Big Data Overview/Teaser (6 Aug 2013)

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On Tuesday, 6 Aug 2013 the Canadian Chamber of Commerce hosted me for a very brief talk on Big Data. In the 20 minutes I stretched to 25, I shared a few interesting stories about big data, considered privacy implications, focused on opportunities in Hong Kong, and ruminated on the future. This is the deck I used.

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  • 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)
  • Big Data Overview/Teaser (6 Aug 2013)

    1. 1. definitions technologies timing and hype people Hong Kong future
    2. 2. the common definition velocity volume variety veracity
    3. 3. the subjective definition
    4. 4. the easy definition
    5. 5. 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
    6. 6. 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
    7. 7. applications
    8. 8. why now? • cheap storage • unbounded compute • data accessibility and world datafication • internet scale: Yahoo! and Google – offspring of Hadoop
    9. 9. visibility/expectations time trigger inflated expectations disillusionment enlightenment productivity adapted from Gartner hype cycle
    10. 10. 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)
    11. 11. visibility/expectations time trigger inflated expectations disillusionment enlightenment productivity adapted from Gartner hype cycle big data: where are we today?
    12. 12. big data and you
    13. 13. 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?
    14. 14. Hong Kong
    15. 15. 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?
    16. 16. 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.
    17. 17. want more? Google+: Hong Kong Big Data http://www.infoincog.com/ scott@infoincog.com all content by Scott Brady Drummonds – scott@infoincog.com

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