"Technology Trigger" — The first phase of a hype cycle is the "technology trigger" or breakthrough, product launch or other event that generates significant press and interest."Peak of Inflated Expectations" — In the next phase, a frenzy of publicity typically generates over-enthusiasm and unrealistic expectations. There may be some successful applications of a technology, but there are typically more failures."Trough of Disillusionment" — Technologies enter the "trough of disillusionment" because they fail to meet expectations and quickly become unfashionable. Consequently, the press usually abandons the topic and the technology."Slope of Enlightenment" — Although the press may have stopped covering the technology, some businesses continue through the "slope of enlightenment" and experiment to understand the benefits and practical application of the technology."Plateau of Productivity" — A technology reaches the "plateau of productivity" as the benefits of it become widely demonstrated and accepted. The technology becomes increasingly stable and evolves in second and third generations. The final height of the plateau varies according to whether the technology is broadly applicable or benefits only a niche market.
http://www.gartner.com/it/page.jsp?id=1763814
* http://www.foxnews.com/scitech/2011/01/04/years-later-does-moores-law-hold-true/** http://ns1758.ca/winch/winchest.htmlhttp://aws.amazon.com/s3/Moore's law is a rule of thumb in the history of computing hardware whereby the number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years. The period often quoted as "18 months" is due to Intel executive David House, who predicted that period for a doubling in chip performance (being a combination of the effect of more transistors and their being faster)
Traffico dati: in cinque anni aumenterà di 15 volte (REPORT ERICSSON)Traffic and Market Report Ericsson: l'85% della popolazione mondiale avrà una connessione 3G entro 5 anni. In Europa il 25% degli abbonamenti saranno LteFra cinque anni le sottoscrizioni a smartphone raggiungeranno quota 3 miliardi, mentre gli abbonamenti alla telefonia mobile hanno raggiunto i 6,2 miliardi nel primo trimestre 2012 con 170 milioni di nuovi abbonamenti nei primi tre mesi di quest’anno. Il traffico dati a livello mondiale crescerà di 15 volte entro il 2017.http://www.corrierecomunicazioni.it/upload/images/06_2012/120605115253.pdf
http://www.internetworldstats.com/top20.htmhttp://www.focus.com/fyi/10-largest-databases-in-the-world/http://en.wikipedia.org/wiki/List_of_countries_by_number_of_mobile_phones_in_use1 Petabyte = 1015 bytes = 1 million GB = 1.000 TB1 Exabyte = 1018bytes = 1.000.000.000 GB = 1.000.000 TBThe world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007 and it is predicted that the amount of traffic flowing over the internet will reach 667 exabytes annually by 2013
http://en.wikipedia.org/wiki/Big_data
Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data - the equivalent of 167 times the information contained in all the books in the US Library of Congress.Facebook handles 40 billion photos from its user base.FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide. [20]The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates. [21]Decoding the human genome originally took 10 years to process; now it can be achieved in one week.[18]
Big Data Analytics History: In the 1980’s, the CPG / Retail industry transition from bi-monthly audit data to scanner data changed the dynamics of the industry. RFID is transforming the supply chain. Combining data from a diverse set of data resources— adding customer sentiment data, geolocation data, customer preference analysis, and market trending information based on contextual text analytics, all add up to provide a level of sophistication previously unavailable to the industry.Predictive analytics is well understood by the retail industry. Retailers want to predict factors that might be important for a buyer to make a purchasing decision before that product ever was stocked on shelves. What if retailers could know exactly what market dynamics were modifying demand curves before they occurred? Imagine the impact on operational efficiencies in terms of inventory cost control, intelligent distribution and routing, and demand projection. The retail Use Cases are quite varied.
Telecom Use Case: A large telecommunications provider analyzed call logs and complex data from multiple sources. It used sophisticated predictive models across that data to predict the likelihood that any particular customer would leave. The result: Analytics helped the telecommunications company build more insightful customer relationships and reduce churn.
Interesting Use Case – Amazon Will Pay Shoppers $5 to Walk Out of Stores Empty-HandedInteresting use of consumer data entry to power next generation retail price competition…. Amazon is offering consumers up to $5 off on purchases if they compare prices using their mobile phone application in a store. The promotion will serve as a way for Amazon to increase usage of its bar-code-scanning application, while also collecting intelligence on prices in the stores.Amazon’s Price Check app, which is available for iPhone and Android, allows shoppers to scan a bar code, take a picture of an item or conduct a text search to find the lowest prices. Amazon is also asking consumers to submit the prices of items with the app, so Amazon knows if it is still offering the best prices. A great way to feed data into its learning engine from brick-and-mortar retailers.This is an interesting trend that should terrify brick-and-mortar retailers. While the real-time “Everyday Low Price” information empowers consumers, it terrifies retailers, who increasingly are feeling like showrooms — shoppers come to to check out the merchandise but ultimately decide to walk out and buy online instead
Risk Modeling Use Case — A large financial institution took separate data warehouses from multiple departments and combined them into a single global repository in Hadoop for analysis. The bank used the Hadoop cluster to construct a new and more accurate score of the risk in its customer portfolios. The more accurate score allowed the bank to manage its exposure better and to offer each customer better products and advice.Trade Surveillance Use Case — A large investment bank combines data about the parties that participate in a trade with the complex data that describes relationships among those parties and how they interact with one another. The combination allows the bank to recognize unusual trading activity and to flag it for human review.
Clickstream Use Case – Big Box Retailer need to analyze their clickstream – §3.5 billion records; 71 million unique cookies; 1.7 million targeted ads required per day. The problem: how to improve Return on Ad Spend (ROAS). Also how to speed up the analytics so consumers get more relevant ads quicker, which is especially important during holiday seasons!Suggestion Use Case. Yelp is growing rapidly and with more than 50 million of monthly visitors and 18 million or reviews the company generates about 400GB of data a day. That data needs to be processed and analyzed. A simple use case is Spelling Suggestions. By looking at millions of misspelled words Yelp uses an algorithm to create suggestions for common misspellings. By looking at typical queries, yelp can list common suggestions for a query even before you finish typing. This is possible because Yelp analyses all the web logs from their websites
Threat detection Use Case : A federal law enforcement agency has a challenge: how does it monitor threat (or criminal) behaviors and communications in order to raise awareness of interdiction opportunities while also exposing non-obvious relationships between terrorist actors/agents? Using massive amounts of data from numerous data sources, how could the agency proactively engage and disrupt planned criminal activities? And how can the agency execute on these needs in a cost effective and efficient manner—exposing information to partner organizations as appropriate.
Utilities run big, expensive and complicated systems to generate power. Each grid now includes sophisticated sensors that monitor voltage, current, frequency and other important operating characteristics. Efficiency now means paying careful attention to all of the data streaming off of the sensors.Smart meters – The rollout of smart meters as part of the Smart Grid adoption by utilities everywhere has resulted in a deluge of data flowing at unprecedented levels. Most utilities are ill-prepared to analyze the data once the meters are turned on.
Online Dating Use Case: A leading online dating service uses sophisticated analyses to measure the compatibility between individual members, so that it can suggest good matches for a potential relationship.Big Data Analytics helped customers find romance. The algorithms that power Match.com are not very different than those that are behind LinkedIn.Social Gaming: Zynga in their S-1 filing claimed that “they process and serve more than a petabyte of content for players every day, a volume of data that is unmatched in the social game industry. We continually analyze game data to optimize our games. We believe that combining data analytics with creative game design enables us to create a superior player experience.”
