paradigma del big data - gianni hanawa

Paradigma del Big Data

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Paradigma del Big Data

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… y más formatos … y más equipos!… Luego más equipos …En el comienzo…

Como lo “vive” el usuario?

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1991

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20001993 1996 1999

20072003

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2007 – iPhone Announced 2007 – Silverlight introduced 2007 – Hulu 2007 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. Iphone Silverlight – Smooth Streaming Hulu 2008 – Roku Released And 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 date By 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.

Algunas estadísticas….

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§ 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)

Algo mas 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

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Cable/DTH

Contenido no es solo Video

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Contenido no es solo Video

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Muchas tecnologías

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Contenido ûûûû tech ûûûû plataforma

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Transmisión de Información

IPv6 vs IPv4

Ø Versión 4 (32 bits, 4 bloques de 8 bits c/u)4.68.80.110 (www.level3.com) 232 = 4,294’967,296

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Ø Versión 6 (128 bits, 8 bloques de 16 bits c/u)2001:0db8:85a3:08d3:1319:8a2e:0370:73342128 = 340 x 1036

Los Websitesdeben cargar en menos de 2 segundos para 2012

Cada vez más rápido

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Los 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)

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 seconds Web 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. Size 1995: 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 year Platforms Multitude of browsers and devices. Different technologies and standards

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

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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

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.

Cual es el reto de Big Data?

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It's about getting things down to one number. Using the stats the way we read them, we'll find value in players that no one else can see.People are overlooked for a variety of biased reasons and perceived flaws. Age, appearance, personality. Bill James and mathematics cut straight through that. Billy, of the 20,000 notable players for us to consider, I believe that there is a championship team of twenty-five people that we can afford, because everyone else in baseball undervalues them.

Libro de Michael Lewis del 2003 sobre la temporada 2002 de los A’s de Oakland y su General Manager Billy Beane.

Analizando con Big Data

Traigamos el ejemplo hacia esta parte del mundo:

A que jugador contrataría para obtener un campeonato en el torneo local?

EQUIPO CAMPEONAPERTURA 2002 IndependienteCLAUSURA 2003 River PlateAPERTURA 2003 Boca JuniorsCLAUSURA 2004 River PlateAPERTURA 2004 Newell's Old BoysCLAUSURA 2005 Velez SarsfieldAPERTURA 2005 Boca JuniorsCLAUSURA 2006 Boca Juniors

Estudiantes (LP)

GOLEADOR EQUIPOAndrés Silvera IndependienteLuciano Figueroa Rosario CentralErnesto Farías Estudiantes (LP)Rolando Zárate Velez SarsfieldLisandro López Racing ClubMariano Pavone Estudiantes (LP)Javier Cámpora Tiro FederalGonzalo Vargas Gimnasia (LP)Rodrigo Palacio Boca Juniors

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APERTURA 2006Estudiantes (LP)

CLAUSURA 2007 San LorenzoAPERTURA 2007 LanusCLAUSURA 2008 River PlateAPERTURA 2008 Boca JuniorsCLAUSURA 2009 Velez SarsfieldAPERTURA 2009 BanfieldCLAUSURA 2010 Argentinos Juniors

APERTURA 2010Estudiantes (LP)

CLAUSURA 2011 Velez SarsfieldAPERTURA 2011 Boca JuniorsCLAUSURA 2012 Arsenal

Rodrigo PalacioMauro Zárate

Boca JuniorsVelez Sarsfield

Martín Palermo Boca JuniorsGermán Denis IndependienteDario Cvitanich BanfieldJose Sand LanusJose Sand LanusSantiago Silva BanfieldMauro Boselli Estudiantes (LP)Denis StracqualursiSantiago Silva

TigreVelez Sarsfield

Javier CámporaTeófilo Gutierrez

HuracanRacing Club

Ruben Ramírez Godoy CruzCarlos Luna Tigre

Analizando con Big Data

Los seres humanos solemos estar polarizados a la hora de tomar decisiones…

Por ejemplo:

Ø Ataques mortales de tiburones(vs la probabilidad de morir por resbalarse en un piso mojado)

Ø Seguro de vida para personas que hacen paracaidismo, buceo o escalada en roca (vs los que corren, montan bicicleta o juegan futbol)

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Ø La gente está dispuesta a pagar mas por el helado de chocolate que el de vainilla (pocos centavos tal vez, pero en grandes volúmenes…)

DEBEMOS TENER LA (BIG) DATA PERO ESTABLECIENDO UN CLARO VÍNCULO ENTRE LO QUE

ANALIZAMOS DE ELLA Y NUESTRA ESTRATEGIA CORPORATIVA

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.

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Torturen a los datos y confesará el delito que quieran.- Ronald Coase, Economics, Nobel Prize Laureate

Los Datos maduran como el vino, las aplicaciones como el pescado.- James Governor

Si tenemos datos, veamos los datos. Si todo lo que tenemos son opiniones, pues vayamos adelante con la mia.

- Jim Barksdale, former Netscape CEO

Solo una advertencia final…

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paradigma del big data - gianni hanawa

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