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Big Data Overview/Teaser (6 Aug 2013)
- 7. hot components hardware software people servers storage network s appliances platforms traditional DBMS columnar DBs NoSQL Hadoop platform architects stats. people comp. scientists traditional IT visualization
- 8. applications
- 9. why now? • cheap storage • unbounded compute • data accessibility and world datafication • internet scale: Yahoo! and Google – offspring of Hadoop
- 10. visibility/expectations time trigger inflated expectations disillusionment enlightenment productivity adapted from Gartner hype cycle
- 11. visibility/expectations time trigger inflated expectations disillusionment enlightenment productivity adapted from Gartner hype cycle online trading I am the smartest investor ever! (INTC, MSFT) the internet sucks! (IPET, WBVN) some ideas are good (NFLX, GOOG) some companies are keepers (AMZN, ORCL, AAPL) example: tech stocks (1997-today)
- 12. visibility/expectations time trigger inflated expectations disillusionment enlightenment productivity adapted from Gartner hype cycle big data: where are we today?
- 13. big data and you
- 17. privacy • what is your expectation of your data’s lifespan? • what is the relationship between privacy and intellectual property protection? • do you know your digital exhaust? • should you be compensated for helping Google earn another billion dollars?
- 18. Hong Kong
- 22. want to get involved? • decision tree: – individual? • learn: join G+ group, ask Scott for reading recommendations • work: Scott knows some recruiters and hiring businesses • profit: let’s talk – government? • join and support ODHK • sponsor research in local schools – business? • wade into water, do not charge in – investor? • who has the data? • who has demonstrated an ability to monetize it?
- 23. changing future • borderless big data will increasingly become invasive. how will regional laws keep up? • “free” services will shift money from many small contributors to a few large businesses. • data must be properly valued which requires a market.
- 24. want more? Google+: Hong Kong Big Data http://www.infoincog.com/ scott@infoincog.com all content by Scott Brady Drummonds – scott@infoincog.com
Notas del editor
- 2009: http://www.nature.com/nature/journal/v457/n7232/full/nature07634.htmlCDC, 60 years old, dept. of health and human services
- Gartner, 2001, three v’s. veracity added by others later.
- “too big from which to derive value”
- 76% of analysts use MS Excel: http://www.billingviews.com/microsoft-excel-king-analytics-hill/
- http://www.intel.com/content/www/us/en/communications/internet-minute-infographic.html
- Retail:CRM – Customer Scoring,Store Siting and Layout,Fraud Detection / Prevention,Supply Chain OptimizationFinancial Services:Algorithmic Trading,Risk Analysis,Fraud Detection,Portfolio AnalysisManufacturing:Product Research,Engineering Analytics,Process & Quality Analysis,Distribution Optimization (GE and Wikibon think manufacturing/industrial growing 2x faster than any other segment: http://online.wsj.com/article/PR-CO-20130618-908554.html)Government:Market Governance,Counter-Terrorism,Econometrics,Health InformaticsEnergy:Smart Grid,ExplorationHealthcare & Life Sciences:Pharmaco-Genomics,Bio-Informatics,Pharmaceutical Research,Clinical Outcomes ResearchAdvertising & Public Relations:Demand Signaling,Ad Targeting,Sentiment Analysis,Customer AcquisitionMedia & Telecommunications:Network Optimization,Customer Scoring,Churn Prevention,Fraud Prevention
- Doug Cutting and Mike Cafarella in 2005
- http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=1&_r=1&hp
- http://www.gov.hk/en/theme/psi/datasets/questions:what is the relationship between traffic and air pollution? (data joining)how does property value lead/trail changes in population (historical analysis)what are the trends for weather-related closures (trending)
- http://www.gov.hk/en/theme/psi/datasets/questions:what is the relationship between traffic and air pollution? (data joining)how does property value lead/trail changes in population (historical analysis)what are the trends for weather-related closures (trending)