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Óscar Méndez - Big data: de la investigación científica a la gestión empresarial
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Óscar Méndez - Big data: de la investigación científica a la gestión empresarial

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El 3 de julio de 2014, organizamos en la Fundación Ramón Areces una jornada con el lema 'Big Data: de la investigación científica a la gestión empresarial'. En ella estudiamos los retos y ...

El 3 de julio de 2014, organizamos en la Fundación Ramón Areces una jornada con el lema 'Big Data: de la investigación científica a la gestión empresarial'. En ella estudiamos los retos y oportunidades del Big data en las ciencias sociales, en la economía y en la gestión empresarial. Entre otros ponentes, acudieron expertos de la London School of Economics, BBVA, Deloite, Universidades de Valencia y Oviedo, el Centro Nacional de Supercomputación...

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  • Hilo de la presentación: <br /> TESIS <br /> ---------- <br /> Aparación de Big Data 2.0 (cambio ed paradigma Big Query) <br /> Requerimientos: 100X <br /> Necesidad de arquitectura NO-HADOOP para conseguir estos requerimientos <br /> OPORTUNIDAD <br /> ------------------------ <br /> Dado que es la única plataforma NO-HADOOP open source, si la tesis es correcta será: <br /> The Open Source Big Data 2.0 Platform <br />
  • There is not any longer one Internet, there are thousands of Internets, one for every user, because Internet is filtered and adapted to every user… <br />
  • Ellos estan en esto, precisamente, en iniciar, ser proactivos adelantarse a lo que quieren o van a querer los usuarios. <br /> En algunos casos prediciendo lo que van a querer <br /> En otros casos creando productos que el usuario va a querer <br /> Ambas vertientes hay que trabajarlas: <br /> Tienes que predecir, y ser mas inteligente <br /> Tienes que crear o complementar tus productos con lo que van a querer los usuarios. Integrarlos con el mundo tecnologico digital en el que vivimos. <br />
  • Desarrollos realizados: <br /> NH HOTEL <br /> Proyecto con bases de datos noSQL: Indexadas Lucene, Solr, scrapping, tecnologías semánticas de medición de sentimiento e identificación de identidades <br /> Tags: Crawling / Categorización y Agregación de opiones. <br /> Project: Medición de la reputación de un hotel <br /> Target: Rastrear los sitios más importantes en los que se encuentra información sobre los hoteles. De ellos se extraen las valoraciones de los usuarios, haciendo una clasificación de ellos, para presentarlos en un portal que permite a los administradores de los hoteles ver rápidamente cómo sus hoteles se relacionan con los de la competencia y tambien con los datos recopilados en las encuestas a los clientes. Si hay disparidad en las encuestas de clientes se detecta y analiza si hay “apaño” por parte de la dirección del hotel o se estudia las razones. Tambien está integrado con el SAP contable para corraborar que el aumento de las valoraciones va ligado al aumento de ingresos. <br />
  • http://www.youtube.com/watch?v=6gmP4nk0EOE <br />
  • Cada vez hay mas gap con las empresas que lo estan haciendo bien, tienes que recortar terreno sobre ellas, y ganarlo sobre las tradicionales <br /> Tu competencia futura seran empresas que no esperas…. Libros, retail, viajes, banca (paypal), telefonia… <br /> Bill Gates: Deberian empezar a caerte bien los freakies, porque seguramente en el futuro trabajaras para uno de ellos…. <br />
  • http://www.youtube.com/watch?v=6gmP4nk0EOE <br />
  • Es vital reinventarse crear nuevos productos disruptivos, innovadores...no puedes ser indolente con tus productos, y acomodarte en ellos, tienes que innovar, sacar nuevos productos, convertirlos en tu purple cow, y obtener beneficios de ellos para crear un ambiente propicio para crear nuevos purple cows. Eso es lo que se ha hecho con IRO, y ahora toca obtener de este purple cow todo el beneficio que se pueda y ocupar el mayor espacio posible. <br />

Óscar Méndez - Big data: de la investigación científica a la gestión empresarial Óscar Méndez - Big data: de la investigación científica a la gestión empresarial Presentation Transcript

  • Los datos, la nueva materia prima del marketing
  • Too many Vs for Big Data Batch of new technologies that allow us to extract value out of a dataset which, due to it’s volume, variety or velocity, was not previously exploited
  • “Set of new technologies, able to extract additional value of all the available data of a company”
  • Petabytes: Google 300 PB, facebook: 45 PB, Yahoo! 180 PB Exabytes: U.S. healthcare Zetabytes: 2011, 1.8 ZB created. World Information 9.57 ZB YottaByte, Brontobyte, GeopByte to be reached I don’t have so much data… A big European company = Terabytes
  • Why does it apply to marketing?
  • Marketing has all that data…. and more M2M not a trend, your future
  • MARKET TRENDS
  • DATA = VALUE DATA = COMPANY VALUE
  • Startups Gran parte del valor de las empresas se mide por sus datos
  • Grandes empresas 467.142 Mill $ 373.608 Mill $ 150.211 Mill $ 148.210 Mill $ 1 3 20 21 Data Loyalty
  • DowJones PER2014 = 13 Facebook PER2014 = 48 Diferencia 35
  • PER = PER + PDR+ PER = 13 + 35 = 48 +
  • UNIVERSAL DATA VALUE BUSINESS INTELLIGENCE DATA DRIVEN DECISIONS BIG DATA INTERACTIVE BIG DATA REAL TIME DATA STREAMING BIG DATA INTELLIGENCE 10 10 20 20 20 BIG DATA STORED 20 100 UDV= = = 0,35 PDR 100 35 100 10 10 20 20 20 20 35 PDR
  • UDV DE TU EMPRESA BUSINESS INTELLIGENCE DATA DRIVEN DECISIONS BIG DATA INTERACTIVE BIG DATA REAL TIME DATA STREAMING BIG DATA INTELLIGENCE 5 5 0 0 0 BIG DATA STORED 0 10 PDR (tú) = UDV 10 = 0,35 10 = 3,5 10 10 20 20 20 20 35 3,5(tú) PDR X X
  • Opportunities/possibilities Threats/risk for marketing
  • The Bubble filter
  • You must enter in the user bubble
  • 83% of the surveyed companies were able to do things with Big Data that seemed impossible to achieve before “The art of possible” “Impossible is not a fact, it’s an opinion”
  • Visualizations And Analysis
  • Social networks tracking (Tag Clouds)
  • Social networks tracking and flow of data
  • Social networks tracking and geolocation
  • Marketing with Sentiment analysis and semantic engines
  • Description: Search the social network comments and mentions of interest of a particular issue or event for further evaluation, influencers detection and graphical display of the conversation to facilitate analysis. Advantages: Show real-time event (symposium, forum, seminar, etc..) with visual information.  Get opinions and feelings about a topic in social networks in real time Identify the influencers of a hot topic  Risk detection and prevention  Emotional mining: Know the term that is most popular for some people, brand, event, etc.and this way you can know about the generated feelings by the most important terms. Social networks tracking Application
  • Description: Search the network content and publications on specific subjects of our interest, to detect, filter, collect and process relevant information in semi- real time or batch. Associated with the semantic analysis this allows the detection and classification of the contents effectively. Advantages: Allows the generating of sites in a dynamic way without any intervention or exhaustive searches, with the contents collected and categorized. Unifies in a single web all the tasks that users have to do manually, so it saves them money and generates loyalty. Web Content Crawling and Scraping
  • EXAMPLES
  • +160% clicks vs. one size fits all +79% clicks vs. randomly selected +43% clicks vs. editor selected Recommended links News Interests Top Searches Description: Customizing homepages based on user navigation Analysis and customization of the homepage and site in real time for each user based on their browsing Modification of contents, highlights, ads, in real time based on user history Advantages: Over 300% increase in clickthrough Creating millions of web pages in real time Increasing Conversions Increase in sales Cost ten times lower than other solutions Marketing online: Customizing Web Sites (Behavioral Customization)
  • Description: Newsletter development, email-marketing or any other sent material segmented by individual preferences Analyzes and takes into account: •Financial information and user data •Navigation and usage information from previous marketing shipments •Mobile app data (GPS, payments, browsing of offers…) • Users’ information from the social networks Advantages: Increased clickthrough Increase in conversions and sales Natural language processing – semantics and sentiments Combines private and public data Marketing offline: Personalized Marketing with Big Data
  • NH Quality Focus: Complementing the internal data of a company by combining the structured and the unstructured data, with the data generated by the web and social networks, allows us to determine the validity of the data of our brand, product or company. The comparison and analysis of internal and external data (web) increases the value of our data and allows us to gain a competitive advantage over our competitors. Advantages:  It allows sales improvement. Improves loyalty. Increases Conversions. Detects errors or data manipulation.  SEO improvement with regards to the users and the public data. Improves marketing and product boosting with regards to trends. Marketing through private structured data with unstructured public data
  • Description: Allows you to label and categorize automatically and massively, any type of content or information. Advantages: Allows searching, categorization, clustering, and be able to extract value out of information otherwise hardly findable and usable. Utilizes state of the art tools to identify entities, NED systems, NERD. These tools combined with the use of disambiguation of entities using a Big Data system containing the Wikipedia and other sources of information. Speed processing capabilities and data volume​​ superior to that of other systems. Massive information tagging
  • TECHNOLOGY AND THE FUTURE OF BIG DATA
  • COMBINATION AND SPEED COUCHDBCOUCHDB Combine all type of data and past, present and future “Cross Data Spark” main mission is: •To facilitate the use of data stored in different noSQL databases and data containers •To allow combining stored data (past), real-time data (present), and future data (predictive).
  • MACHINE LEARNING AND ALGORITHMS USING ONLY SPARK FOR ALL PROCESSING: BATCH, INTERACTIVE AND STREAMING CROSSDATA SPARK: Stratio is able to combine, in one query, stored data with streaming data entering in the system Polyglots: Spark integrated with the main noSQL databases, starting with Cassandra & Mongo DB.
  • Lean = Easier deployment, management, and use of the system Former Hadoop or Hybrid Hadoop-Spark Platforms Stratio Platform SIMPLE AND EASY
  • SIMPLIFICATION Simplify Building Process
  • No te puedes quedar mirando
  • Arriesga, innova, reinventate
  • Hazlo ahora, si no puede ser tarde
  • No hay nada mas arriesgado que no arriesgarse
  • Enjoy with “Big Data”
  • Q&A
  • “the best way to predict the future is to create it” THANKS Óscar Méndez, CEO de Stratio,