queries and do some initial data discovery and exploration against the data. We have a couple of different query engines for it actually now. You can use SQL syntax, which your analysts probably know and love, to do some basic data discovery, to do some more complex analytics that are user-defined functions or even to do things as simple as aggregate that data and make it smaller so that you can bring it into your existing data warehouse environment.

You can also connect your existing BI tools, your Tableau, is a big partner of ours; but really most BIs, visualization or analytics tools can connect via our industry standard JDBC and ODBC drivers. So it gives you this complete set of big data capabilities. You're allowed to export your queries results or data sets anytime for free, so you can easily integrate that data. Treat this as a data refinery. I like to think of it more as a refinery than a lake because you can actually do stuff with it. You can go through, find the valuable information and then bring it into your enterprise processes.

The next slide, we talk about the three Vs of big data - some people say four or five. Our customers tend to struggle with the volume and velocity of the data coming at them. And so to get specific about the data types - Clickstream, Web access logs, mobile data is a big area for us, mobile application logs, application logs from custom Web apps or other applications, event logs. And increasingly, we have a lot of customers dealing with sensor data, so from wearable devices, from products, from automotive, and other types of machine data. So when I say big data, that's the type of big data that I'm talking about.

Now, a few use cases in perspective for you - we work with a retailer, a large retailer. They are very well known in Asia. They're expanding here in the US. You'll start to see stores; they're often called Asian IKEA, so, simple design. They have a loyalty app and a website. And in fact, using Treasure Data, they were able to deploy that loyalty app very quickly. Our customers get up and running within days or weeks because of our software and our service architecture and because we have all of the people doing all of that hard work behind the scenes to give you all of those capabilities as a service.

So they use our service for mobile application analytics looking at the behavior, what people are clicking on in their mobile loyalty application. They look at the website clicks and they combine that with our e-commerce and POS data to design more efficient promotions. They actually wanted to drive people into stores because they found that people, when they go into stores spend more money and I'm like that; to pick up things, you spend more money.

Another use case that we're seeing in digital video games, incredible agility. They want to see exactly what is happening in their game, and make changes to that game even within hours of its release. So for them, that real-time view is incredibly important. We just released a game but we noticed in the first hour that everyone is dropping off at Level 2; how are we going to change that? They might change that within the same day. So real time is very important. They're sending us billions of event logs per day. But that could be any kind of mobile application where you want some kind of real-time view into how somebody's using that.

And finally, a big area for us is our product behavior and sensor analytics. So with sensor data that's in cars, that's in other kinds of machines, utilities, that's another area for us, in wearable devices. We have research and development teams that want to quickly know what the impact of a change to a product is or people interested in the behavior of how people are interacting with the product. And we have a lot more use cases which, of course, we're happy to share with you.

And then finally, just show you how this can fit into your environment, we offer again the capability to collect that data. We have very unique collection technology. So again, if real-time collection is something that you're struggling with or you anticipate struggling with, please come look at the Treasure Data service. We have really made capabilities for collecting streaming data. You can also bulk load your data, store it, analyze it with our embedded query engines and then, as I mentioned, you can export it right to your data warehouse. I think Will mentioned the need to introduce big data into your existing processes. So not go around or create a new silo, but how do you make that data smaller and then move it into your data warehouse and you can connect to your BI, visualization and advanced analytics tools.

But perhaps, the key points I want to leave you with are that we are managed service, that's software as a service; it's very cost effective. A monthly subscription service starting at a few thousand dollars a month and we'll get you up and running in a matter of days or weeks. So compare that with the cost of months and months of building your own infrastructure and hiring those people and finding it and spending all that time on infrastructure. If you're experimenting or if you need something yesterday, you can get up and running really quickly with Treasure Data.

And I'm just pointing you to our website and to our starter service. If you're a hands-on person who likes to play, please check out our starter service. You can get on, no credit card required, just name and email, and you can play with our sample data, load up your own data and really get a sense of what we're talking about. So thanks so much. Also, check our website. We were named the Gartner Cool Vendor in Big Data this year, very proud of that. And you can also get a copy of that report for free on our website as well as many other analyst white papers. So thanks so much.

Eric: Okay, thank you very much. We've got some time for questions here, folks. We'll go a little bit long too because we've got a bunch of folks still on the line here. And I know I've got some questions myself, so let me go ahead and take back control and then I'm going to ask a couple of questions. Robin and Kirk, feel free to dive in as you see fit.

So let me go ahead and jump right to one of these first slides that I checked out from Pentaho. So here, I love this evolving big data architecture, can you kind of talk about how it is that this kind of fits together at a company? Because obviously, you go into some fairly large organization, even a mid-size company, and you're going to have some people who already have some of this stuff; how do you piece this all together? Like what does the application look like that helps you stitch all this stuff together and then what does the interface look like?

Will: Great question. The interfaces are a variety depending on the personas involved. But as an example, we like to tell the story of - one of the panelists mentioned the data refinery use case - we see that a lot in customers.

One of our customer examples that we talk about is Paytronix, where they have that traditional EDW data mart environment. They are also introducing Hadoop, Cloudera in particular, and with various user experiences in that. So first there's an engineering experience, so how do you wire all these things up together? How do you create the glue between the Hadoop environment and EDW?

And then you have the business user experience which we talked about, a number of BI tools out there, right? Pentaho has a more embeddable OEM BI tool but there are great ones out there like Tableau and Excel, for instance, where folks want to explore the data. But usually, we want to make sure that the data is governed, right? One of the questions in the discussions, what about single-version experience, how do you manage that, and without the technology like Pentaho data integration to blend that data together not on the glass but in the IT environments. So it really protects and governs the data and allows for a single experience for the business analyst and business users.

Eric: Okay, good. That's a good answer to a difficult question, quite frankly. And let me just ask the question to each of the presenters and then maybe Robin and Kirk if you guys want to jump in too. So I'd like to go ahead and push this slide for WebAction which I do think is really a very interesting company. Actually, I know Sami Akbay who is one of the co-founders, as well. I remember talking to him a couple years ago and saying, "Hey man, what are you doing? What are you up to? I know you've got to be working on something." And of course, he was. He was working on WebAction, under the covers here.

A question came in for you, Steve, so I'll throw it over to you, of data cleansing, right? Can you talk about these components of this real-time capability? How do you deal with issues like data cleansing or data quality or how does that even work?

Steve: So it really depends on where you're getting your feeds from. Typically, if you're getting your feeds from a database as you change data capture then, again, it depends there on how the data was entered. Data cleansing really becomes a problem when you're getting your data from multiple sources or people are entering it manually or you kind of have arbitrary texts that you have to try and pull things out of. And that could certainly be part of the process, although that type simply doesn't lend itself to true, kind of, high-speed real-time processing. Data cleansing, typically, is an expensive process.

So it may well be that that could be done after the fact in the store site. But the other thing that the platform is really, really good at is correlation, so in correlation and enrichment of data. You can, in real time, correlate the incoming data and check to see whether it matches a certain pattern or it matches data that's being retrieved from a database or Hadoop or some other store. So you can correlate it with historical data, is one thing you could do.

The other thing that you can do is basically do analysis on that data and see whether it kind of matches certain required patterns. And that's something that you can also do in real time. But the traditional kind of data cleansing, where you're correcting company names or you're correcting addresses and all those types of things, those should probably be done in the source or kind of after the fact, which is very expensive and you pray that they won't do those in real time.

Eric: Yeah. And you guys are really trying to address the, of course, the real-time nature of things but also get the people in time. And we talked about, right, I mentioned at the top of the hour, this whole window of opportunity and you're really targeting specific applications at companies where you can pull together data not going the usual route, going this alternate route and do so in such a low latency that you can keep customers. For example, you can keep people satisfied and it's interesting, when I talked to Sami at length about what you guys are doing, he made a really good point. He said, if you look at a lot of the new Web-based applications; let's look at things like Twitter, Bitly or some of these other apps; they're very different than the old applications that we looked at from, say, Microsoft like Microsoft Word.

I often use Microsoft as sort of a whipping boy and specifically Word to talk about the evolution of software. Because Microsoft Word started out as, of course, a word processing program. I'm one of those people who remember Word Perfect. I loved being able to do the reveal keys or the reveal code, basically, which is where you could see the actual code in there. You could clean something up if your bulleted list was wrong, you can clean it up. Well, Word doesn't let you do that. And I can tell you that Word embeds a mountain of code inside every page that you do. If anyone doesn't believe me, then go to Microsoft Word, type "Hello World" and then do "Export as" or "Save as" .html. Then open that document in a text editor and that will be about four pages long of codes just for two words.

So you guys, I thought it was very interesting and it's time we talked about that. And that's where you guys focus on, right, is identifying what you might call cross-platform or cross-enterprise or cross-domain opportunities to pull data together in such quick time that you can change the game, right?

Steve: Yeah, absolutely. And one of the keys that, I think, you did elude to, anyway, is you really want to know about things happening before your customers do or before they really, really become a problem. As an example are the set-top boxes. Cable boxes, they emit telemetry all the time, loads and loads of telemetry. And not just kind of the health of the box but it's what you're watching and all that kind of stuff, right? The typical pattern is you wait till the box fails and then you call your cable provider and they'll say, "Well, we will get to you sometime between the hours of 6am and 11pm in the entire month of November." That isn't a really good customer experience.

But if they could analyze that telemetry in real time then they could start to do things like that we know these boxes are likely to fail in the next week based historical patterns. Therefore we'll schedule our cable repair guy to turn up at this person's house prior to it failing. And we'll do that in a way that suits us rather than having to send him from Santa Cruz up to Sunnyvale. We'll schedule everything in a nice order, traveling salesman pattern, etc., so that we can optimize our business. And so the customer is happy because they don't have a failing cable box. And the cable provider is happy because they have just streamlined things and they don't have to send people all over the place. That's just a very quick example. But there are tons and tons of examples where knowing about things as they happen, before they happen, can save companies a fortune and really, really improve their customer relations.

Eric: Yeah, right. No doubt about it. Let's go ahead and move right on to MarkLogic. As I mentioned before, I've known about these guys for quite some time and so I'll bring you into this, Frank. You guys were far ahead of the whole big data movement in terms of building out your application, it's really database. But building it out and you talked about the importance of search.

So a lot of people who followed the space know that a lot of the NoSQL tools out there are now bolting on search capabilities whether through third parties or they try to do their own. But to have that search already embedded in that, baked-in so to speak, really is a big deal. Because if you think about it, if you don't have SQL, well then how do you go in and search the data? How do you pull from that data resource? And the answer is to typically use search to get to the data that you're looking for, right?

So I think that's one of the key differentiators for you guys aside being able to pull data from all these different sources and store that data and really facilitate this sort of hybrid environment. I'm thinking that search capability is a big deal for you, right?

Frank: Yeah, absolutely. In fact, that's the only way to solve the problem consistently when you don't know what all the data is going to look like, right? If you cannot possibly imagine all the possibilities then the only way to make sure that you can locate all the information that you want, that you can locate it consistently and you can locate it regardless of how you evolve your data model and your data sets is to make sure you give people generic tools that allow them to interrogate that data. And the easiest, most intuitive way to do that is through a search paradigm, right? And through the same approach in search takes where we created an inverted index. You have entries where you can actually look into those and then find records and documents and rows that actually contain the information you're looking for to then return it to the customer and allow them to process it as they see fit.

Eric: Yeah and we talked about this a lot, but you're giving me a really good opportunity to kind of dig into it - the whole search and discovery side of this equation. But first of all, it's a lot of fun. For anyone who likes that stuff, this is the fun part, right? But the other side of the equation or the other side of the coin, I should say, is that it really is an iterative process. And you got to be able to - here I'll be using some of the marketing language - have that conversation with the data, right? In other words, you need to be able to test the hypothesis, play around with it and see how that works. Maybe that's not there, test something else and constantly change things and iterate and search and research and just think about stuff. And that's a process. And if you have big hurdles, meaning long latencies or a difficult user interface or you got to go ask IT; that just kills the whole analytical experience, right?

So it's important to have this kind of flexibility and to be able to use searches. And I like the way that you depicted it here because if we're looking at searching around different, sort of, concepts or keys, if you will, key values and they're different dimensions. You want to be able to mix and match that stuff in order to enable your analyst to find useful stuff, right?

Frank: Yeah, absolutely. I mean, hierarchy is an important thing as well, right? So that when you include something like a title, right, or a specific term or value, that you can actually point to the correct one. So if you're looking for a title of an article, you're not getting titles of books, right? Or you're not getting titles of blog posts. The ability to distinguish between those and through the hierarchy of the information is important as well.

You pointed out earlier the development, absolutely, right? The ability for our customers to actually pull in new data sources in a matter of hours, start to work with them, evaluate whether or not they're useful and then either continue to integrate them or leave them by the wayside is extremely valuable. When you compare it to a more traditional application development approach where what you end up doing is you have to figure out what data you want to ingest, source the data, figure out how you're going to fit it in your existing data model or model that in, change that data model to incorporate it and then actually begin the development, right? Where we kind of turn that on our head and say just bring it to us, allow you to start doing the development with it and then decide later whether or not you want to keep it or almost immediately whether or not it's of value.

Eric: Yeah, it's a really good point. That's a good point. So let me go ahead and bring in our fourth presenter here, Treasure Data. I love these guys. I didn't know much about them so I'm kind of kicking myself. And then Hannah came to us and told us what they were doing. And Hannah mentioned, she was a media person and she went over to the dark side.

Hannah: I did, I defected.

Eric: That's okay, though, because you know what we like in the media world. So it's always nice when a media person goes over to the vendor side because you understand, hey, this stuff is not that easy to articulate and it can be difficult to ascertain from a website exactly what this product does versus what that product does. And what you guys are talking about is really quite interesting. Now, you are a cloud-managed service. So any data that someone wants to use they upload to your cloud, is that right? And then you will ETL or CDC, additional data up to the cloud, is that how that works?

Hannah: Well, yeah. So let me make an important distinction. Most of the data, the big data, that our customers are sending us is already outside the firewall - mobile data, sensor data that's in products. And so we're often used as an interim staging area. So data is not often coming from somebody's enterprise into our service so much as it's flowing from a website, a mobile application, a product with lots of sensors in it - into our cloud environment.

Now if you'd like to enrich that big data in our environment, you can definitely bulk upload some application data or some customer data to enrich that and do more of the analytics directly in the cloud. But a lot of our value is around collecting that data that's already outside the firewall, bringing together into one place. So even if you do intend to bring this up sort of behind your firewall and do more of your advanced analytics or bring it into your existing BI or analytics environment, it's a really good staging point. Because you don't want to bring a billion rows of day into your data warehouse, it's not cost effective. It's even difficult if you're planning to store that somewhere and then batch upload.

So we're often the first point where data is getting collected that's already outside firewall.

Eric: Yeah, that's a really good point, too. Because a lot of companies are going to be nervous about taking their proprietary customer data, putting it up in the cloud and to manage the whole process.

Hannah: Yeah.

Eric: And what you're talking about is really getting people a resource for crunching those heavy duty numbers of, as you suggest, data that's third party like mobile data and the social data and all that kind of fun stuff. That's pretty interesting.

Hannah: Yeah, absolutely. And probably they are nervous about the products because the data are already outside. And so yeah, before bringing it in, and I really like that refinery term, as I mentioned, versus the lake. So can you do some basic refinery? Get the good stuff out and then bring it behind the firewall into your other systems and processes for deeper analysis. So it's really all data scientists can do, real-time data exploration of this new big data that's flowing in.

Eric: Yeah, that's right. Well, let me go ahead and bring in our analysts and we'll kind of go back in reverse order. I'll start with you, Robin, with respect to Treasure Data and then we'll go to Kirk for some of the others. And then back to Robin and back to Kirk just to kind of get some more assessment of this.

And you know the data refinery, Robin, that Hannah is talking about here. I love that concept. I've heard only a few people talking about it that way but I do think that you certainly mentioned that before. And it really does speak to what is actually happening to your data. Because, of course, a refinery, it basically distills stuff down to its root level, if you think about oil refineries. I actually studied this for a while and it's pretty basic, but the engineering that goes into it needs to be exactly correct or you don't get the stuff that you want. So I think it's a great analogy. What do you think about this whole concept of the Treasure Data Cloud Service helping you tackle some of those very specific analytical needs without having to bring stuff in-house?

Robin: Well, I mean, obviously depending on the circumstances to how convenient that is. But anybody that's actually got already made process is already going to put you ahead of the game if you haven't got one yourself. This is the first takeaway for something like that. If somebody assembled something, they've done it, it's proven in the marketplace and therefore there's some kind of value in effect, well, the work is already gone into it. And there's also the very general fact that refining of data is going to be a much bigger issue than it ever was before. I mean, it is not talked about, in my opinion anyway, it's not talked about as much as it should be. Simply apart from the fact that size of the data has grown and the number of sources and the variety of those sources has grown quite considerably. And the reliability of the data in terms of whether it's clean, they need to disambiguate the data, all sorts of issues that rise just in terms of the governance of the data.

So before you actually get around to being able to do reliable analysis on it, you know, if your data's dirty, then your results will be skewed in some way or another. So that is something that has to be addressed, that has to be known about. And the triangulator of providing, as far as I can see, a very viable service to assist in that.

Eric: Yes, indeed. Well, let me go ahead and bring Kirk back into the equation here just real quickly. I wanted to take a look at one of these other slides and just kind of get your impression of things, Kirk. So maybe let's go back to this MarkLogic slide. And by the way, Kirk provided the link, if you didn't see it folks, to some of his class discovery slides because that's a very interesting concept. And I think this is kind of brewing at the back of my mind, Kirk, as I was talking about this a moment ago. This whole question that one of the attendees posed about how do you go about finding new classes. I love this topic because it really does speak to the sort of, the difficult side of categorizing things because I've always had a hard time categorizing stuff. I'm like, "Oh, god, I can fit in five categories, where do I put it?" So I just don't want to categorize anything, right?

And that's why I love search, because you don't have to categorize it, you don't have to put it in the folder. Just search for it and you'll find it if you know how to search. But if you're in that process of trying to segment, because that's basically what categorization is, it's segmenting; finding new classes, that's kind of an interesting thing. Can you kind of speak to the power of search and semantics and hierarchies, for example, as Frank was talking about with respect to MarkLogic and the role that plays in finding new classes, what do you think about that?

Kirk: Well, first of all, I'd say you are reading my mind. Because that was what I was thinking of a question even before you were talking, this whole semantic piece here that MarkLogic presented. And if you come back to my slide, you don't have to do this, but back on the slide five on what I presented this afternoon; I talked about this semantics that the data needs to be captured.

So this whole idea of search, there you go. I firmly believe in that and I've always believed in that with big data, sort of take the analogy of Internet, I mean, just the Web, I mean having the world knowledge and information and data on a Web browser is one thing. But to have it searchable and retrievable efficiently as one of the big search engine companies provide for us, then that's where the real power of discovery is. Because connecting the search terms, sort of the user interests areas to the particular data granule, the particular webpage, if you want to think the Web example or the particular document if you're talking about document library. Or a particular customer type of segment if that's your space.

And semantics gives you that sort of knowledge layering on top of just a word search. If you're searching for a particular type of thing, understanding that a member of a class of such things can have a certain relationship to other things. Even include that sort of relationship information and that's a class hierarchy information to find things that are similar to what you're looking for. Or sometimes even the exact opposite of what you're looking for, because that in a way gives you sort of additional core of understanding. Well, probably something that's opposite of this.

Eric: Yeah.

Kirk: So actually understand this. I can see something that's opposite of this. And so the semantic layer is a valuable component that's frequently missing and it's interesting now that this would come up here in this context. Because I've taught a graduate course in database, data mining, learning from data, data science, whatever you want to call it for over a decade; and one of my units in this semester-long course is on semantics and ontology. And frequently my students would look at me like, what does this have to do with what we're talking about? And of course at the end, I think we do understand that putting that data in some kind of a knowledge framework. So that, just for example, I'm looking for information about a particular customer behavior, understanding that that behavior occurs, that's what the people buy at a sporting event. What kind of products do I offer to my customers when I notice on their social media - on Twitter or Facebook - that they say they're going to a sporting event like football, baseball, hockey, World Cup, whatever it might be.

Okay, so sporting event. So they say they're going to, let's say, a baseball game. Okay, I understand that baseball is a sporting event. I understand that's usually a social and you go with people. I understand that it's usually in an outdoor space. I mean, understanding all those contextual features, it enables sort of, more powerful, sort of, segmentation of the customer involved and your sort of personalization of the experience that you're giving them when, for example, they're interacting with your space through a mobile app while they're sitting in a stadium.

So all that kind of stuff just brings so much more power and discovery potential to the data in that sort of indexing idea of indexing data granules by their semantic place and the knowledge space is really pretty significant. And I was really impressed that came out today. I think it's sort of a fundamental thing to talk.

Eric: Yeah, it sure is. It's very important in the discovery process, it's very important in the classification process. And if you think about it, Java works in classes. It's an object oriented, I guess, more or less, you could say form of programming and Java works in classes. So if you're actually designing software, this whole concept of trying to find new classes is actually pretty important stuff in terms of the functionality you're trying to deliver. Because especially in this new wild, wooly world of big data where you have so much Java out there running so many of these different applications, you know there are 87, 000 ways or more to get anything done with a computer, to get any kind of bit of functionality done.

One of my running jokes when people say, "Oh, you can build a data warehouse using NoSQL." I'm like, "well, you could, yeah, that's true. You could also build a data warehouse using Microsoft Word." It's not the best idea, it's not going to perform very well but you can actually do it. So the key is you have to find the best way to do something.

Adelante.

Kirk: Let me just respond to that. It's interesting you mentioned the Java class example which didn't come into my mind until you said it. One of the aspects of Java and classes and that sort of object orientation is that there are methods that bind to specific classes. And this is really the sort of a message that I was trying to send in my presentation and that once you understand some of these data granules - these knowledge nuggets, these tags, these annotations and these semantic labels - then you can bind a method to that. They basically have this reaction or this response and have your system provide this sort of automated, proactive response to this thing the next time that we see it in the data stream.

So that concept of binding actions and methods to specific class is really one of the powers of automated real-time analytics. And I think that you sort of hit on something.

Eric: Good, good, good. Well, this is good stuff. So let's see, Will, I want to hand it back to you and actually throw a question to you from the audience. We got a few of those in here too. And folks, we're going long because we want to get some of these great concepts in these good questions.

So let me throw a question over to you from one of the audience numbers who's saying, "I'm not really seeing how business intelligence is distinguishing cause and effect." In other words, as the systems are making decisions based on observable information, how do they develop new models to learn more about the world? It's an interesting point so I'm hearing a cause-and-effect correlation here, root cause analysis, and that's some of that sort of higher-end stuff in the analytics that you guys talk about as opposed to traditional BI, which is really just kind of reporting and kind of understanding what happened. And of course, your whole direction, just looking at your slide here, is moving toward that predictive capability toward making those decisions or at least making those recommendations, right? So the idea is that you guys are trying to service the whole range of what's going on and you're understanding that the key, the real magic, is in the analytical goal component there on the right.

Will: Absolutely. I think that question is somewhat peering into the future, in the sense that data science, as I mentioned before, we saw the slide with the requirements of the data scientist; it's a pretty challenging role for someone to be in. They have to have that rich knowledge of statistics and science. You need to have the domain knowledge to apply your mathematical knowledge to the domains. So what we're seeing today is there aren't these out-of-the-box predictive tools that a business user, like, could pull up in Excel and automatically predict their future, right?

It does require that advanced knowledge in technology at this stage. Now someday in the future, it may be that some of these systems, these scale-out systems become sentient and start doing some wild stuff. But I would say at this stage, you still have to have a data scientist in the middle to continue to build models, not these models. These predictive models around data mining and such are highly tuned in and built by the data scientist. They're not generated on their own, if you know what I mean.

Eric: Yeah, exactly. That's exactly right. And one of my lines is "Machines don't lie, at least not yet."

Will: Not yet, exactly.

Eric: I did read an article - I have to write something about this - about some experiment that was done at a university where they said that these computer programs learned to lie, but I got to tell you, I don't really believe it. We'll do some research on that, folks.

And for the last comment, so Robin I'll bring you back in to take a look at this WebAction platform, because this is very interesting. This is what I love about a whole space is that you get such different perspectives and different angles taken by the various vendors to serve very specific needs. And I love this format for our show because we got four really interesting vendors that are, frankly, not really stepping on each others' toes at all. Because we're all doing different bits and pieces of the same overall need which is to use analytics, to get stuff done.

But I just want to get your perspective on this specific platform and their architecture. How they're going about doing things. I find it pretty compelling. ¿Qué piensas?

Robin: Well, I mean, it's pointed at extremely fast results from streaming data and as search, you have to architect for that. I mean, you're not going to get away with doing anything, amateurish, as we got any of that stuff. I hear this is extremely interesting and I think that one of the things that we witnessed over the past; I mean I think you and I, our jaw has been dropping more and more over the past couple of years as we saw more and more stuff emerge that was just like extraordinarily fast, extraordinarily smart and pretty much unprecedented.

This is obviously, WebAction, this isn't its first rodeo, so to speak. It's actually it's been out there taking names to a certain extent. So I don't see but supposed we should be surprised that the architecture is fairly switched but it surely is.

Eric: Well, I'll tell you what, folks. We burned through a solid 82 minutes here. I mean, thank you to all those folks who have been listening the whole time. If you have any questions that were not answered, don't be shy, send an email to yours truly. We should have an email from me lying around somewhere. And a big, big thank you to both our presenters today, to Dr. Kirk Borne and to Dr. Robin Bloor.

Kirk, I'd like to further explore some of that semantic stuff with you, perhaps in a future webcast. Because I do think that we're at the beginning of a very new and interesting stage now. What we're going to be able to leverage a lot of the ideas that the people have and make them happen much more easily because, guess what, the software is getting less expensive, I should say. It's getting more usable and we're just getting all this data from all these different sources. And I think it's going to be a very interesting and fascinating journey over the next few years as we really dig into what this stuff can do and how can it improve our businesses.

So big thank you to Techopedia as well and, of course, to our sponsors - Pentaho, WebAction, MarkLogic and Treasure Data. And folks, wow, with that we're going to conclude, but thank you so much for your time and attention. We'll catch you in about a month and a half for the next show. And of course, the briefing room keeps on going; radio keeps on going; all our other webcast series keep on rocking and rolling, folks. Muchas gracias. We'll catch you next time. Adiós.