definitions technologies timing and hype people hong kong future
TRANSCRIPT
components
hardware software
people
servers
storage
networks
appliances
platforms
traditional DBMS
columnar DBs
NoSQL
Hadoop
platform architects
stats. people
comp. scientists
traditional IT
visualization
hot components
hardware software
people
servers
storage
networks
appliances
platforms
traditional DBMS
columnar DBs
NoSQL
Hadoop
platform architects
stats. people
comp. scientists
traditional IT
visualization
why now?
• cheap storage• unbounded compute• data accessibility and world datafication• internet scale: Yahoo! and Google– offspring of Hadoop
visi
bilit
y/ex
pect
ation
s
time
trigger inflated expectations
disillusionment
enlightenment
productivity
adapted from Gartner hype cycle
visi
bilit
y/ex
pect
ation
s
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)
visi
bilit
y/ex
pect
ation
s
time
trigger inflated expectations
disillusionment
enlightenment
productivity
adapted from Gartner hype cyclebig data: where are we
today?
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?
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?
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.
want more?
Google+: Hong Kong Big Datahttp://www.infoincog.com/
all content by Scott Brady Drummonds – [email protected]