big data overview/teaser (6 aug 2013)
DESCRIPTION
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.TRANSCRIPT
definitions
technologies
timing and hype
peopleHong Kong
future
the common definition
velocityvolume
varietyveracity
the subjective definition
the easy definition
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
applications
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?
big data and you
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?
Hong Kong
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]