3. gianni hanawa

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  • 2007 – iPhone Announced2007 – Silverlight introduced2007 – Hulu2007 was some year for consumers and truly the perfect storm of a strong economy high consumer spending and a revitalization of once burned DOT.COM professionals who saw the vision in the consumption of video everywhere.IphoneSilverlight – Smooth StreamingHulu2008 – Roku ReleasedAnd not so much about Roku but there is a lesson here. Consumer electronics don’t all come from Scientific Atlantic, Apple and Sony. They come from small agile companies who are flxible and speak to their customers need for their product.2011 – iPad 2 Launch dateBy now, if you aren’t getting your media to at least 5 different internet enabled devices in your out of touch, or in your 80’s, or in a developing country.
  • User Expectations In 2006, the average online shopper expected a web page to load in 4 seconds. Today, that same shopper expects your page to load in 2 seconds or less. Up to 40% of shoppers will abandon a site after waiting 3 seconds for a page to load.9 out of 10 people will not return to a site after a disappointing experience. Of these, 3 will go on to tell others about their experience.Almost 60% of mobile web users expect websites to perform as quickly on their handheld devices as they do on their home computers. About the same number of mobile users say they would be unlikely to visit a site again after a poor mobile web experience.The Average Page Load time for the Fortune 500 is 7 secondsWeb pages Website owners are sending out increasingly huge web pages through a pipeline whose ability has not grown in the same proportion. Content 1995: The average web page contained just 2.3 objects. This means just 2.3 calls to whatever data centers were serving the site.Today: The average web page contains a whopping 75.25 objects – everything from CSS to images to Javascript. This means 75.25 server round trips are needed to pull all the page’s resources to the user’s browser. The result: pages that load slowly and inconsistently. Size1995: The average page size was a lean 14.1k.Today: The average page size is a bloated 498k.The average page weight has increase 60% over the last yearPlatforms Multitude of browsers and devices. Different technologies and standards
  • What do these saved seconds mean to you? If your site were an ecommerce site, your site would see significant growth in these four metrics:SEO ranking - We found that after we accelerated a website, Googlebot was able to crawl about twice as many pages as it was able to at the outset, as we halved the amount of time Googlebot needed to download each page. As you probably already know, Google allocates either a set amount of time, or a set amount of data, for crawling each site. The more pages that Google can crawl within these limitations, the better a site’s ranking will be.Page views – AOL found that visitors in the top ten percentile of site speed viewed 50% more pages than visitors in the bottom ten percentile. On average, visitors to the optimized site viewed 9% more pages than visitors to the unoptimized site.Increased conversions - Shopzilla decreased its average page load time from 6 seconds to 1.2 seconds and experienced a 12% increase in revenue and a 25% increase in page views. It also doubled the number of sessions from search engine marketing and cut the number of required servers in half. Increased revenue – Microsoft’s Bing conducted a test wherein they slowed their site down by 2 seconds. Users made almost 2% fewer queries, clicked 3.75% less often, and reported being significantly less satisfied with their overall experience. Conversely, speeding up the site by 2 seconds resulted in a 5% revenue increase.If every major online retailer in the US were to implement this acceleration this year, it would generate an additional $25 billion in revenue.
  • Libro de Michael Lewis del 2003 sobre la temporada 2002 de los A’s de Oakland y su General Manager Billy Beane.
  • 3. gianni hanawa

    1. 1. Paradigma del Big Data Y donde está el usuario?
    2. 2. Paradigma del Big Data
    3. 3. Paradigma del Big Data
    4. 4. Como lo “vive” el usuario? En el comienzo… … Luego más equipos … … y más formatos … y más equipos! 1993 1995 1996 1999 2000 2001 2005 20111991 1992 1998 2003 2007 2008
    5. 5. Algunas estadísticas….• 4000 Millones de videos vistos por día (8 millones en 2005)• 600 Millones sobre plataformas móviles• 30 Millones de reproducciones en un día (Kony 2012)• 72 horas de video subidas por minuto (un año antes eran 48hs)
    6. 6. Algo más de estadísticas….• 2,200 Millones de usuarios de internet (508 Millones en América)• 2Mbps promedio de acceso a internet en banda ancha• Fuerte crecimiento de OTT e IPTV como medios alternativos al Cable/DTH
    7. 7. Contenido no es sólo video
    8. 8. Contenido no es sólo video
    9. 9. Muchas tecnologías
    10. 10. Contenido  tech  plataforma ver 3low ver 4 ver 3med ver 5 ver 4high ver 5 ver 8 ver 9 ver 16low ver 10med ver 17high ver 18 ver 3 ver 4low ver 5 ver 10med ver 11high uBuntu ver 12 Debian Fedoralow ver 7med ver 8 XPhigh ver 9 Vista Win 7
    11. 11. Transmisión de InformaciónIPv6 vs IPv4 Versión 4 (32 bits, 4 bloques de 8 bits c/u) 4.68.80.110 232 = 4,294’967,296 Versión 6 (128 bits, 8 bloques de 16 bits c/u) 2001:0db8:85a3:08d3:1319:8a2e:0370:7334 2128 = 340 x 1036
    12. 12. Cada vez más rápido Los Websites deben cargar en menos de 2 segundos para 2012Los usuarios esperan Websites cada vez más rápidos Las páginas Web son cada vez más complejas El impacto promedio de 1 segundo de retardo en el tiempo de respuesta de los sitios web (Aberdeen research 2009)
    13. 13. Qué dice el mercado 9% más de tráfico por cada 400 milisegundos de mejora 1% más ingresos por cada 100 milisegundos de mejora 12% de incremento en los ingresos y 25% de incremento en las visitas por cada 5 segundos de mejora 5% más ingresos por usuario por cada 2 segundos de mejora Ahora usa la velocidad del sitio para determinar la posición en el ranking
    14. 14. Cual es el reto de Big Data?Its about getting things down to one number. Using the stats theway we read them, well find value in players that no one elsecan see. People are overlooked for a variety of biased reasonsand perceived flaws. Age, appearance, personality. Bill James andmathematics cut straight through that. Billy, of the 20,000notable players for us to consider, I believe that there is achampionship team of twenty-five people that we can afford,because everyone else in baseball undervalues them.
    15. 15. Analizando con Big Data Apertura Universidad Católica Sebastián González (Colo-Colo; 18 goals) 2002 Clausura Colo-Colo Manuel Neira (Colo-Colo; 14 goals) Apertura Cobreloa Salvador Cabañas (Audax Italiano; 18 goals) 2003 Clausura Cobreloa Gustavo Biscayzacú (Unión Española; 21 goals)Traigamos el ejemplo hacia 2004 Apertura Universidad de Chile Patricio Galaz (Cobreloa; 23 goals)esta parte del mundo: Clausura Cobreloa Patricio Galaz (Cobreloa; 19 goals) Joel Estay (Everton; 13 goals) Apertura Unión Española Álvaro Sarabia (Puerto Montt; 13 goals)A que jugador contrataría 2005 Héctor Mancilla (Huachipato; 13 goals)para obtener un Cristián Montecinos (Concepción; 13 goals) Clausura Universidad Católica Gonzalo Fierro (Colo-Colo; 13 goals)campeonato en el torneo César Díaz (Cobresal; 13 goals)local? Apertura Colo-Colo Humberto Suazo (Colo-Colo; 19 goals) 2006 Clausura Colo-Colo Leonardo Monje (Universidad de Concepción; 17 goals) Apertura Colo-Colo Humberto Suazo (Colo-Colo; 18 goals) 2007 Clausura Colo-Colo Carlos Villanueva (Audax Italiano; 20 goals) Apertura Everton Lucas Barrios (Colo-Colo; 19 goals) 2008 Clausura Colo-Colo Lucas Barrios (Colo-Colo; 18 goals) Apertura Universidad de Chile Esteban Paredes (Santiago Morning; 17 goals) 2009 Clausura Colo-Colo Diego Rivarola (Santiago Morning; 13 goals) 2010 Universidad Católica Milovan Mirosevic (Universidad Católica; 19 goals) Apertura Universidad de Chile Matías Urbano (Unión San Felipe; 12 goals) 2011 Clausura Universidad de Chile Esteban Paredes (Colo-Colo; 14 goals) 2012 Apertura Universidad de Chile Enzo Gutiérrez (OHiggins; 11 goals)
    16. 16. Analizando con Big DataLos seres humanos solemos estar polarizados a la hora de tomar decisiones…Por ejemplo:Ataques mortales de tiburones (vs la probabilidad de morir por resbalarse en un pisomojado)Seguro de vida para personas que hacen paracaidismo, buceo o escalada en roca (vs losque corren, montan bicicleta o juegan futbol)La gente está dispuesta a pagar mas por el helado de chocolate que el de vainilla (pocoscentavos tal vez, pero en grandes volumenes…)DEBEMOS TENER LA (BIG) DATA PERO ESTABLECIENDO UN CLARO VÍNCULO ENTRE LO QUE ANALIZAMOS DE ELLA Y NUESTRA ESTRATEGIA CORPORATIVA
    17. 17. Algunos pensamientos finales…Es un error capital el teorizar sin tener antes los datos. - Sherlock Holmes, A Study in Scarlett (Arthur Conan Doyle)El commodity mas valioso que conozco es la información. - Gordon Gekko, Wall Street (1987)Torturen a los datos y confesará el delito que quieran. - Ronald Coase, Economics, Nobel Prize LaureateLos Datos maduran como el vino, las aplicaciones como el pescado. - James GovernorSi tenemos datos, veamos los datos. Si todo lo que tenemos son opiniones, puesvayamos adelante con la mia. - Jim Barksdale, former Netscape CEO

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