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definitions

technologies. definitions. timing and hype. future. Hong Kong. people. the common definition. v. olume. v. elocity. v. ariety. v. eracity. the subjective definition. the easy definition. components. servers. traditional DBMS. visualization. storage. hardware. software.

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definitions

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  1. technologies definitions timing and hype future Hong Kong people

  2. the common definition v olume v elocity v ariety v eracity

  3. the subjective definition

  4. the easy definition

  5. components servers traditional DBMS visualization storage hardware software columnar DBs networks NoSQL platforms Hadoop appliances people traditional IT platform architects comp. scientists stats. people

  6. hot components servers traditional DBMS visualization storage hardware software columnar DBs networks NoSQL platforms Hadoop appliances people traditional IT platform architects comp. scientists stats. people

  7. applications

  8. why now? • cheap storage • unbounded compute • data accessibility and world datafication • internet scale: Yahoo! and Google • offspring of Hadoop

  9. adapted from Gartner hype cycle visibility/expectations time trigger inflated expectations disillusionment productivity enlightenment

  10. adapted from Gartner hype cycle example: tech stocks (1997-today) I am the smartest investor ever! (INTC, MSFT) some companies are keepers (AMZN, ORCL, AAPL) visibility/expectations some ideas are good (NFLX, GOOG) the internet sucks! (IPET, WBVN) online trading time trigger inflated expectations disillusionment productivity enlightenment

  11. adapted from Gartner hype cycle big data: where are we today? visibility/expectations time trigger inflated expectations disillusionment productivity enlightenment

  12. big data and you

  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. Hong Kong

  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. 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. 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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